text:
-9290},
url = {https://doi.org/10.1109/TASLP.2016.2580298},
doi = {10.1109/TASLP.2016.2580298},
abstract = {This paper proposes a simple and efficient technique for variance compensation to improve the perceptual quality of synthetic speech in parametric speech synthesis. First, we analyze the problem of spectral and F0 enhancement with global variance GV in HMM-based speech synthesis. In the conventional GV-based parameter generation, the enhancement is achieved by taking account of a GV probability density function with fixed GV model parameters for every output utterance through the speech parameter generation process. We find that the use of fixed GV parameters results in much smaller variations of GVs in synthesized utterances than those in natural speech. In addition, the computational cost is high because of iterative optimization. This paper examines these issues in terms of multiple objective measures such as variance characteristics, GV distortions, and GV correlations. We propose a simple and fast compensation method based on a global affine transformation that provides a GV distribution closer to that of natural speech and improves the correlation of GVs between natural and generated parameter sequences. The experimental results demonstrate that the proposed variance compensation methods outperform the conventional GV-based parameter generation in terms of objective and subjective speech similarity to natural speech while maintaining speech naturalness.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1694–1704},
numpages = {11}
}

@article{10.1109/TASLP.2016.2585859,
author = {Bokaei, Mohammad Hadi and Sameti, Hossein and Liu, Yang and Bokaei, Mohammad Hadi and Sameti, Hossein and Yang Liu and Bokaei, Mohammad Hadi and Sameti, Hossein and Liu, Yang},
title = {Summarizing Meeting Transcripts Based on Functional Segmentation},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2585859},
doi = {10.1109/TASLP.2016.2585859},
abstract = {In this paper, we aim to improve meeting summarization performance using discourse specific information. Since there are intrinsically different characteristics in utterances in different types of function segments, e.g., Monologue segments versus Discussion ones, we propose a new summarization framework where different summarizers are used for different segment types. For monologue segments, we adopt the integer linear programming-based summarization method; whereas for discussion segments, we use a graph-based method to incorporate speaker information. Performance of our proposed method is evaluated using the standard AMI meeting corpus. Results show a good improvement over previous state-of-the-art algorithms according to various evaluation metrics and different compress ratios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1831–1841},
numpages = {11}
}

@article{10.1109/TASLP.2016.2580944,
author = {Ghaffarzadegan, Shabnam and Boril, Hynek and Hansen, John H. L. and Ghaffarzadegan, Shabnam and Boril, Hynek and Hansen, John H. L. and Ghaffarzadegan, Shabnam and Hansen, John H. L. and Boril, Hynek},
title = {Generative Modeling of Pseudo-Whisper for Robust Whispered Speech Recognition},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2580944},
doi = {10.1109/TASLP.2016.2580944},
abstract = {Whisper is a common means of communication used to avoid disturbing individuals or to exchange private information. As a vocal style, whisper would be an ideal candidate for human-handheld/computer interactions in open-office or public area scenarios. Unfortunately, current speech technology is predominantly focused on modal neutral speech and completely breaks down when exposed to whisper. One of the major barriers for successful whisper recognition engines is the lack of available large transcribed whispered speech corpora. This study introduces two strategies that require only a small amount of untranscribed whisper samples to produce excessive amounts of whisper-like pseudo-whisper utterances from easily accessible modal speech recordings. Once generated, the pseudo-whisper samples are used to adapt modal acoustic models of a speech recognizer toward whisper. The first strategy is based on Vector Taylor Series VTS where a whisper “background” model is first trained to capture a rough estimate of global whisper characteristics from a small amount of actual whisper data. Next, that background model is utilized in the VTS to establish specific broad phone classes' unvoiced/voiced phones transformations from each input modal utterance to its pseudo-whispered version. The second strategy generates pseudo-whisper samples by means of denoising autoencoders DAE. Two generative models are investigated-one produces pseudo-whisper cepstral features on a frame-by-frame basis, while the second generates pseudo-whisper statistics for whole phone segments. It is shown that word error rates of a TIMIT-trained speech recognizer are considerably reduced for a whisper recognition task with a constrained lexicon after adapting the acoustic model toward the VTS or DAE pseudo-whisper samples, compared to model adaptation on an available small whisper set.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1705–1720},
numpages = {16}
}

@article{10.1109/TASLP.2016.2588340,
author = {Ji, An and Johnson, Michael T. and Berry, Jeffrey J. and An Ji and Johnson, Michael T. and Berry, Jeffrey J. and Ji, An and Johnson, Michael T. and Berry, Jeffrey J.},
title = {Parallel Reference Speaker Weighting for Kinematic-Independent Acoustic-to-Articulatory Inversion},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2588340},
doi = {10.1109/TASLP.2016.2588340},
abstract = {Acoustic-to-articulatory inversion, the estimation of articulatory kinematics from an acoustic waveform, is a challenging but important problem. Accurate estimation of articulatory movements has the potential for significant impact on our understanding of speech production, on our capacity to assess and treat pathologies in a clinical setting, and on speech technologies such as computer aided pronunciation assessment and audio-video synthesis. However, because of the complex and speaker-specific relationship between articulation and acoustics, existing approaches for inversion do not generalize well across speakers. As acquiring speaker-specific kinematic data for training is not feasible in many practical applications, this remains an important and open problem. This paper proposes a novel approach to acoustic-to-articulatory inversion, Parallel Reference Speaker Weighting PRSW, which requires no kinematic data for the target speaker and a small amount of acoustic adaptation data. PRSW hypothesizes that acoustic and kinematic similarities are correlated and uses speaker-adapted articulatory models derived from acoustically derived weights. The system was assessed using a 20-speaker data set of synchronous acoustic and Electromagnetic Articulography EMA kinematic data. Results demonstrate that by restricting the reference group to a subset consisting of speakers with strong individual speaker-dependent inversion performance, the PRSW method is able to attain kinematic-independent acoustic-to-articulatory inversion performance nearly matching that of the speaker-dependent model, with an average correlation of 0.62 versus 0.63. This indicates that given a sufficiently complete and appropriately selected reference speaker set for adaptation, it is possible to create effective articulatory models without kinematic training data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1865–1875},
numpages = {11}
}

@article{10.1109/TASLP.2016.2586608,
author = {Zhang, Jiajun and Zhou, Yu and Zong, Chengqing and Jiajun Zhang and Yu Zhou and Chengqing Zong and Zhang, Jiajun and Zong, Chengqing and Zhou, Yu},
title = {Abstractive Cross-Language Summarization via Translation Model Enhanced Predicate Argument Structure Fusing},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2586608},
doi = {10.1109/TASLP.2016.2586608},
abstract = {Cross-language multidocument summarization is the task to generate a summary in a target language e.g., Chinese from a collection of documents in a different source language e.g., English. Previous methods such as the extractive and compressive algorithms focus only on single sentence selection and compression, which cannot make full use of the similar sentences containing complementary information. Furthermore, the translation model knowledge is not fully explored in previous approaches. To address these two problems, we propose in this paper an abstractive cross-language summarization framework. First, the source language documents are translated into target language with a machine translation system. Then, the method constructs a pool of bilingual concepts and facts represented by the bilingual elements of the source-side predicate-argument structures PAS and their target-side counterparts. Finally, new summary sentences are produced by fusing bilingual PAS elements with the integer linear programming algorithm to maximize both of the salience and translation quality of the PAS elements. The experimental results on English-to-Chinese cross-language summarization demonstrate that our proposed method outperforms the state-of-the-art extractive systems in both automatic and manual evaluations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1842–1853},
numpages = {12}
}

@article{10.1109/TASLP.2016.2583794,
author = {Adiloglu, Kamil and Vincent, Emmanuel and Adiloglu, Kamil and Vincent, Emmanuel and Vincent, Emmanuel and Adiloglu, Kamil},
title = {Variational Bayesian Inference for Source Separation and Robust Feature Extraction},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2583794},
doi = {10.1109/TASLP.2016.2583794},
abstract = {We consider the task of separating and classifying individual sound sources mixed together. The main challenge is to achieve robust classification despite residual distortion of the separated source signals. A promising paradigm is to estimate the uncertainty about the separated source signals and to propagate it through the subsequent feature extraction and classification stages. We argue that variational Bayesian VB inference offers a mathematically rigorous way of deriving uncertainty estimators, which contrasts with state-of-the-art estimators based on heuristics or on maximum likelihood ML estimation. We propose a general VB source separation algorithm, which makes it possible to jointly exploit spatial and spectral models of the sources. This algorithm achieves 6% and 5% relative error reduction compared to ML uncertainty estimation on the CHiME noise-robust speaker identification and speech recognition benchmarks, respectively, and it opens the way for more complex VB approximations of uncertainty.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1746–1758},
numpages = {13}
}

@article{10.1109/TASLP.2016.2580943,
author = {Mirsamadi, Seyedmahdad and Hansen, John H. L. and Mirsamadi, Seyedmahdad and Hansen, John H. L. and Hansen, John H. L. and Mirsamadi, Seyedmahdad},
title = {A Generalized Nonnegative Tensor Factorization Approach for Distant Speech Recognition With Distributed Microphones},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2580943},
doi = {10.1109/TASLP.2016.2580943},
abstract = {Automatic speech recognition ASR using distant far-field microphones is a challenging task, in which room reverberation is one of the primary causes of performance degradation. This study proposes a multichannel spectral enhancement method for reverberation-robust ASR using distributed microphones. The proposed method uses the techniques of nonnegative tensor factorization in order to identify the clean speech component from a set of observed reverberant spectrograms from the different channels. The general family of alpha-beta divergences is used for the tensor decomposition task which provides increased flexibility for the algorithm and is shown to provide improvements in highly reverberant scenarios. Unlike many conventional array processing solutions, the proposed method does not require closely-spaced microphones and is independent of source and microphone locations. The algorithm can automatically adapt to unbalanced direct-to-reverberation ratios among different channels, which is useful in blind scenarios in which no information is available about source-to-microphone distances. For a medium vocabulary distant ASR task based on TIMIT utterances, and using clean-trained deep neural network acoustic models, absolute WER improvements of +17.2%, +20.7%, and +23.2% are achieved in single-channel, two-channel, and four-channel scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1721–1731},
numpages = {11}
}

@article{10.1109/TASLP.2016.2587218,
author = {Lafay, Gregoire and Lagrange, Mathieu and Rossignol, Mathias and Benetos, Emmanouil and Roebel, Axel and Lafay, Gregoire and Lagrange, Mathieu and Rossignol, Mathias and Benetos, Emmanouil and Roebel, Axel and Roebel, Axel and Lafay, Gregoire and Lagrange, Mathieu and Rossignol, Mathias and Benetos, Emmanouil},
title = {A Morphological Model for Simulating Acoustic Scenes and Its Application to Sound Event Detection},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2587218},
doi = {10.1109/TASLP.2016.2587218},
abstract = {This paper introduces a model for simulating environmental acoustic scenes that abstracts temporal structures from audio recordings. This model allows us to explicitly control key morphological aspects of the acoustic scene and to isolate their impact on the performance of the system under evaluation. Thus, more information can be gained on the behavior of an evaluated system, providing guidance for further improvements. To demonstrate its potential, this model is employed to evaluate the performance of nine state of the art sound event detection systems submitted to the IEEE DCASE 2013 Challenge. Results indicate that the proposed scheme is able to successfully build datasets useful for evaluating important aspects of the performance of sound event detection systems, such as their robustness to new recording conditions and to varying levels of background audio.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1854–1864},
numpages = {11}
}

@article{10.1109/TASLP.2016.2585862,
author = {Epain, Nicolas and Jin, Craig T. and Epain, Nicolas and Jin, Craig T. and Epain, Nicolas and Jin, Craig T.},
title = {Spherical Harmonic Signal Covariance and Sound Field Diffuseness},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2585862},
doi = {10.1109/TASLP.2016.2585862},
abstract = {Characterizing sound field diffuseness has many practical applications, from room acoustics analysis to speech enhancement and sound field reproduction. In this paper, we investigate how spherical microphone arrays SMAs can be used to characterize diffuseness. Due to their specific geometry, SMAs are particularly well suited for analyzing the spatial properties of sound fields. In particular, the signals recorded by an SMA can be analyzed in the spherical harmonic SH domain, which has special and desirable mathematical properties when it comes to analyzing diffuse sound fields. We present a new measure of diffuseness, the COMEDIE diffuseness estimate, which is based on the analysis of the SH signal covariance matrix. This algorithm is suited for the estimation of diffuseness arising either from the presence of multiple sources distributed around the SMA or from the presence of a diffuse noise background. As well, we introduce the concept of a diffuseness profile, which consists in measuring the diffuseness for several SH orders simultaneously. Experimental results indicate that diffuseness profiles better describe the properties of the sound field than a single diffuseness measurement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1796–1807},
numpages = {12}
}

@article{10.1109/TASLP.2016.2583065,
author = {Fuster, Laura and de Diego, Maria and Azpicueta-Ruiz, Luis A. and Ferrer, Miguel and Fuster, Laura and de Diego, Maria and Azpicueta-Ruiz, Luis A. and Ferrer, Miguel and de Diego, Maria and Fuster, Laura and Azpicueta-Ruiz, Luis A. and Ferrer, Miguel},
title = {Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2583065},
doi = {10.1109/TASLP.2016.2583065},
abstract = {Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters. In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios SNRs or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1732–1745},
numpages = {14}
}

@article{10.1109/TASLP.2016.2573048,
author = {Wang, Lin and Reiss, Joshua D. and Cavallaro, Andrea},
title = {Over-Determined Source Separation and Localization Using Distributed Microphones},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2573048},
doi = {10.1109/TASLP.2016.2573048},
abstract = {We propose an overdetermined source separation and localization method for a set of M microphones distributed around an unknown number, N < M, of sources. We reformulate the overdetermined acoustic mixing procedure with a new determined mixing model and apply a determined M \texttimes{} M independent component analysis (ICA) in each frequency bin directly. The reformulated ICA operates without knowing N and also leads to better separation in reverberant scenarios. To solve the challenging permutation ambiguity problem, we first employ a time activity-based clustering approach to cluster the separated frequency components into M channels. We then propose a remixing procedure to detect and merge channels from the same source. The detection is done by analyzing time and frequency activities, spectral likeliness, and spatial location. To estimate the spatial location, we propose a time--frequency masking-based steered response power algorithm. Simulated and real-data experiments in a very challenging reverberant scenario confirm the effectiveness of the proposed method in obtaining the number of sources, the separated signals, and the location and spatial likelihood of each source.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1569–1584},
numpages = {16},
keywords = {source localization, blind source separation, over-determined mixture, permutation alignment}
}

@article{10.5555/2992829.2992835,
author = {Pan, Chao and Chen, Jingdong and Benesty, Jacob},
title = {Reduced-Order Robust Superdirective Beamforming with Uniform Linear Microphone Arrays},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
abstract = {Sensor arrays for audio and speech signal acquisition are generally required to have frequency-invariant beampatterns to avoid adding spectral distortion to the broadband signals of interest. One way to obtain frequency-invariant beampatterns is via superdirective beamforming. However, traditional superdirective beamformers may cause significant white noise amplification (particularly at low frequencies), making them sensitive to uncorrelated white noise. To circumvent the problem of white noise amplification, a method was developed to find the superdirective beamforming filter with a constraint on the white noise gain (WNG), leading to the so-called WNG-constrained superdirective beamformer. But this method damages the frequency invariance of the beampattern. In this paper, we develop a flatness-constrained robust superdirective beamformer. We divide the overall beamformer into two subbeamformers, which are convolved together: one subbeamformer forms a lower order superdirective beampattern while the other attempts to improve the WNG. We show that this robust approach can improve the WNG while limiting the frequency dependency of the beampattern at the same time.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1544–1555},
numpages = {12},
keywords = {white noise gain, microphone arrays, robust beamforming, cardioid, directivity factor, beampattern design, superdirective beamforming}
}

@article{10.1109/TASLP.2016.2560523,
author = {Delikaris-Manias, Symeon and Vilkamo, Juha and Pulkki, Ville},
title = {Signal-Dependent Spatial Filtering Based on Weighted-Orthogonal Beamformers in the Spherical Harmonic Domain},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2560523},
doi = {10.1109/TASLP.2016.2560523},
abstract = {Spatial filtering with microphone arrays is a technique that can be utilized to obtain the signal of a target sound source from a specific direction. Typical approaches in the field of audio underperform in practical environments with multiple sound sources and diffuse sound. In this contribution, we propose a post-filtering technique to suppress the effect of interferers and diffuse sound. The proposed technique utilizes the cross-spectral estimates of the output of two beamformers to formulate a time-frequency soft masker. The beamformers' outputs are used only for parameter estimation and not for generating an audio signal. Two sets of beamformer weights, a constant and an adaptive, are applied to the microphone array signals for the parameter estimation. The weights of the constant beamformer are designed such that they provide a spatially narrow beam pattern that is time and frequency invariant, having a unity gain toward the direction of interest. The weights of the adaptive beamformer are formulated using linearly constrained optimization with the constraint of weighted orthogonality with respect to the constant beamformer weights, as well as the unity gain toward the look direction. The orthogonality constraint provides diffuse sound suppression while the unity gain distortionless response. The cross spectrum of these two beamformers provides the target energy at a given look direction for the post filter. The study focuses on compact microphone arrays with which the typical beamforming techniques feature a tradeoff between noise amplification and spatial selectivity, especially in the low frequency region. The proposed method is evaluated with instrumental measures and listening tests under different reverberation times, in dual and multitalker scenarios. The evaluation shows that the proposed method provides a better performance when compared with a previous state-of-the-art spatial filter based on cross-pattern coherence, a linearly constrained beamformer and a Wiener postfilter.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1507–1519},
numpages = {13},
keywords = {post filtering, beamforming, microphone array signal processing, spherical harmonics, cross spectrum}
}

@article{10.1109/TASLP.2016.2577501,
author = {Karimian-Azari, Sam and Jensen, Jesper Rindom and Christensen, Mads Gr\ae{}b\o{}ll},
title = {Computationally Efficient and Noise Robust DOA and Pitch Estimation},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2577501},
doi = {10.1109/TASLP.2016.2577501},
abstract = {Many natural signals, such as voiced speech and some musical instruments, are approximately periodic over short intervals. These signals are often described in mathematics by the sum of sinusoids (harmonics) with frequencies that are proportional to the fundamental frequency, or pitch. In sensor (microphone) array signal processing, the periodic signals are estimated from spatiotemporal samples regarding to the direction of arrival (DOA) of the signal of interest. In this paper, we consider the problem of pitch and DOA estimation of quasi-periodic audio signals. In reallife scenarios, recorded signals are often contaminated by different types of noise, which challenges the assumption of white Gaussian noise in most state-of-the-art methods. We establish filtering methods based on noise statistics to apply to nonparametric spectral and spatial parameter estimates of the harmonics. We design minimum variance solutions with distortionless constraints to estimate the pitch from the frequency estimates, and to estimate the DOA from multichannel phase estimates of the harmonics. Applying this filtering method as the sum of weighted frequency and DOA estimates of the harmonics, we also design a joint DOA and pitch estimator. In white Gaussian noise, we derive even more computationally efficient solutions which are designed using the narrowband power spectrum of the harmonics. Numerical results reveal the performance of the estimators in colored noise compared with the Cram\'{e}r--Rao lower bound. Experiments on real-life signals indicate the applicability of the methods in practical low local signal-to-noise ratios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1609–1621},
numpages = {13},
keywords = {harmonic signal model, analytical signal, minimum variance distortionless response (MVDR), pitch estimation, direction of arrival (DOA) estimation}
}

@article{10.1109/TASLP.2016.2570945,
author = {FitzGerald, Derry and Liutkus, Antoine and Badeau, Roland},
title = {Projection-Based Demixing of Spatial Audio},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2570945},
doi = {10.1109/TASLP.2016.2570945},
abstract = {We propose a method to unmix multichannel audio signals into their different constitutive spatial objects. To achieve this, we characterize an audio object through both a spatial and a spectro-temporal modeling. The particularity of the spatial model we pick is that it neither assumes an object has only one underlying source point, nor does it attempt to model the complex room acoustics. Instead, it focuses on a listener perspective, and takes each object as the superposition of many contributions with different incoming directions and interchannel delays. Our spectro-temporal probabilistic model is based on the recently proposed α-harmonisable processes, which are adequate for signals with large dynamics, such as audio. Then, the main originality of this paper is to provide a new way to estimate and exploit interchannel dependences of an object for the purpose of demixing. In the Gaussian α = 2 case, previous research focused on covariance structures. This approach is no longer valid for α < 2 where covariances are not defined. Instead, we show how simple linear combinations of the mixture channels can be used to learn the model parameters, and the method we propose consists in pooling the estimates based on many projections to correctly account for the original multichannel audio. Intuitively, each such downmix of the mixture provides a new perspective where some objects are canceled or enhanced. Finally, we also explain how to recover the different spatial audio objects when all parameters have been computed. Performance of the method is illustrated on the separation of stereophonic music signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1556–1568},
numpages = {13},
keywords = {probabilistic models, source separation, musical source separation, nonnegative matrix factorization (NMF)}
}

@article{10.1109/TASLP.2016.2562505,
author = {Li, Sheng and Akita, Yuya and Kawahara, Tatsuya},
title = {Semi-Supervised Acoustic Model Training by Discriminative Data Selection from Multiple ASR Systems' Hypotheses},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2562505},
doi = {10.1109/TASLP.2016.2562505},
abstract = {While the performance of ASR systems depends on the size of the training data, it is very costly to prepare accurate and faithful transcripts. In this paper, we investigate a semisupervised training scheme, which takes the advantage of huge quantities of unlabeled video lecture archive, particularly for the deep neural network (DNN) acoustic model. In the proposed method, we obtain ASR hypotheses by complementary GMM- and DNN-based ASR systems. Then, a set of CRF-based classifiers is trained to select the correct hypotheses and verify the selected data. The proposed hypothesis combination shows higher quality compared with the conventional system combination method (ROVER). Moreover, compared with the conventional data selection based on confidence measure score, our method is demonstrated more effective for filtering usable data. Significant improvement in the ASR accuracy is achieved over the baseline system and in comparison with the models trained with the conventional system combination and data selection methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1520–1530},
numpages = {11},
keywords = {speech recognition, semisupervised training, acoustic model, lecture transcription}
}

@article{10.5555/2992829.2992841,
author = {Kitamura, Daichi and Ono, Nobutaka and Sawada, Hiroshi and Kameoka, Hirokazu and Saruwatari, Hiroshi},
title = {Determined Blind Source Separation Unifying Independent Vector Analysis and Nonnegative Matrix Factorization},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
abstract = {This paper addresses the determined blind source separation problem and proposes a new effective method unifying independent vector analysis (IVA) and nonnegative matrix factorization (NMF). IVA is a state-of-the-art technique that utilizes the statistical independence between sources in a mixture signal, and an efficient optimization scheme has been proposed for IVA. However, since the source model in IVA is based on a spherical multivariate distribution, IVA cannot utilize specific spectral structures such as the harmonic structures of pitched instrumental sounds. To solve this problem, we introduce NMF decomposition as the source model in IVA to capture the spectral structures. The formulation of the proposed method is derived from conventional multichannel NMF (MNMF), which reveals the relationship between MNMF and IVA. The proposed method can be optimized by the update rules of IVA and single-channel NMF. Experimental results show the efficacy of the proposed method compared with IVA and MNMF in terms of separation accuracy and convergence speed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1622–1637},
numpages = {16},
keywords = {determined, nonnegative matrix factorization, independent vector analysis, blind source separation}
}

@article{10.1109/TASLP.2016.2580302,
author = {Obin, Nicolas and Roebel, Axel},
title = {Similarity Search of Acted Voices for Automatic Voice Casting},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2580302},
doi = {10.1109/TASLP.2016.2580302},
abstract = {This paper presents a large-scale similarity search of professionally acted voices for computer-aided voice casting. The proposed voice casting system explores Gaussian mixture model-based acoustic models and multilabel recognition of perceived paralinguistic content (speaker states and speaker traits, e.g., age/gender, voice quality, emotion) for the voice casting of professionally acted voices. First, acoustic models (universal background model, super-vector, i-vector) are constructed to model the acoustic space of voices, from which the similarity between voices can be measured directly in the acoustic space. Second, multiple binary classification of speaker traits and states is added to the acoustic models in order to represent the vocal signature of a voice, which is then used to measure the similarity between voices in the paralinguistic space. Finally, a similarity search is processed in order to determine the set of target actors that are the most similar to the voice of a source actor. In a subjective experiment conducted in the real-context of cross-language voice casting, the multilabel scoring system significantly outperforms the acoustic scoring system. This constitutes a proof of concept for the role of perceived para-linguistic categories in the perception of voice similarity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1638–1647},
numpages = {10},
keywords = {speaker recognition, para-linguistics, multi-label classification, voice similarity, speaker traits and states, voice casting}
}

@article{10.1109/TASLP.2016.2573050,
author = {Liu, Yang and Li, Sujian and Wei, Furu and Ji, Heng},
title = {Relation Classification via Modeling Augmented Dependency Paths},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2573050},
doi = {10.1109/TASLP.2016.2573050},
abstract = {Previous research on relation classification has verified the effectiveness of using dependency shortest paths or dependency subtrees. How to efficiently unify these two kinds of dependency information in relation classification is still an open problem. In this paper, we propose a novel structure, termed augmented dependency path (ADP), which is composed of the shortest dependency path between two entities and the subtrees attached to the shortest path. To exploit the semantic representation behind the ADP structure, we develop the dependency-based neural networks (DepNN) model which combines the advantages of the recursive neural network (RNN) and the convolutional neural network (CNN). In DepNN, RNN is designed to model the dependency subtrees since it is good at capturing the hierarchical structures. Then, the semantic representation in subtrees is passed to the nodes on the shortest path and CNN is used to get the most important features on the ADP. Experiments on the SemEval-2010 dataset show that the ADP structure including both the shortest dependency path and the attached subtrees is helpful to classify the semantic relations between two entities and our proposed method can achieve the state-of-the-art performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1585–1594},
numpages = {10},
keywords = {relation classification, shortest dependency path, dependency subtree, recursive neural network, convolution neural network}
}

@article{10.5555/2992829.2992839,
author = {Kuklasi\'{n}ski, Adam and Doclo, Simon and Jensen, S\o{}ren Holdt and Jensen, Jesper},
title = {Maximum Likelihood PSD Estimation for Speech Enhancement in Reverberation and Noise},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
abstract = {In this contribution, we focus on the problem of power spectral density (PSD) estimation from multiple microphone signals in reverberant and noisy environments. The PSD estimation method proposed in this paper is based on the maximum likelihood (ML) methodology. In particular, we derive a novel ML PSD estimation scheme that is suitable for sound scenes which besides speech and reverberation consists of an additional noise component whose second-order statistics are known. The proposed algorithm is shown to outperform an existing similar algorithm in terms of PSD estimation accuracy. Moreover, it is shown numerically that the mean-squared estimation error achieved by the proposed method is near the limit set by the corresponding Cram\'{e}r--Rao lower bound. The speech dereverberation performance of a multichannel Wiener filter based on the proposed PSD estimators is measured using several instrumental measures and is shown to be higher than when the competing estimator is used. Moreover, we perform a speech intelligibility test where we demonstrate that both the proposed and the competing PSD estimators lead to similar intelligibility improvements.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1595–1608},
numpages = {14},
keywords = {PSD estimation, Cram\'{e}r--Rao lower bound, reverberation, microphone array, maximum likelihood estimation}
}

@article{10.1109/TASLP.2016.2547743,
author = {Cavalieri, Daniel C. and Palazuelos-Cagigas, Sira E. and Bastos-Filho, Teodiano F. and Sarcinelli-Filho, M\'{a}rio},
title = {Combination of Language Models for Word Prediction: An Exponential Approach},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2547743},
doi = {10.1109/TASLP.2016.2547743},
abstract = {This paper proposes an exponential interpolation to merge a part-of-speech-based language model and a word-based n-gram language model to accomplish word prediction tasks. In order to find a set of mathematical equations to properly describe the language modeling, a model based on partial differential equations is proposed. With the appropriate initial conditions, it was found an interpolation model similar to the traditional maximum entropy language model. Improvements in keystroke saved and perplexity over the word-based n-gram language model and two other traditional interpolation models is obtained, considering three different languages. The proposed interpolation model also provides additional improvement in hit rate parameter.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1477–1490},
numpages = {14},
keywords = {word prediction (WP), natural language processing (NLP), combination of language models}
}

@article{10.1109/TASLP.2016.2567645,
author = {Dittmar, Christian and M\"{u}ller, Meinard},
title = {Reverse Engineering the Amen Break: Score-Informed Separation and Restoration Applied to Drum Recordings},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2567645},
doi = {10.1109/TASLP.2016.2567645},
abstract = {This work addresses the extraction of high-quality component signals from drum solo recordings (breakbeats) for music production and remixing purposes. Specifically, we employ audio source separation techniques to recover sound events from the drum sound mixture corresponding to the individual drum strokes. Our separation approach is based on an informed variant of non-negative matrix factor deconvolution (NMFD) that has been proposed and applied to drum transcription and separation in earlier works. In this paper, we systematically study the suitability of NMFD and the impact of audio- and score-based side information in the context of drum separation. In the case of imperfect decompositions, we observe different cross-talk artifacts appearing during the attack and the decay segment of the extracted drum sounds. Based on these findings, we propose and evaluate two extensions to the core technique. The first extension is based on applying a cascaded NMFD decomposition while retaining selected side information. The second extension is a time--frequency selective restoration approach using a dictionary of single note drum sounds. For all our experiments, we use a publicly available data set consisting of multitrack drum recordings and corresponding annotations that allows us to evaluate the source separation quality. Using this test set, we show that our proposed methods improve the quality of the component signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1531–1543},
numpages = {13},
keywords = {music decomposition, score-informed music processing, source separation, signal reconstruction}
}

@article{10.1109/TASLP.2016.2553457,
author = {Schwartz, Ofer and Gannot, Sharon and Habets, Emanu\"{e}l A. P.},
title = {An Expectation-Maximization Algorithm for Multimicrophone Speech Dereverberation and Noise Reduction with Coherence Matrix Estimation},
year = {2016},
issue_date = {September 2016},
publisher = {IEEE Press},
volume = {24},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2553457},
doi = {10.1109/TASLP.2016.2553457},
abstract = {In speech communication systems, the microphone signals are degraded by reverberation and ambient noise. The reverberant speech can be separated into two components, namely, an early speech component that consists of the direct path and some early reflections and a late reverberant component that consists of all late reflections. In this paper, a novel algorithm to simultaneously suppress early reflections, late reverberation, and ambient noise is presented. The expectation-maximization (EM) algorithm is used to estimate the signals and spatial parameters of the early speech component and the late reverberation components. As a result, a spatially filtered version of the early speech component is estimated in the E-step. The power spectral density (PSD) of the anechoic speech, the relative early transfer functions, and the PSD matrix of the late reverberation are estimated in the M-step of the EM algorithm. The algorithm is evaluated using real room impulse response recorded in our acoustic lab with a reverberation time set to 0.36 s and 0.61 s and several signal-to-noise ratio levels. It is shown that significant improvement is obtained and that the proposed algorithm outperforms baseline single-channel and multichannel dereverberation algorithms, as well as a state-of-the-art multichannel dereverberation algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1491–1506},
numpages = {16},
keywords = {dereverberation, noise reduction, expectation-maximization}
}

@article{10.5555/2992818.2992828,
author = {Zhang, Meng and Liu, Yang and Luan, Huanbo and Sun, Maosong},
title = {Listwise Ranking Functions for Statistical Machine Translation},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
abstract = {Decision rules play an important role in the tuning and decoding steps of statistical machine translation. The traditional decision rule selects the candidate with the greatest potential from a candidate space by examining each candidate individually. However, viewing each candidate as independent imposes a serious limitation on the translation task. We instead view the problem from a ranking perspective that naturally allows the consideration of an entire list of candidates as a whole through the adoption of a listwise ranking function. Our shift from a pointwise to a list-wise perspective proves to be a simple yet powerful extension to current modeling that allows arbitrary pairwise functions to be incorporated as features, whose weights can be estimated jointly with traditional ones. We further demonstrate that our formulation encompasses the minimum Bayes risk (MBR) approach, another decision rule that considers restricted listwise information, as a special case. Experiments show that our approach consistently outperforms the baseline and MBR methods across the considered test sets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1464–1472},
numpages = {9},
keywords = {discriminative reranking, statistical machine translation, listwise ranking function}
}

@article{10.1109/TASLP.2016.2558822,
author = {Du, Jun and Tu, Yanhui and Dai, Li-Rong and Lee, Chin-Hui},
title = {A Regression Approach to Single-Channel Speech Separation via High-Resolution Deep Neural Networks},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2558822},
doi = {10.1109/TASLP.2016.2558822},
abstract = {We propose a novel data-driven approach to single-channel speech separation based on deep neural networks (DNNs) to directly model the highly nonlinear relationship between speech features of a mixed signal containing a target speaker and other interfering speakers. We focus our discussion on a semisupervised mode to separate speech of the target speaker from an unknown interfering speaker, which is more flexible than the conventional supervised mode with known information of both the target and interfering speakers. Two key issues are investigated. First, we propose a DNN architecture with dual outputs of the features of both the target and interfering speakers, which is shown to achieve a better generalization capability than that with output features of only the target speaker. Second, we propose using a set of multiple DNNs, each intending to be signal-noise-dependent (SND), to cope with the difficulty that one single general DNN could not well accommodate all the speaker mixing variabilities at different signal-to-noise ratio (SNR) levels. Experimental results on the speech separation challenge (SSC) data demonstrate that our proposed framework achieves better separation results than other conventional approaches in a supervised or semisupervised mode. SND-DNNs could also yield significant performance improvements over a general DNN for speech separation in low SNR cases. Furthermore, for automatic speech recognition (ASR) following speech separation, this purely front-end processing with a single set of speaker-independent ASR acoustic models, achieves a relative word error rate (WER) reduction of 11.6% over a state-of-the-art separation and recognition system where a complicated joint back-end decoding framework with multiple sets of speaker-dependent ASR acoustic models needs to be implemented. When speaker-adaptive ASR acoustic models for the target speakers are adopted for the enhanced signals, another 12.1% WER reduction over our best speaker-independent ASR system is achieved.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1424–1437},
numpages = {14},
keywords = {deep neural network, speech separation, divide and conquer, dual outputs, robust speech recognition}
}

@article{10.1109/TASLP.2016.2560534,
author = {Swietojanski, Pawel and Li, Jinyu and Renals, Steve},
title = {Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2560534},
doi = {10.1109/TASLP.2016.2560534},
abstract = {This work presents a broad study on the adaptation of neural network acoustic models by means of learning hidden unit contributions (LHUC) ---a method that linearly re-combines hidden units in a speaker- or environment-dependent manner using small amounts of unsupervised adaptation data. We also extend LHUC to a speaker adaptive training (SAT) framework that leads to a more adaptable DNN acoustic model, working both in a speaker-dependent and a speaker-independent manner, without the requirements to maintain auxiliary speaker-dependent feature extractors or to introduce significant speaker-dependent changes to the DNN structure. Through a series of experiments on four different speech recognition benchmarks (TED talks, Switchboard, AMI meetings, and Aurora4) comprising 270 test speakers, we show that LHUC in both its test-only and SAT variants results in consistent word error rate reductions ranging from 5% to 23% relative depending on the task and the degree of mismatch between training and test data. In addition, we have investigated the effect of the amount of adaptation data per speaker, the quality of unsupervised adaptation targets, the complementarity to other adaptation techniques, one-shot adaptation, and an extension to adapting DNNs trained in a sequence discriminative manner.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1450–1463},
numpages = {14},
keywords = {factorisation, adaptation, learning hidden unit contributions (lHUC), deep neural networks (DNNs)}
}

@article{10.5555/2992818.2992819,
author = {Schepker, Henning and Doclo, Simon},
title = {Least-Squares Estimation of the Common Pole-Zero Filter of Acoustic Feedback Paths in Hearing Aids},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
abstract = {In adaptive feedback cancellation both the convergence speed and the computational complexity depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters, it has been proposed to model the acoustic feedback paths as the convolution of a time-invariant common pole-zero filter and time-varying all-zero filters, enabling to track fast changes. In this paper, a novel procedure to estimate the common pole-zero filter of acoustic feedback paths is presented. In contrast to previous approaches which minimize the so-called equation-error, we propose to approximate the desired output-error minimization by employing a weighted least-squares procedure motivated by the Steiglitz--McBride iteration. The estimation of the common pole-zero filter is formulated as a semidefinite programming problem, to which a constraint based on the Lyapunov theory is added in order to guarantee the stability of the estimated pole-zero filter. Experimental results using measured acoustic feedback paths from a two microphone behind-the-ear hearing aid show that the proposed optimization procedure using the Lyapunov constraint outperforms existing optimization procedures in terms of modelling accuracy and added stable gain.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1334–1347},
numpages = {14},
keywords = {Steiglitz--McBride, semidefinite programming, weighted equation-error, acoustic feedback cancellation, common part modeling, hearing aids, Lyapunov stability}
}

@article{10.1109/TASLP.2016.2556282,
author = {Pessentheiner, Hannes and Hagm\"{u}ller, Martin and Kubin, Gernot},
title = {Localization and Characterization of Multiple Harmonic Sources},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2556282},
doi = {10.1109/TASLP.2016.2556282},
abstract = {We introduce a new and intuitive algorithm to characterize and localize multiple harmonic sources intersecting in the spatial and frequency domains. It jointly estimates their fundamental frequencies, their respective amplitudes, and their directions of arrival based on an intelligent non-parametric signal representation. To obtain these parameters, we first apply variable-scale sampling on unbiased cross-correlation functions between pairs of microphone signals to generate a joint parameter space. Then, we employ a multidimensional maxima detector to represent the parameters in a sparse joint parameter space. In comparison to others, our algorithm solves the issue of pitch-period doubling when using cross-correlation functions, it estimates multiple harmonic sources with a signal power smaller than the signal power of the dominant harmonic source, and it associates the estimated parameters to their corresponding sources in a multidimensional sparse joint parameter space, which can be directly fed into a tracker. We tested our algorithm and three others on synthetic data and speech data recorded in a real reverberant environment and evaluated their performance by employing the joint recall measure, the root-mean-square error, and the cumulative distribution function of fundamental frequencies and directions of arrival. The evaluations show promising results: Our algorithm outperforms the others in terms of the joint recall measure, and it can achieve root-mean-square errors of 1 Hz or 1° and smaller, which facilitates, e.g., distant-speech enhancement or source separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1348–1363},
numpages = {16},
keywords = {pitch-period doubling, data association, pitch estimation, joint estimation, direction of arrival (DOA), microphone array, fundamental frequency, sparse joint parameter space (SJPS)}
}

@article{10.1109/TASLP.2016.2555085,
author = {Laufer, Bracha and Talmon, Ronen and Gannot, Sharon},
title = {Semi-Supervised Sound Source Localization Based on Manifold Regularization},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2555085},
doi = {10.1109/TASLP.2016.2555085},
abstract = {Conventional speaker localization algorithms, based merely on the received microphone signals, are often sensitive to adverse conditions, such as: high reverberation or low signal-to-noise ratio (SNR). In some scenarios, e.g., in meeting rooms or cars, it can be assumed that the source position is confined to a predefined area, and the acoustic parameters of the environment are approximately fixed. Such scenarios give rise to the assumption that the acoustic samples from the region of interest have a distinct geometrical structure. In this paper, we show that the high-dimensional acoustic samples indeed lie on a low-dimensional manifold and can be embedded into a low-dimensional space. Motivated by this result, we propose a semi-supervised source localization algorithm based on two-microphone measurements, which recovers the inverse mapping between the acoustic samples and their corresponding locations. The idea is to use an optimization framework based on manifold regularization, that involves smoothness constraints of possible solutions with respect to the manifold. The proposed algorithm, termed manifold regularization for localization, is adapted while new unlabelled measurements (from unknown source locations) are accumulated during runtime. Experimental results show superior localization performance when compared with a recently presented algorithm based on a manifold learning approach and with the generalized cross-correlation algorithm as a baseline. The algorithm achieves 2° accuracy in typical noisy and reverberant environments (reverberation time between 200 and 800 ms and SNR between 5 and 20 dB).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1393–1407},
numpages = {15},
keywords = {relative transfer function (RTF), diffusion distance, reproducing kernel Hilbert space (RKHS), sound source localization, manifold regularization}
}

@article{10.1109/TASLP.2016.2556422,
author = {Yang, Cheng-Yen and Liu, Chih-Wei and Jou, Shyh-Jye},
title = {A Systematic ANSI S1.11 Filter Bank Specification Relaxation and Its Efficient Multirate Architecture for Hearing-Aid Systems},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2556422},
doi = {10.1109/TASLP.2016.2556422},
abstract = {Recently, emerging mobile computing requires the high integration of hearing aids into a single system-on-chip. Modern hearing aid systems include a frequency decomposer, noise reduction, feedback cancellation, auditory compensation, and intelligent adaptation. The majority of existing works concentrated on improving the performance and efficiency on a single-signal processing block or one-sided effect. These works lacked comprehensive discussions on system-wide aspects regarding the overall impacts. To design an optimal hearing aid system, frequency decomposers, or the filter banks that dominate in hearing aid systems, are the first priority. We propose a systematic relaxation of the ANSI S1.11 specification and its design procedure for filter banks. The proposed design procedure overcomes the drawbacks of previous works and changes the five performance indices of the filter bank: delay, complexity, sub-band rate reduction, ripples of synthesized output, and prescription matchingerrors. These performance indices help system or algorithm designers in selecting a beneficial system-relaxed filter bank to achieve optimal hearing aids. The proposed multirate filter bank using the resampling method provides an efficient, low complexity, and delay-constrained computing architecture. Finally, seven design cases are used to demonstrate the proposed method, and comprehensive discussions of the five performance indices are presented.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1380–1392},
numpages = {13},
keywords = {hearing aids, NAL-NL1, ANSI S1.11, quasi-ANSI, multirate filter bank, prescription matching}
}

@article{10.1109/TASLP.2016.2556860,
author = {Khalilian, Hanieh and Baji\'{c}, Ivan V. and Vaughan, Rodney G.},
title = {Comparison of Loudspeaker Placement Methods for Sound Field Reproduction},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2556860},
doi = {10.1109/TASLP.2016.2556860},
abstract = {This paper presents a comparison between several loudspeaker placement methods for sound field reproduction (SFR). The goal of these placement methods is to reduce the SFR error under a power constraint by selecting suitable locations for the loudspeakers. The first method is based on singular value decomposition of the acoustic transfer function (ATF) matrix. Depending on the configuration, an ideal ATF matrix is created and, then, approximated by selecting the appropriate locations for the loudspeakers. Another method is based on the constrained matching pursuit (CMP) algorithm, in which candidate locations of the loudspeakers are selected iteratively to minimize the approximation error of the desired sound field. The third method is based on sparsity-promoting sound field approximation using the least absolute shrinkage and selection operator. Loudspeaker placements obtained using these methods are compared against benchmark configuration of uniformly distributed loudspeakers. The comparison indicates that for constrained power, the CMP-based placement has the least reproduction error.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1364–1379},
numpages = {16},
keywords = {sound field reproduction (SFR), singular value decomposition (SVD), least absolute shrinkage and selection operator (Lasso), constrained matching pursuit (CMP)}
}

@article{10.5555/2992818.2992824,
author = {Kounades-Bastian, Dionyssos and Girin, Laurent and Alameda-Pineda, Xavier and Gannot, Sharon and Horaud, Radu},
title = {A Variational EM Algorithm for the Separation of Time-Varying Convolutive Audio Mixtures},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
abstract = {This paper addresses the problem of separating audio sources from time-varying convolutive mixtures. We propose a probabilistic framework based on the local complex-Gaussian model combined with non-negative matrix factorization. The time-varying mixing filters are modeled by a continuous temporal stochastic process. We present a variational expectation--maximization (VEM) algorithm that employs a Kalman smoother to estimate the time-varying mixing matrix, and that jointly estimate the source parameters. The sound sources are then separated by Wiener filters constructed with the estimators provided by the VEM algorithm. Extensive experiments on simulated data show that the proposed method outperforms a blockwise version of a state-of-the-art baseline method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1408–1423},
numpages = {16},
keywords = {time-varying mixing filters, Kalman smoother, moving sources, variational EM, audio source separation}
}

@article{10.1109/TASLP.2016.2558826,
author = {Liu, Xunying and Chen, Xie and Wang, Yongqiang and Gales, Mark J. F. and Woodland, Philip C.},
title = {Two Efficient Lattice Rescoring Methods Using Recurrent Neural Network Language Models},
year = {2016},
issue_date = {August 2016},
publisher = {IEEE Press},
volume = {24},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2558826},
doi = {10.1109/TASLP.2016.2558826},
abstract = {An important part of the language modelling problem for automatic speech recognition (ASR) systems, and many other related applications, is to appropriately model long-distance context dependencies in natural languages. Hence, statistical language models (LMs) that can model longer span history contexts, for example, recurrent neural network language models (RNNLMs), have become increasingly popular for state-of-the-art ASR systems. As RNNLMs use a vector representation of complete history contexts, they are normally used to rescore N-best lists. Motivated by their intrinsic characteristics, two efficient lattice rescoring methods for RNNLMs are proposed in this paper. The first method uses an n-gram style clustering of history contexts. The second approach directly exploits the distance measure between recurrent hidden history vectors. Both methods produced 1-best performance comparable to a 10 k-best rescoring baseline RNNLM system on two large vocabulary conversational telephone speech recognition tasks for US English and Mandarin Chinese. Consistent lattice size compression and recognition performance improvements after confusion network (CN) decoding were also obtained over the prefix tree structured N-best rescoring approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1438–1449},
numpages = {12},
keywords = {recurrent neural network, lattice rescoring, speech recognition, language model}
}

@article{10.1109/TASLP.2016.2544661,
author = {Zhou, Guangyou and Xie, Zhiwen and He, Tingting and Zhao, Jun and Hu, Xiaohua Tony},
title = {Learning the Multilingual Translation Representations for Question Retrieval in Community Question Answering via Non-Negative Matrix Factorization},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2544661},
doi = {10.1109/TASLP.2016.2544661},
abstract = {Community question answering (CQA) has become an increasingly popular research topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find the most relevant and recent questions that have been solved by other users. However, the word ambiguity and word mismatch problems bring about new challenges for question retrieval in CQA. State-of-the-art approaches address these issues by implicitly expanding the queried questions with additional words or phrases using monolingual translation models. While useful, the effectiveness of these models is highly dependent on the availability of quality parallel monolingual corpora (e.g., question--answer pairs) in the absence of which they are troubled by noise issues. In this work, we propose an alternative way to address the word ambiguity and word mismatch problems by taking advantage of potentially rich semantic information drawn from other languages. Our proposed method employs statistical machine translation to improve question retrieval and enriches the question representation with the translated words from other languages via non-negative matrix factorization. Experiments conducted on real CQA data sets show that our proposed approach is promising.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1305–1314},
numpages = {10},
keywords = {community question answering, text mining, question retrieval, natural language processing, information retrieval}
}

@article{10.1109/TASLP.2016.2516439,
author = {Gao, Min and Lu, Jing and Qiu, Xiaojun},
title = {A Simplified Subband ANC Algorithm without Secondary Path Modeling},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2516439},
doi = {10.1109/TASLP.2016.2516439},
abstract = {Active noise control algorithms without secondary path modeling are very appealing because they can effectively overcome the problems caused by imperfect secondary path model. For broadband noise control, subband structures have been adopted in these kinds of algorithms, where the full-band adaptive filter coefficients are obtained from subband filter coefficients with weight transformation. However, the usually used two-direction search method suffers from slow convergence if the secondary path phase is close to± 90° To deal with this problem, a four-direction search method has been proposed in frequency domain, which is sometimes too complicated for practical applications. In this paper, a simplified algorithm is proposed to reduce the implementation complexity. In the proposed algorithm, the delay of the secondary path is estimated first, and then the estimated delay is used in the delay compensation to reduce the number of subbands. Two reference signals are generated in each subband and two update directions are used. Only one subband reference signal and one update direction are selected to approximate the phase response of the residual secondary path, and then the full-band adaptive controller coefficients are adjusted directly in time domain with them without the need of further weight transformation. Just like those existing subband algorithms, the simplified algorithm can obtain similar steady-state noise reduction performance as that of the FxLMS algorithm but without secondary path modeling. Simulations and experiments are carried out to demonstrate the feasibility of the proposed algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1164–1174},
numpages = {11},
keywords = {adaptive controller, simplified subband algorithm, active noise control (ANC), secondary path modeling}
}

@article{10.5555/2992803.2992814,
author = {Das, Rajib Lochan and Chakraborty, Mrityunjoy},
title = {Improving the Performance of the PNLMS Algorithm Using <i>l</i><sub>1</sub> Norm Regularization},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
abstract = {The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In this paper, we address this problem by introducing a carefully constructed l1 norm (of the coefficients) penalty in the PNLMS cost function which favors sparsity. This results in certain zero attracting terms in the PNLMS weight update equation which help in the shrinkage of the coefficients, especially the inactive taps, thereby arresting the slowing down of convergence and also producing lesser steady state excess mean square error (EMSE). A rigorous convergence analysis of the proposed algorithm is presented that expresses the steady state mean square deviation of both the active and the inactive taps in terms of a zero attracting coefficient of the algorithm. The analysis reveals that further reduction of the EMSE is possible by deploying a variable step size (VSS) simultaneously with a variable zero attracting coefficient in the weight update process. Simulation results confirm superior performance of the proposed VSS zero attracting PNLMS algorithm over existing algorithms, especially in terms of having both higher convergence speed and lesser steady state EMSE simultaneously.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1280–1290},
numpages = {11},
keywords = {PNLMS algorithm, sparse systems, mean square deviation (msd), zero attractors, variable step size}
}

@article{10.1109/TASLP.2016.2522643,
author = {Aihara, Ryo and Takiguchi, Tetsuya and Ariki, Yasuo},
title = {Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2522643},
doi = {10.1109/TASLP.2016.2522643},
abstract = {A novel voice conversion (VC) method for arbitrary speakers is proposed. Non-negative matrix factorization (NMF) has recently been applied to exemplar-based VC. It offers noise robustness and naturalness of the converted voice, compared with widely used Gaussian mixture model-based VC. However, because NMF-based VC requires parallel training data from source and target speakers, the voice of arbitrary speakers cannot be converted in this framework. In this study, we propose the multiple non-negative matrix factorization (Multi-NMF) to allow the implementation of many-to-many, exemplar-based VC. Our experimental results demonstrate that the conversion quality of the proposed method is close to that of conventional one-to-one VC, even though the proposed method requires neither the source speakers' spectra, nor the target speakers' spectra, to be included in the training set.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1175–1184},
numpages = {10},
keywords = {non-negative matrix factorization (nmf), voice conversion, many-to-many, speech synthesis, exemplar-based}
}

@article{10.1109/TASLP.2016.2551863,
author = {Degottex, Gilles and Ardaillon, Luc and Roebel, Axel},
title = {Multi-Frame Amplitude Envelope Estimation for Modification of Singing Voice},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2551863},
doi = {10.1109/TASLP.2016.2551863},
abstract = {Singingvoice synthesis benefits from very highquality estimation of the resonances and anti-resonances of the vocal tract filter (VTF), i.e., an amplitude spectral envelope. In the state of the art, a single frame of DFT transform is commonly used as a basis for building spectral envelopes. Even though multiple frame analysis (MFA) has already been suggested for envelope estimation, it is not yet used in concrete applications. Indeed, even though existing attempts have shown very interesting results, we will demonstrate that they are either over complicated or fail to satisfy the high accuracy that is necessary for singing voice. In order to allow future applications of MFA, this article aims to improve the theoretical understanding and advantages of MFA-based methods. The use of singing voice signals is very beneficial for studying MFA methods due to the fact that the VTF configuration can be relatively stable and, at the same time, the vibrato creates a regular variation that is easy to model. By simplifying and extending previous works, we also suggest and describe two MFA-based methods. To better understand the behaviors of the envelope estimates, we designed numerical measurements to assess single frame analysis and MFA methods using synthetic signals. With listening tests, we also designed two proofs of concept using pitch scaling and conversion of timbre. Both evaluations show clear and positive results for MFA-based methods, thus, encouraging this research direction for future applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1242–1254},
numpages = {13},
keywords = {voice synthesis, spectral envelope, singing voice, voice analysis and modeling, multi frame analysis}
}

@article{10.1109/TASLP.2016.2551865,
author = {Wu, Zhizheng and King, Simon},
title = {Improving Trajectory Modelling for DNN-Based Speech Synthesis by Using Stacked Bottleneck Features and Minimum Generation Error Training},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2551865},
doi = {10.1109/TASLP.2016.2551865},
abstract = {We propose two novel techniques---stacking bottleneck features and minimum generation error (MGE) training criterion---to improve the performance of deep neural network (DNN)-based speech synthesis. The techniques address the related issues of frame-by-frame independence and ignorance of the relationship between static and dynamic features, within current typical DNN-based synthesis frameworks. Stacking bottleneck features, which are an acoustically informed linguistic representation, provides an efficient way to include more detailed linguistic context at the input. The MGE training criterion minimises overall output trajectory error across an utterance, rather than minimising the error per frame independently, and thus takes into account the interaction between static and dynamic features. The two techniques can be easily combined to further improve performance. We present both objective and subjective results that demonstrate the effectiveness of the proposed techniques. The subjective results show that combining the two techniques leads to significantly more natural synthetic speech than from conventional DNN or long short-term memory recurrent neural network systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1255–1265},
numpages = {11},
keywords = {deep neural network, acoustic modelling, bottleneck, minimum generation error, speech synthesis}
}

@article{10.1109/TASLP.2016.2549699,
author = {Deepak, K. T. and Prasanna, S. R. Mahadeva},
title = {Foreground Speech Segmentation and Enhancement Using Glottal Closure Instants and Mel Cepstral Coefficients},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2549699},
doi = {10.1109/TASLP.2016.2549699},
abstract = {In this paper, the speech signal recorded from the desired speaker close to microphone in natural environment is regarded as foreground speech and rest of the interfering sources as background noise. The proposed paper exploits speech production features like glottal closure instants in time domain and vocal tract information in spectral domain to segment the desired speaker's speech and to further enhance it. The foreground speech is perceptually enhanced using the auditory perception feature in mel-frequency domain using mel-cepstral coefficients and its inversion using mel log spectrum approximation filter. The focus is on enhancing the production and perceptual features of foreground speech rather than relying on modeling the interfering sources. The speech data are collected in different natural environments from different speakers in order to evaluate the proposed method. The enhanced speech signals derived at three different stages of the proposed method are evaluated with state-of-the-art methods in terms of subjective and objective measures. The proposed method provides improved performance compared to the considered state-of-the-art methods. In terms of the proposed objective measure foreground to background Ratio, the enhancement approach presented in this paper gives an average improvement of 12 dB as opposed to existing spectral subtraction-based method which provides 3 dB. Moreover, subjective evaluation using 24 different subjects corroborates the objective test results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1204–1218},
numpages = {15},
keywords = {MCC, MLSA, speech enhancement, zero band filter, foreground segmentation, glottal closure instants}
}

@article{10.1109/TASLP.2016.2551864,
author = {Sahoo, Subhasmita and Routray, Aurobinda},
title = {A Novel Method of Glottal Inverse Filtering},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2551864},
doi = {10.1109/TASLP.2016.2551864},
abstract = {This paper presents a new technique for glottal inverse filtering using a distributed model of the vocal tract. A discrete state space model has been constructed for the speech production system by combining the concatenated tube model of the vocal tract and Liljencrants--Fant (LF) model of the glottal flow derivative waveform. An adaptive system identification technique, basedonextended Kalmanfiltering, has been used for estimation of the states and model parameters from continuous speech. The glottal signal, represented by the LF model, is piecewise differentiable in one glottal cycle. Hence, the hybrid system has been characterized by separate models during two different modes. Multiple model estimation has been performed by switching between the two models at the mode jumps. The open phase of the glottal cycle has been considered as Mode 1; whereas, the return phase and closed phase combined has been taken as Mode 2. The starting point of Mode 1, also known as glottal opening instant, was estimated by observing formant modulation, which remains negligible during closed phase, and starts to increase at the onset of opening. The starting point of Mode 2, also known as the glottal closing instant, was computed by peak-picking from linear prediction (LP) residual signal. The proposed method estimates the glottal waveform as well as changes in flow occurring at different sections of the vocal tract during speech production. This technique has been found to be accurate and robust to variations in pitch as compared to other LP-based methods in the literature. The method also estimates the air pressure distribution at different sections of the vocal tract.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1230–1241},
numpages = {12},
keywords = {LF model, extended Kalman filter, concatenated tube model, multiple model estimation, glottal inverse filtering}
}

@article{10.1109/TASLP.2016.2551041,
author = {Hajimolahoseini, Habib and Amirfattahi, Rassoul and Gazor, Saeed and Soltanian-Zadeh, Hamid},
title = {Robust Estimation and Tracking of Pitch Period Using an Efficient Bayesian Filter},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2551041},
doi = {10.1109/TASLP.2016.2551041},
abstract = {In this paper, we introduce an algorithm for estimating and tracking the pitch period of audio signals using Bayesian filters. For this purpose, we propose a general Bayesian model, which is robust to the nonstationary variations of the amplitude and frequency of the input signal. We also employ a state-space model, which uses the delayed versions of the input signal to model the periodicity of nonstationary audio signals. This simple model allows a significant reduction of the required number of particles for the estimation of the pitch period compared to the state-of-the-art particle filtering methods. Moreover, we propose to estimate the logarithm of the period instead of the period itself. We show that the resulting algorithm does not require prior knowledge about the initial state and is robust to the octave error phenomenon, which is a common problem in pitch period estimation methods. Most of the existing methods require that the processing window be longer than the largest existing period of the input signal. In contrast, the proposed method does not impose such a limit. Our method often results in a higher time-domain resolution with no perceptible compromise on the frequency-domain resolution, especially for high-pitched audio signals such as music. Simulation results reveal that the proposed algorithm outperforms the state-of-the-art pitch period detection algorithms at low signal to noise ratios assuming no prior knowledge about the initial conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1219–1229},
numpages = {11},
keywords = {audio signals, real-time estimation, Bayesian filtering, time-varying pitch period estimation, particle filtering, state-space model}
}

@article{10.5555/2992803.2992806,
author = {Chen, Kai and Huo, Qiang},
title = {Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
abstract = {This paper presents a study of using deep bidirectional long short-term memory (DBLSTM) recurrent neural network as acoustic model for DBLSTM-HMM based large vocabulary continuous speech recognition (LVCSR), where a context-sensitive-chunk (CSC) back-propagation through time (BPTT) approach is used to train DBLSTM by splitting each training sequence into chunks with appended contextual observations, and a CSC-based decoding method with possibly overlapped CSCs is used for recognition. Our approach makes mini-batch based training on GPU more efficient and reduces the latency of DBLSTM-based LVCSR from a whole utterance to a short chunk. Evaluations have been made on Switchboard-I benchmark task. In comparison with epoch-wise BPTT training, our method can achieve more than three times speedup on a single GPU card without degrading recognition accuracy. In comparison with a highly optimized DNN-HMM system trained by a frame-level cross entropy (CE) criterion, our CE-trained DBLSTM-HMM system achieves relative word error rate reductions of 9% and 5% on Eval2000 and RT03S testing sets, respectively. Furthermore, by running model averaging based parallel training of DBLSTM on a cluster of GPUs, CSC-BPTT incurs less accuracy degradation than epoch-wise BPTT while achieves a linear speedup.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1185–1193},
numpages = {9},
keywords = {BPTT, long short-term memory, model averaging, DNN, context sensitive chunk, DBLSTM, parallel training, LVCSR}
}

@article{10.1109/TASLP.2016.2553441,
author = {Jaureguiberry, Xabier and Vincent, Emmanuel and Richard, Ga\"{e}l},
title = {Fusion Methods for Speech Enhancement and Audio Source Separation},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2553441},
doi = {10.1109/TASLP.2016.2553441},
abstract = {A wide variety of audio source separation techniques exist and can already tackle many challenging industrial issues. However, in contrast with other application domains, fusion principles were rarely investigated in audio source separation despite their demonstrated potential in classification tasks. In this paper, we propose a general fusion framework which takes advantage of the diversity of existing separation techniques in order to improve separation quality. We obtain new source estimates by summing the individual estimates given by different separation techniques weighted by a set of fusion coefficients. We investigate three alternative fusion methods which are based on standard nonlinear optimization, Bayesian model averaging, or deep neural networks. Experiments conducted for both speech enhancement and singing voice extraction demonstrate that all the proposed methods outperform traditional model selection. The use of deep neural networks for the estimation of time-varying coefficients notably leads tolarge quality improvements, up to 3 dB in terms of signal-to-distortion ratio compared to model selection.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1266–1279},
numpages = {14},
keywords = {aggregation, variational Bayes, deep learning, audio source separation, non-negative matrix factorization (NMF), model averaging, ensemble, fusion, deep neural networks (DNNs), speech enhancement, singing voice extraction}
}

@article{10.1109/TASLP.2016.2545928,
author = {Kim, Chanwoo and Stern, Richard M.},
title = {Power-Normalized Cepstral Coefficients (PNCC) for Robust Speech Recognition},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2545928},
doi = {10.1109/TASLP.2016.2545928},
abstract = {This paper presents a new feature extraction algorithm called power normalized Cepstral coefficients (PNCC) that is motivated by auditory processing. Major new features of PNCC processing include the use of a power-law nonlinearity that replaces the traditional log nonlinearity used in MFCC coefficients, a noise-suppression algorithm based on asymmetric filtering that suppresses background excitation, and a module that accomplishes temporal masking. We also propose the use of medium-time power analysis in which environmental parameters are estimated over a longer duration than is commonly used for speech, as well as frequency smoothing. Experimental results demonstrate that PNCC processing provides substantial improvements in recognition accuracy compared to MFCC and PLP processing for speech in the presence of various types of additive noise and in reverberant environments, with only slightly greater computational cost than conventional MFCC processing, and without degrading the recognition accuracy that is observed while training and testing using clean speech. PNCC processing also provides better recognition accuracy in noisy environments than techniques such as vector Taylor series (VTS) and the ETSI advanced front end (AFE) while requiring much less computation. We describe an implementation of PNCC using "online processing" that does not require future knowledge of the input.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1315–1329},
numpages = {15},
keywords = {asymmetric filtering, medium-time power estimation, spectral weight smoothing, rate-level curve, on-line speech processing, feature extraction, temporal masking, physiological modeling, robust speech recognition, power function, modulation filtering}
}

@article{10.1109/TASLP.2016.2540815,
author = {Taseska, Maja and Habets, Emanu\"{e}l A. P.},
title = {Spotforming: Spatial Filtering with Distributed Arrays for Position-Selective Sound Acquisition},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2540815},
doi = {10.1109/TASLP.2016.2540815},
abstract = {Hands-free capture of speech often requires extraction of sources from a certain spot of interest (SOI), while reducing interferers and background noise. Although state-of-the-art spatial filters are fully data-dependent and computed using the power spectral density (PSD) matrices of the desired and the undesired signals, the existing solutions to extract sources from a SOI are only partially data-dependent. Estimating the time-varying PSD matrices from the data is a challenging problem, especially in dynamic and quickly time-varying acoustic scenes. Hence, the spot signal statistics are often pre-computed based on a near-field propagation model, resulting in suboptimal filters. In this work, we propose a fully data-dependent spatial filtering framework for extraction of speech signals that originate from a SOI. To achieve position-based spatial selectivity, distributed arrays are used, which offer larger spatial diversity compared to arrays of closely spaced microphones. The PSD matrices of the desired and the undesired signals are updated at each time-frequency bin by using a minimum Bayes risk detector that is based on a probabilistic model of narrowband position estimates. The proposed framework is applicable in challenging multitalk situations, without requiring any prior information, except the geometry, location, and orientation of the arrays.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1291–1304},
numpages = {14},
keywords = {PSD matrix estimation, signal detection., distributed arrays, spatial filtering, source extraction}
}

@article{10.1109/TASLP.2016.2546458,
author = {Stafylakis, Themos and Alam, Md. Jahangir and Kenny, Patrick},
title = {Text-Dependent Speaker Recognition with Random Digit Strings},
year = {2016},
issue_date = {July 2016},
publisher = {IEEE Press},
volume = {24},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2546458},
doi = {10.1109/TASLP.2016.2546458},
abstract = {In this paper, we explore joint factor analysis (JFA) for text-dependent speaker recognition with random digit strings. The core of the proposed method is a JFA model by which we extract features. These features can either represent overall utterances or individual digits, and are fed into a trainable backend to estimate likelihood ratios. Within this framework, several extensions are proposed. First is a logistic regression method for combining log-likelihood ratios that correspond to individual mixture components. Second is the extraction of phonetically aware Baum--Welch statistics, by using forced alignment instead of the typical posterior probabilities that are derived by the universal background model. We also explore a digit-string-dependent way to apply score normalization that exhibits a notable improvement compared to the standard one. By fusing six JFA features, we attained 2.01% and 3.19% equal error rates on male and female, respectively, on the challenging RSR2015 (part III) dataset.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1194–1203},
numpages = {10},
keywords = {text-dependent speaker recognition, joint factor analysis}
}

@article{10.1109/TASLP.2016.2537982,
author = {Chen, Yan-You and Wu, Chung-Hsien and Huang, Yi-Chin and Lin, Shih-Lun and Wang, Jhing-Fa},
title = {Candidate Expansion and Prosody Adjustment for Natural Speech Synthesis Using a Small Corpus},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2537982},
doi = {10.1109/TASLP.2016.2537982},
abstract = {This study proposes a hybrid approach to natural-sounding speech synthesis based on candidate expansion, unit selection, and prosody adjustment using a small corpus. The proposed method is more specific to tonal language, in particular Mandarin. In conventional speech synthesis studies, the quality of synthesized speech depends heavily on the size of the speech corpus. However, it is highly time-consuming and labor-intensive to prepare a large labeled corpus. In this work, candidate expansion is proposed to retrieve potential candidates that are unlikely to be retrieved using only linguistic features. The optimal unit sequence is then obtained from the expanded candidates by using the proposed unit selection mechanism at the phoneme and prosodic word levels. Finally, a prosodic word-level prosody adjustment is proposed to improve the continuity and smoothness of the prosody of the synthesized speech. To evaluate the proposed method, the Tsing-Hua corpus of speech synthesis was adopted. The results of an objective evaluation demonstrate the effectiveness of candidate expansion and the improvement of the continuity and smoothness of the prosody of the synthesized speech. The results of a subjective evaluation also show the proposed system could synthesize the speech with improved quality and naturalness, in particular for a small-sized or resource-limited corpus.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1052–1065},
numpages = {14},
keywords = {prosody adjustment, hybrid speech synthesis, articulatory features, candidate expansion}
}

@article{10.1109/TASLP.2016.2533865,
author = {Chen, Lijiang and Mao, Xia and Yan, Hong},
title = {Text-Independent Phoneme Segmentation Combining EGG and Speech Data},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2533865},
doi = {10.1109/TASLP.2016.2533865},
abstract = {A new approach for text-independent phoneme segmentation at sampling point level is proposed in this paper. The algorithm consists of two phases: First, the voiced sections in speech data are detected using the information of vocal folds vibration contained in electroglottograph (EGG). A Hilbert envelope feature is adopted to achieve sampling point level detection accuracy. Second, the voiced sections and other sections are treated separately. Each voiced section is divided into several candidate phonemes using the Viterbi algorithm. Then adjacent candidate phonemes are merged based on a Hotellings T-square test method. For other sections, the unvoiced consonants are detected from silence based on a singularity exponent feature. Comparison experiments show that the proposed method has better performance than the existing ones for a variety of tolerances, and is more robust to noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1029–1037},
numpages = {9},
keywords = {electroglottograph, singularity exponent, hotellings t-square test, viterbi algorithm, hilbert envelope}
}

@article{10.1109/TASLP.2016.2537202,
author = {Tavakoli, Vincent Mohammad and Jensen, Jesper Rindom and Christensen, Mads Gr\ae{}sb\o{}ll and Benesty, Jacob},
title = {A Framework for Speech Enhancement with Ad Hoc Microphone Arrays},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2537202},
doi = {10.1109/TASLP.2016.2537202},
abstract = {Speech enhancement is vital for improved listening practices. Ad hoc microphone arrays are promising assets for this purpose. Most well-established enhancement techniques with conventional arrays can be adapted into ad hoc scenarios. Despite recent efforts to introduce various ad hoc speech enhancement apparatus, a common framework for integration of conventional methods into this new scheme is still missing. This paper establishes such an abstraction based on inter and intra subarray speech coherencies. Along with measures for signal quality at the input of subarrays, a measure of coherency is proposed both for subarray selection in local enhancement approaches, and also for selecting a proper global reference when more than one subarray are used. Proposed methods within this framework are evaluated with regard to quantitative and qualitative measures, including array gains, the speech distortion ratio, the PESQ measure, and the STOI intelligibility measure. Major findings in this work are the observed changes in the superiority of different methods for certain conditions. When perceptual quality or intelligibility of the speech are the ultimate goals, there are turning points where the MVDR and the LCMV are superior to Wiener-based methods. Also, for certain scenarios, local approaches may be preferred to global ones.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1038–1051},
numpages = {14},
keywords = {pseudo-coherence vector, microphone array, speech enhancement, multichannel, ad hoc array, noise reduction}
}

@article{10.1109/TASLP.2016.2533859,
author = {Wang, Lin and Hon, Tsz-Kin and Reiss, Joshua D. and Cavallaro, Andrea},
title = {An Iterative Approach to Source Counting and Localization Using Two Distant Microphones},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2533859},
doi = {10.1109/TASLP.2016.2533859},
abstract = {We propose a time difference of arrival (TDOA) estimation framework based on time-frequency inter-channel phase difference (IPD) to count and localize multiple acoustic sources in a reverberant environment using two distant microphones. The time-frequency (T-F) processing enables exploitation of the non-stationarity and sparsity of audio signals, increasing robustness to multiple sources and ambient noise. For inter-channel phase difference estimation, we use a cost function, which is equivalent to the generalized cross correlation with phase transform (GCC) algorithm and which is robust to spatial aliasing caused by large inter-microphone distances. To estimate the number of sources, we further propose an iterative contribution removal (ICR) algorithm to count and locate the sources using the peaks of the GCC function. In each iteration, we first use IPD to calculate the GCC function, whose highest peak is detected as the location of a sound source; then we detect the T-F bins that are associated with this source and remove them from the IPD set. The proposed ICR algorithm successfully solves the GCC peak ambiguities between multiple sources and multiple reverberant paths.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1079–1093},
numpages = {15},
keywords = {ipd, microphone array, source counting, tdoa estimation, gcc-phat}
}

@article{10.1109/TASLP.2015.2511925,
author = {Celikyilmaz, Asli and Sarikaya, Ruhi and Jeong, Minwoo and Deoras, Anoop},
title = {An Empirical Investigation of Word Class-Based Features for Natural Language Understanding},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2511925},
doi = {10.1109/TASLP.2015.2511925},
abstract = {There are many studies that show using class-based features improves the performance of natural language processing (NLP) tasks such as syntactic part-of-speech tagging, dependency parsing, sentiment analysis, and slot filling in natural language understanding (NLU), but not much has been reported on the underlying reasons for the performance improvements. In this paper, we investigate the effects of the word class-based features for the exponential family of models specifically focusing on NLU tasks, and demonstrate that the performance improvements could be attributed to the regularization effect of the class-based features on the underlying model. Our hypothesis is based on empirical observation that shrinking the sum of parameter magnitudes in an exponential model tends to improve performance. We show on several semantic tagging tasks that there is a positive correlation between the model size reduction by the addition of the class-based features and the model performance on a held-out dataset. We also demonstrate that class-based features extracted from different data sources using alternate word clustering methods can individually contribute to the performance gain. Since the proposed features are generated in an unsupervised manner without significant computational overhead, the improvements in performance largely come for free and we show that such features provide gains for a wide range of tasks from semantic classification and slot tagging in NLU to named entity recognition (NER).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {994–1005},
numpages = {12},
keywords = {conditional random fields, regularization, exponential models, class-based features, shrinkage features, natural language understanding}
}

@article{10.1109/TASLP.2016.2541303,
author = {Ouali, Chahid and Dumouchel, Pierre and Gupta, Vishwa},
title = {Fast Audio Fingerprinting System Using GPU and a Clustering-Based Technique},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2541303},
doi = {10.1109/TASLP.2016.2541303},
abstract = {In this paper, we present our audio fingerprinting system that detects a transformed copy of an audio from a large collection of audios in a database. The audio fingerprints in this system encode the positions of salient regions of binary images derived from a spectrogram matrix. The similarity between two fingerprints is defined as the intersection of their elements (i.e. positions of the salient regions). The search algorithm labels each reference fingerprint in the database with the closest query frame and then counts the number of matching frames when the query is overlaid over the reference. The best match is based on this count. The salient regions fingerprints together with this nearest-neighbor search give excellent copy detection results. However, for a large database, this search is time consuming. To reduce the search time, we accelerate this similarity search by using a graphics processing unit (GPU). To speed this search even further, we use a two-step search based on a clustering technique and a lookup table that reduces the number of comparisons between the query and the reference fingerprints. We also explore the tradeoff between the speed of search and the copy detection performance. The resulting system achieves excellent results on TRECVID 2009 and 2010 datasets and outperforms several state-of-the-art audio copy detection systems in detection performance, localization accuracy and run time. For a fast detection scenario with detection speed comparable to the Ellis' Shazam-based system, our system achieved the same min NDCR as the NN-based system, and significantly better detection accuracy than Ellis' Shazam-based system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1106–1118},
numpages = {13},
keywords = {clustering, parallel processing, content-based copy detection, fast search, audio fingerprint, gpu}
}

@article{10.1109/TASLP.2016.2526782,
author = {Qian, Xiaojun and Meng, Helen and Soong, Frank},
title = {A Two-Pass Framework of Mispronunciation Detection and Diagnosis for Computer-Aided Pronunciation Training},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2526782},
doi = {10.1109/TASLP.2016.2526782},
abstract = {This paper presents a two-pass framework with discriminative acoustic modeling for mispronunciation detection and diagnoses (MD&D). The first pass of mispronunciation detection does not require explicit phonetic error pattern modeling. The framework instantiates a set of antiphones and a filler model to augment the original phone model for each canonical phone. This guarantees full coverage of all possible error patterns while maximally exploiting the phonetic information derived from the text prompt. The antiphones can be used to detect substitutions. The filler model can detect insertions, and phone skips are allowed to detect deletions. As such, there is no prior assumption on the possible error patterns that can occur. The second pass of mispronunciation diagnosis expands the detected insertions and substitutions into phone networks, and another recognition pass attempts to reveal the phonetic identities of the detected mispronunciation errors. Discriminative training (DT) is applied respectively to the acoustic models of the mispronunciation detection pass and the mispronunciation diagnosis pass. DT effectively separates the acoustic models of the canonical phones and the antiphones. Overall, with DT in both passes of MD&D, the error rate is reduced by 40.4% relative, compared with the maximum likelihood baseline. After DT, the error rates of the respective passes are also lower than those of a strong single-pass baseline with DT by 1.3% and 5.1% relative which are statistically significant.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1020–1028},
numpages = {9},
keywords = {discriminative training, mispronunciation detection and diagnosis, computer-aided pronunciation training}
}

@article{10.1109/TASLP.2016.2522646,
author = {Nguyen, Duc Hoang Ha and Xiao, Xiong and Chng, Eng Siong and Li, Haizhou},
title = {Feature Adaptation Using Linear Spectro-Temporal Transform for Robust Speech Recognition},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2522646},
doi = {10.1109/TASLP.2016.2522646},
abstract = {Spectral information represents short-term speech information within a frame of a few tens of milliseconds, while temporal information captures the evolution of speech statistics over consecutive frames. Motivated by the findings that human speech comprehension relies on the integrity of both the spectral content and temporal envelope of speech signal, we study a spectro-temporal transform framework that adapts run-time speech features to minimize the mismatch between run-time and training data, and its implementation that includes cross transform and cascaded transform. A Kullback-Leibler divergence based cost function is proposed to estimate the transform parameters. We conducted experiments on the REVERB Challenge 2014 task, where clean and multi-condition trained acoustic models are tested with real reverberant and noisy speech. We found that temporal information is important for reverberant speech recognition and the simultaneous use of spectral and temporal information for feature adaptation is effective. We also investigate the combination of the cross transform with fMLLR, the combination of batch, utterance and speaker mode adaptation, and multicondition adaptive training using proposed transforms. All experiments consistently report significant word error rate reductions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1006–1019},
numpages = {14},
keywords = {robust speech recognition, temporal filtering, feature adaptation, linear transform}
}

@article{10.1109/TASLP.2016.2541299,
author = {Raposo, Francisco and Ribeiro, Ricardo and de Matos, David Martins},
title = {Using Generic Summarization to Improve Music Information Retrieval Tasks},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2541299},
doi = {10.1109/TASLP.2016.2541299},
abstract = {In order to satisfy processing time constraints, many music information retrieval (MIR) tasks process only a segment of the whole music signal. This may lead to decreasing performance, as the most important information for the tasks may not be in the processed segments. We leverage generic summarization algorithms, previously applied to text and speech, to summarize items in music datasets. These algorithms build summaries (both concise and diverse), by selecting appropriate segments from the input signal, also making them good candidates to summarize music. We evaluate the summarization process on binary and multiclass music genre classification tasks, by comparing the accuracy when using summarized datasets against the accuracy when using human-oriented summaries, continuous segments (the traditional method used for addressing the previously mentioned time constraints), and full songs of the original dataset. We show that GRASSHOPPER, LexRank, LSA, MMR, and a Support Sets-based centrality model improve classification performance when compared to selected baselines. We also show that summarized datasets lead to a classification performance whose difference is not statistically significant from using full songs. Furthermore, we make an argument stating the advantages of sharing summarized datasets for future MIR research.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1119–1128},
numpages = {10},
keywords = {music classification, automatic music summarization, generic summarization algorithms}
}

@article{10.1109/TASLP.2016.2545920,
author = {Taghia, Jalal and Martin, Rainer},
title = {A Frequency-Domain Adaptive Line Enhancer with Step-Size Control Based on Mutual Information for Harmonic Noise Reduction},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2545920},
doi = {10.1109/TASLP.2016.2545920},
abstract = {We propose an adaptive line enhancer with a frequency-dependent step-size. The proposed frequency-domain adaptive line enhancer is used as a single-channel noise reduction system for removing harmonic noise from noisy speech. Our main contribution is to exploit the temporal dependence in the log-magnitude and phase spectra of the noisy speech using mutual information, and to derive a frequency-dependent step-size which detects the presence of harmonic noise in different frequency bins. Our proposed step-size control allows the suppression of harmonic noise and the preservation of speech components. The experiments are performed with different real-life acoustic noises which contain harmonic components. Using instrumental speech intelligibility and quality measures, we demonstrate that the proposed approach can outperform the conventional frequency-domain adaptive line enhancer with a fixed step-size for harmonic noise reduction.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1140–1154},
numpages = {15},
keywords = {speech enhancement, correlation, mutual information, step-size control, harmonic noise, adaptive line enhancer}
}

@article{10.1109/TASLP.2016.2540805,
author = {Zhang, Xueliang and Zhang, Hui and Nie, Shuai and Gao, Guanglai and Liu, Wenju},
title = {A Pairwise Algorithm Using the Deep Stacking Network for Speech Separation and Pitch Estimation},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2540805},
doi = {10.1109/TASLP.2016.2540805},
abstract = {Speech separation and pitch estimation in noisy conditions are considered to be a "chicken-and-egg" problem. On one hand, pitch information is an important cue for speech separation. On the other hand, speech separation makes pitch estimation easier when background noise is removed. In this paper, we propose a supervised learning architecture to solve these two problems iteratively. The proposed algorithm is based on the deep stacking network (DSN), which provides a method for stacking simple processing modules to build deep architectures. Each module is a classifier whose target is the ideal binary mask (IBM), and the input vector includes spectral features, pitch-based features and the output from the previous module. During the testing stage, we estimate the pitch using the separation results and update the pitch-based features to the next module. When embedded into the DSN, pitch estimation and speech separation each run several times. We obtain the final results from the last module. Systematic evaluations show that the proposed system results in both a high quality estimated binary mask and accurate pitch estimation and outperforms recent systems in its generalization ability.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1066–1078},
numpages = {13},
keywords = {computational auditory scene analysis, pitch estimation, speech separation, supervised learning}
}

@article{10.1109/TASLP.2016.2544660,
author = {Li, Lantian and Wang, Dong and Zhang, Chenhao and Zheng, Thomas Fang},
title = {Improving Short Utterance Speaker Recognition by Modeling Speech Unit Classes},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2544660},
doi = {10.1109/TASLP.2016.2544660},
abstract = {Short utterance speaker recognition (SUSR) is highly challenging due to the limited enrollment and/or test data. We argue that the difficulty can be largely attributed to the mismatched prior distributions of the speech data used to train the universal background model (UBM) and those for enrollment and test. This paper presents a novel solution that distributes speech signals into a multitude of acoustic subregions that are defined by speech units, and models speakers within the subregions. To avoid data sparsity, a data-driven approach is proposed to cluster speech units into speech unit classes, based on which robust subregion models can be constructed. Further more, we propose a model synthesis approach based on maximum likelihood linear regression (MLLR) to deal with no-data speech unit classes. The experiments were conducted on a publicly available database SUD12. The results demonstrated that on a text-independent speaker recognition task where the test utterances are no longer than 2 seconds and mostly shorter than 0.5 seconds, the proposed sub-region modeling offered a 21.51% relative reduction in equal error rate (EER), compared with the standard GMM-UBM baseline. In addition, with the model synthesis approach, the performance can be greatly improved in scenarios where no enrollment data are available for some speech unit classes.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1129–1139},
numpages = {11},
keywords = {model synthesis, subregion model, speaker recognition, short utterance}
}

@article{10.1109/TASLP.2016.2536481,
author = {O'Leary, Se\'{a}n and R\"{o}bel, Axel},
title = {A Montage Approach to Sound Texture Synthesis},
year = {2016},
issue_date = {June 2016},
publisher = {IEEE Press},
volume = {24},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2536481},
doi = {10.1109/TASLP.2016.2536481},
abstract = {Sound texture synthesis has applications in creating audio scenes for film and video games. In this paper, a novel algorithm for sound texture synthesis is presented. The goal of this algorithm is to produce new examples of a given sampled texture, the synthesized textures being of any desired duration. The algorithm is based on a montage approach to synthesis in that the original sample is cut into small pieces, referred to as atoms, and these atoms are concatenated together in a new sequence, preserving certain structures of the original texture. The sequence modelling of the atoms has two levels: atoms are concatenated to create segments and segments are concatenated, based on their history, to create textures. This approach deals with problems of repetition associated with sampling based sound texture synthesis techniques. Listening tests show that the results of the synthesis are very promising for a broad range of textures, including quasi-periodic and more random textures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1094–1105},
numpages = {12},
keywords = {synthesis, concatenative, audio texture, sound texture, granular}
}

@article{10.1109/TASLP.2016.2537203,
author = {Xu, Haotian and Ou, Zhijian},
title = {Scalable Discovery of Audio Fingerprint Motifs in Broadcast Streams with Determinantal Point Process Based Motif Clustering},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2537203},
doi = {10.1109/TASLP.2016.2537203},
abstract = {In this paper, we study the scalable discovery of audio repetitive patterns/motifs in long broadcast streams, where two segments are said to be repetitive if their audio fingerprints are close to each other. In this task, as we are confined to handle limited variability, we can adapt an audio hashing technique, originally proposed for searching a given music clip in music tracks, to successfully devise a linear complexity similarity matching method with a new step of repeated interval formation. This is the first contribution of this paper. As the similarity matching is super fast and thus coarse, there are false alarms in the large number of pair-wise matches generated, which constitute a major source of noise. We propose applying subset selection to the original set of pair-wise matches based on determinantal point processes (DPPs), as a filtering step, to reduce the noise. The selected subset of pair-wise matches is then subjected to motif clustering. We successfully apply DPP-based subset selection to improve motif clustering, which has a nice property that favors both quality and diversity. This is the second contribution of this paper. The proposed method is thoroughly evaluated on a 9-hour real-world audio stream and is compared with several reference methods. The bootstrap technique is used for the significance test. It is shown that the similarity matching is computationally very efficient (above 100 times faster than real time), and the filtering step with DPPs can significantly improve the precision of motif discovery, without sacrificing the recall performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {978–989},
numpages = {12},
keywords = {audio fingerprinting, motif clustering, determinantal point process, audio motif discovery}
}

@article{10.1109/TASLP.2016.2520364,
author = {Receveur, Simon and Wei\ss{}, Robin and Fingscheidt, Tim},
title = {Turbo Automatic Speech Recognition},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2520364},
doi = {10.1109/TASLP.2016.2520364},
abstract = {Performance of automatic speech recognition (ASR) systems can significantly be improved by integrating further sources of information such as additional modalities, or acoustic channels, or acoustic models. Given the arising problem of information fusion, striking parallels to problems in digital communications are exhibited, where the discovery of the turbo codes by Berrou et al. was a groundbreaking innovation. In this paper, we show ways how to successfully apply the turbo principle to the domain of ASR and thereby provide solutions to the above-mentioned information fusion problem. The contribution of our work is fourfold: First, we review the turbo decoding forward-backward algorithm (FBA), giving detailed insights into turbo ASR, and providing a new interpretation and formulation of the so-called extrinsic information being passed between the recognizers. Second, we present a real-time capable turbo-decoding Viterbi algorithm suitable for practical information fusion and recognition tasks. Then we present simulation results for a multimodal example of information fusion. Finally, we prove the suitability of both our turbo FBA and turbo Viterbi algorithm also for a single-channel multimodel recognition task obtained by using two acoustic feature extraction methods. On a small vocabulary task (challenging, since spelling is included), our proposed turbo ASR approach outperforms even the best reference system on average over all SNR conditions and investigated noise types by a relative word error rate (WER) reduction of 22.4% (audio-visual task) and 18.2% (audio-only task), respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {846–862},
numpages = {17},
keywords = {multimedia systems, speech recognition, hidden Markov models, robustness, iterative decoding}
}

@article{10.1109/TASLP.2016.2531285,
author = {Zhang, Geliang and Godsill, Simon},
title = {Fundamental Frequency Estimation in Speech Signals with Variable Rate Particle Filters},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2531285},
doi = {10.1109/TASLP.2016.2531285},
abstract = {Fundamental frequency estimation, known as pitch estimation in speech signals is of interest both to the research community and to industry. Meanwhile, the particle filter is known to be a powerful Bayesian inference method to track dynamic parameters in nonlinear state-space models. In this paper, we propose a speech model under a time-varying source-filter speech model, and use variable rate particle filters (VRPF) to develop methods for estimation of pitch periods in speech signals. A Rao--Blackwellised variable rate particle filter (RBVRPF) is also implemented. The proposed VRPF and RBVRPF are compared with a state-of-the-art pitch estimation algorithm, the YIN algorithm. Simulation results show that more accurate estimation of pitch can be obtained by VRPF and RBVRPF even under strong background noise conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {890–900},
numpages = {11},
keywords = {rao--blackwellisation, pitch estimation, variable rate particle filters, source-filter model}
}

@article{10.1109/TASLP.2016.2530409,
author = {Marxer, Ricard and Purwins, Hendrik},
title = {Unsupervised Incremental Online Learning and Prediction of Musical Audio Signals},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2530409},
doi = {10.1109/TASLP.2016.2530409},
abstract = {Guided by the idea that musical human-computer interaction may become more effective, intuitive, and creative when basing its computer part on cognitively more plausible learning principles, we employ unsupervised incremental online learning (i.e. clustering) to build a system that predicts the next event in a musical sequence, given as audio input. The flow of the system is as follows: 1) segmentation by onset detection, 2) timbre representation of each segment by Mel frequency cepstrum coefficients, 3) discretization by incremental clustering, yielding a tree of different sound classes (e.g. timbre categories/instruments) that can grow or shrink on the fly driven by the instantaneous sound events, resulting in a discrete symbol sequence, 4) extraction of statistical regularities of the symbol sequence, using hierarchical N-grams and the newly introduced conceptual Boltzmann machine that adapt to the dynamically changing clustering tree in 3), and 5) prediction of the next sound event in the sequence, given the last n previous events. The system's robustness is assessed with respect to complexity and noisiness of the signal. Clustering in isolation yields an adjusted Rand index (ARI) of 82.7%/85.7% for data sets of singing voice and drums. Onset detection jointly with clustering achieve an ARI of 81.3%/76.3% and the prediction of the entire system yields an ARI of 27.2%/39.2%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {863–874},
numpages = {12},
keywords = {adaptive algorithms, prediction algorithms, music information retrieval, unsupervised learning}
}

@article{10.1109/TASLP.2016.2536478,
author = {Zhang, Xiao-Lei and Wang, DeLiang},
title = {A Deep Ensemble Learning Method for Monaural Speech Separation},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2536478},
doi = {10.1109/TASLP.2016.2536478},
abstract = {Monaural speech separation is a fundamental problem in robust speech processing. Recently, deep neural network (DNN)-based speech separation methods, which predict either clean speech or an ideal time-frequency mask, have demonstrated remarkable performance improvement. However, a single DNN with a given window length does not leverage contextual information sufficiently, and the differences between the two optimization objectives are not well understood. In this paper, we propose a deep ensemble method, named multicontext networks, to address monaural speech separation. The first multicontext network averages the outputs of multiple DNNs whose inputs employ different window lengths. The second multicontext network is a stack of multiple DNNs. Each DNN in a module of the stack takes the concatenation of original acoustic features and expansion of the soft output of the lower module as its input, and predicts the ratio mask of the target speaker; the DNNs in the same module employ different contexts. We have conducted extensive experiments with three speech corpora. The results demonstrate the effectiveness of the proposed method. We have also compared the two optimization objectives systematically and found that predicting the ideal time-frequency mask is more efficient in utilizing clean training speech, while predicting clean speech is less sensitive to SNR variations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {967–977},
numpages = {11},
keywords = {monaural speech separation, ensemble learning, multicontext networks, mapping-based separation, masking-based separation, deep neural networks}
}

@article{10.1109/TASLP.2016.2531284,
author = {Kroher, Nadine and G\'{o}mez, Emilia},
title = {Automatic Transcription of Flamenco Singing from Polyphonic Music Recordings},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2531284},
doi = {10.1109/TASLP.2016.2531284},
abstract

license_type: permissive

branch_name: refs/heads/main

extension: bib