text:
101},
numpages = {12}
}

@article{10.1109/TASLP.2016.2614351,
author = {Papadopoulos, Helene and Tzanetakis, George and Papadopoulos, Helene and Tzanetakis, George and Tzanetakis, George and Papadopoulos, Helene},
title = {Models for Music Analysis From a Markov Logic Networks Perspective},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2614351},
doi = {10.1109/TASLP.2016.2614351},
abstract = {Analyzing and formalizing the intricate mechanisms of music is a very challenging goal for Artificial Intelligence. Dealing with real audio recordings requires the ability to handle both uncertainty and complex relational structure at multiple levels of representation. Until now, these two aspects have been generally treated separately, probability being the standard way to represent uncertainty in knowledge, while logical representation being the standard way to represent knowledge and complex relational information. Several approaches attempting a unification of logic and probability have recently been proposed. In particular, Markov logic networks MLNs, which combine first-order logic and probabilistic graphical models, have attracted increasing attention in recent years in many domains. This paper introduces MLNs as a highly flexible and expressive formalism for the analysis of music that encompasses most of the commonly used probabilistic and logic-based models. We first review and discuss existing approaches for music analysis. We then introduce MLNs in the context of music signal processing by providing a deep understanding of how they specifically relate to traditional models, specifically hidden Markov models and conditional random fields. We then present a detailed application of MLNs for tonal harmony music analysis that illustrates the potential of this framework for music processing.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {19–34},
numpages = {16}
}

@article{10.1109/TASLP.2016.2627186,
author = {Abeber, Jakob and Frieler, Klaus and Cano, Estefania and Pfleiderer, Martin and Zaddach, Wolf-Georg and Abesser, Jakob and Frieler, Klaus and Cano, Estefan\'{\i}a and Pfleiderer, Martin and Zaddach, Wolf-Georg},
title = {Score-Informed Analysis of Tuning, Intonation, Pitch Modulation, and Dynamics in Jazz Solos},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2627186},
doi = {10.1109/TASLP.2016.2627186},
abstract = {Both the collection and analysis of large music repertoires constitute major challenges within musicological disciplines such as jazz research. Automatic methods of music analysis based on audio signal processing have the potential to assist researchers and to accelerate the transcription and analysis of music recordings significantly. In this paper, we propose a framework for analyzing improvised monophonic solos in multi-instrumental jazz recordings with special focus on reed and brass instruments. The analysis algorithms rely on prior score-information, which is taken from high quality manual solo transcriptions. Following an initial solo and accompaniment source separation, we propose algorithms for tone-wise extraction of fundamental frequency and intensity contours. Based on this fine-grained representation of recorded jazz solos, we perform several exploratory experiments motivated by questions relating to jazz research in order to analyze the use of expressive stylistic devices such as intonation, pitch modulation, and dynamics in jazz solos. The results show that a score-informed audio analysis of jazz recordings can provide valuable insights into the individual stylistic characteristics of jazz musicians.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {168–177},
numpages = {10}
}

@article{10.1109/TASLP.2016.2621675,
author = {Li, Kun and Qian, Xiaojun and Meng, Helen and Kun Li and Xiaojun Qian and Meng, Helen},
title = {Mispronunciation Detection and Diagnosis in L2 English Speech Using Multidistribution Deep Neural Networks},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2621675},
doi = {10.1109/TASLP.2016.2621675},
abstract = {This paper investigates the use of multidistribution deep neural networks DNNs for mispronunciation detection and diagnosis MDD, to circumvent the difficulties encountered in an existing approach based on extended recognition networks ERNs. The ERNs leverage existing automatic speech recognition technology by constraining the search space via including the likely phonetic error patterns of the target words in addition to the canonical transcriptions. MDDs are achieved by comparing the recognized transcriptions with the canonical ones. Although this approach performs reasonably well, it has the following issues: 1 Learning the error patterns of the target words to generate the ERNs remains a challenging task. Phones or phone errors missing from the ERNs cannot be recognized even if we have well-trained acoustic models; and 2 acoustic models and phonological rules are trained independently, and hence, contextual information is lost. To address these issues, we propose an acoustic-graphemic-phonemic model AGPM using a multidistribution DNN, whose input features include acoustic features, as well as corresponding graphemes and canonical transcriptions encoded as binary vectors. The AGPM can implicitly model both grapheme-to-likely-pronunciation and phoneme-to-likely-pronunciation conversions, which are integrated into acoustic modeling. With the AGPM, we develop a unified MDD framework, which works much like free-phone recognition. Experiments show that our method achieves a phone error rate PER of 11.1%. The false rejection rate FRR, false acceptance rate FAR, and diagnostic error rate DER for MDD are 4.6%, 30.5%, and 13.5%, respectively. It outperforms the ERN approach using DNNs as acoustic models, whose PER, FRR, FAR, and DER are 16.8%, 11.0%, 43.6%, and 32.3%, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {193–207},
numpages = {15}
}

@article{10.1109/TASLP.2016.2613280,
author = {Moore, Alastair H. and Evers, Christine and Naylor, Patrick A. and Moore, Alastair H. and Evers, Christine and Naylor, Patrick A.},
title = {Direction of Arrival Estimation in the Spherical Harmonic Domain Using Subspace Pseudointensity Vectors},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2613280},
doi = {10.1109/TASLP.2016.2613280},
abstract = {Direction of arrival DOA estimation is a fundamental problem in acoustic signal processing. It is used in a diverse range of applications, including spatial filtering, speech dereverberation, source separation and diarization. Intensity vector-based DOA estimation is attractive, especially for spherical sensor arrays, because it is computationally efficient. Two such methods are presented that operate on a spherical harmonic decomposition of a sound field observed using a spherical microphone array. The first uses pseudointensity vectors PIVs and works well in acoustic environments where only one sound source is active at any time. The second uses subspace pseudointensity vectors SSPIVs and is targeted at environments where multiple simultaneous soures and significant levels of reverberation make the problem more challenging. Analytical models are used to quantify the effects of an interfering source, diffuse noise, and sensor noise on PIVs and SSPIVs. The accuracy of DOA estimation using PIVs and SSPIVs is compared against the state of the art in simulations including realistic reverberation and noise for single and multiple, stationary and moving sources. Finally, robust performance of the proposed methods is demonstrated by using speech recordings in a real acoustic environment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {178–192},
numpages = {15}
}

@article{10.1109/TASLP.2016.2628642,
author = {Koutrouvelis, Andreas I. and Hendriks, Richard Christian and Heusdens, Richard and Jensen, Jesper and Koutrouvelis, Andreas I. and Hendriks, Richard Christian and Heusdens, Richard and Jensen, Jesper},
title = {Relaxed Binaural LCMV Beamforming},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2628642},
doi = {10.1109/TASLP.2016.2628642},
abstract = {In this paper, we propose a new binaural beamforming technique, which can be seen as a relaxation of the linearly constrained minimum variance LCMV framework. The proposed method can achieve simultaneous noise reduction and exact binaural cue preservation of the target source, similar to the binaural minimum variance distortionless response BMVDR method. However, unlike BMVDR, the proposed method is also able to preserve the binaural cues of multiple interferers to a certain predefined accuracy. Specifically, it is able to control the trade-off between noise reduction and binaural cue preservation of the interferers by using a separate trade-off parameter per-interferer. Moreover, we provide a robust way of selecting these trade-off parameters in such a way that the preservation accuracy for the binaural cues of the interferers is always better than the corresponding ones of the BMVDR. The relaxation of the constraints in the proposed method achieves approximate binaural cue preservation of more interferers than other previously presented LCMV-based binaural beamforming methods that use strict equality constraints.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {137–152},
numpages = {16}
}

@article{10.1109/TASLP.2016.2614725,
author = {Wu, Jin Chu and Martin, Alvin F. and Greenberg, Craig S. and Kacker, Raghu N. and Jin Chu Wu and Martin, Alvin F. and Greenberg, Craig S. and Kacker, Raghu N. and Greenberg, Craig S. and Wu, Jin Chu and Martin, Alvin F. and Kacker, Raghu N.},
title = {The Impact of Data Dependence on Speaker Recognition Evaluation},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2614725},
doi = {10.1109/TASLP.2016.2614725},
abstract = {The data dependence due to multiple use of the same subjects has impact on the standard error SE of the detection cost function DCF in speaker recognition evaluation. The DCF is defined as a weighted sum of the probabilities of type I and type II errors at a given threshold. A two-layer data structure is constructed: Target scores are grouped into target sets based on the dependence, and likewise for non-target scores. On account of the needed equal probabilities for scores being selected when resampling, target sets must contain the same number of target scores, and so must non-target sets. In addition to the bootstrap method with i.i.d. assumption, the nonparametric two-sample one-layer and two-layer bootstrap methods are carried out based on whether the resampling takes place only on sets, or subsequently on scores within the sets. Due to the stochastic nature of the bootstrap, the distributions of the SEs of the DCF estimated using the three different bootstrap methods are created and compared. After performing hypothesis testing, it is found that data dependence increases not only the SE but also the variation of the SE, and the two-layer bootstrap is more conservative than the one-layer bootstrap. The rationale regarding the different impacts of the three bootstrap methods on the estimated SEs is investigated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {5–18},
numpages = {14}
}

@article{10.1109/TASLP.2016.2618007,
author = {Chazan, Shlomo E. and Goldberger, Jacob and Gannot, Sharon and Chazan, Shlomo E. and Goldberger, Jacob and Gannot, Sharon},
title = {A Hybrid Approach for Speech Enhancement Using MoG Model and Neural Network Phoneme Classifier},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2618007},
doi = {10.1109/TASLP.2016.2618007},
abstract = {In this paper, we present a single-microphone speech enhancement algorithm. A hybrid approach is proposed merging the generative mixture of Gaussians MoG model and the discriminative deep neural network DNN. The proposed algorithm is executed in two phases, the training phase, which does not recur, and the test phase. First, the noise-free speech log-power spectral density is modeled as an MoG, representing the phoneme-based diversity in the speech signal. A DNN is then trained with phoneme labeled database of clean speech signals for phoneme classification with mel-frequency cepstral coefficients as the input features. In the test phase, a noisy utterance of an untrained speech is processed. Given the phoneme classification results of the noisy speech utterance, a speech presence probability SPP is obtained using both the generative and discriminative models. SPP-controlled attenuation is then applied to the noisy speech while simultaneously, the noise estimate is updated. The discriminative DNN maintains the continuity of the speech and the generative phoneme-based MoG preserves the speech spectral structure. Extensive experimental study using real speech and noise signals is provided. We also compare the proposed algorithm with alternative speech enhancement algorithms. We show that we obtain a significant improvement over previous methods in terms of speech quality measures. Finally, we analyze the contribution of all components of the proposed algorithm indicating their combined importance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2516–2530},
numpages = {15}
}

@article{10.1109/TASLP.2016.2615239,
author = {Moritz, Niko and Kollmeier, Birger and Anemuller, Jorn and Moritz, Niko and Kollmeier, Birger and Anemuller, Jorn},
title = {Integration of Optimized Modulation Filter Sets Into Deep Neural Networks for Automatic Speech Recognition},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2615239},
doi = {10.1109/TASLP.2016.2615239},
abstract = {Inspired by physiological studies on the human auditory system and by results from psychoacoustics, an amplitude modulation filter bank AMFB has been developed and successfully applied to feature extraction for automatic speech recognition ASR in earlier work. Here, we address the question as to which amplitude modulation AM frequency decomposition leads to optimal ASR performance by proposing a parameterized functional relationship between modulation center frequency and modulation bandwidth. Word error rates WERs of ASR experiments with 1551 different AMFBs are systematically evaluated and compared, resulting in the identification of a comparatively narrow range of optimal modulation frequency to modulation bandwidth characteristics. To integrate modulation processing with deep neural network DNN acoustic modeling, we propose merging of modulation filter coefficients with DNN weights prior to a final training step and an improved mean-variance normalization scheme for AMFBs. These modifications are shown to result in further reduction of WERs and are indicative of the proposed system's improved generalization ability, when compared across corpora of 100-960 h of data with mismatched training and test conditions. Analysis of DNN-learned temporal AM filtering properties is carried out and implications for the relevance of different modulation regions as well as the relation to psychoacoustic findings are discussed. ASR experiments with the proposed system demonstrate a high degree of robustness against extrinsic acoustic distortions, resulting in, e.g., an average WER of 9.79% on the Aurora-4 task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2439–2452},
numpages = {14}
}

@article{10.1109/TASLP.2016.2602549,
author = {Krawczyk-Becker, Martin and Gerkmann, Timo and Krawczyk-Becker, Martin and Gerkmann, Timo},
title = {On MMSE-Based Estimation of Amplitude and Complex Speech Spectral Coefficients Under Phase-Uncertainty},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2602549},
doi = {10.1109/TASLP.2016.2602549},
abstract = {Among the most commonly used single-channel approaches for the enhancement of noise corrupted speech are Bayesian estimators of clean speech coefficients in the short-time Fourier transform domain. However, the vast majority of these approaches effectively only modifies the spectral amplitude and does not consider any information about the clean speech spectral phase. More recently, clean speech estimators that can utilize prior phase information have been proposed and shown to lead to improvements over the traditional, phase-blind approaches. In this work, we revisit phase-aware estimators of clean speech amplitudes and complex coefficients. To complete the existing set of estimators, we first derive a novel amplitude estimator given uncertain prior phase information. Second, we derive a closed-form solution for complex coefficients when the prior phase information is completely uncertain or not available. We put the novel estimators into the context of existing estimators and discuss their advantages and disadvantages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2251–2262},
numpages = {12}
}

@article{10.1109/TASLP.2016.2604212,
author = {Liu, Jinxin and Chen, Xuefeng and Jinxin Liu and Xuefeng Chen},
title = {Adaptive Compensation of Misequalization in Narrowband Active Noise Equalizer Systems},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2604212},
doi = {10.1109/TASLP.2016.2604212},
abstract = {With the development of active noise control, some residual noise reshaping other than cancelling demands have arisen in fields like active sound quality control and psychoacoustics. Active noise equalizer ANE is one of the most popular methods of residual noise reshaping. However, the problem of misequalization, i.e., the sensitivity to secondary path modeling errors, restricts its applications. In this paper, an enhanced ANE EANE algorithm is proposed to compensate for the misequalization problem. Firstly, the misequalization phenomenon is analytically investigated. Next, EANE, which can adaptively tune the equalization parameters is proposed according to the analytical results. Multiple-harmonic EANE is also studied. Finally, numerical simulations are carried out, in which the results show that EANE can perform a more accurate residual amplitude control at the reference frequencies than traditional ANE system when there are secondary path modeling errors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2390–2399},
numpages = {10}
}

@article{10.1109/TASLP.2016.2602546,
author = {Barker, Tom and Virtanen, Tuomas and Barker, Tom and Virtanen, Tuomas},
title = {Blind Separation of Audio Mixtures Through Nonnegative Tensor Factorization of Modulation Spectrograms},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2602546},
doi = {10.1109/TASLP.2016.2602546},
abstract = {This paper presents an algorithm for unsupervised single-channel source separation of audio mixtures. The approach specifically addresses the challenging case of separation where no training data are available. By representing mixtures in the modulation spectrogram MS domain, we exploit underlying similarities in patterns present across frequency. A three-dimensional tensor factorization is able to take advantage of these redundant patterns, and is used to separate a mixture into an approximated sum of components by minimizing a divergence cost. Furthermore, we show that the basic tensor factorization can be extended with convolution in time being used to improve separation results and provide update rules to learn components in such a manner. Following factorization, sources are reconstructed in the audio domain from estimated components using a novel approach based on reconstruction masks that are learned using MS activations, and then applied to a mixture spectrogram. We demonstrate that the proposed method produces superior separation performance to a spectrally based nonnegative matrix factorization approach, in terms of source-to-distortion ratio. We also compare separation with the perceptually motivated interference-related perceptual score metric and identify cases with higher performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2377–2389},
numpages = {13}
}

@article{10.1109/TASLP.2016.2607343,
author = {Villalba, Jesus and Miguel, Antonio and Ortega, Alfonso and Lleida, Eduardo and Villalba, Jesus and Miguel, Antonio and Ortega, Alfonso and Lleida, Eduardo},
title = {Bayesian Networks to Model the Variability of Speaker Verification Scores in Adverse Environments},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2607343},
doi = {10.1109/TASLP.2016.2607343},
abstract = {State-of-the-art speaker recognition technology attains great performance in controlled conditions. However, when the speech segments suffer distortions like noise or reverberation performance can severely deteriorate, this fact motivated us to investigate how score distributions diverge from the ideal ones in degraded conditions. We propose a Bayesian network model that assumes that two scores exist: one observed and another one hidden. The observed score or noisy score is the one given by the speaker verification system. Meanwhile, the hidden score or clean score is the ideal score that we would obtain in a trial with high-quality speech. A set of quality measures helps to relate both scores. We applied this network to two tasks. The first one consists in rejecting unreliable trials, i.e., trials that we cannot assure whether they are target or nontarget. We prove that this method outperforms previous approaches, based on another type of Bayesian networks. The second task is to compute an improved likelihood ratio, dependent on the quality measures. This ratio improved calibration in noisy conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2327–2340},
numpages = {14}
}

@article{10.1109/TASLP.2016.2602884,
author = {Qian, Yanmin and Bi, Mengxiao and Tan, Tian and Yu, Kai and Yanmin Qian and Mengxiao Bi and Tian Tan and Kai Yu},
title = {Very Deep Convolutional Neural Networks for Noise Robust Speech Recognition},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2602884},
doi = {10.1109/TASLP.2016.2602884},
abstract = {Although great progress has been made in automatic speech recognition, significant performance degradation still exists in noisy environments. Recently, very deep convolutional neural networks CNNs have been successfully applied to computer vision and speech recognition tasks. Based on our previous work on very deep CNNs, in this paper this architecture is further developed to improve recognition accuracy for noise robust speech recognition. In the proposed very deep CNN architecture, we study the best configuration for the sizes of filters, pooling, and input feature maps: the sizes of filters and poolings are reduced and dimensions of input features are extended to allow for adding more convolutional layers. Then the appropriate pooling, padding, and input feature map selection strategies are investigated and applied to the very deep CNN to make it more robust for speech recognition. In addition, an in-depth analysis of the architecture reveals key characteristics, such as compact model scale, fast convergence speed, and noise robustness. The proposed new model is evaluated on two tasks: Aurora4 task with multiple additive noise types and channel mismatch, and the AMI meeting transcription task with significant reverberation. Experiments on both tasks show that the proposed very deep CNNs can significantly reduce word error rate WER for noise robust speech recognition. The best architecture obtains a 10.0% relative reduction over the traditional CNN on AMI, competitive with the long short-term memory recurrent neural networks LSTM-RNN acoustic model. On Aurora4, even without feature enhancement, model adaptation, and sequence training, it achieves a WER of 8.81%, a 17.0% relative improvement over the LSTM-RNN. To our knowledge, this is the best published result on Aurora4.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2263–2276},
numpages = {14}
}

@article{10.1109/TASLP.2016.2615238,
author = {Tervo, Sakari and Tervo, Sakari},
title = {Single Snapshot Detection and Estimation of Reflections From Room Impulse Responses in the Spherical Harmonic Domain},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2615238},
doi = {10.1109/TASLP.2016.2615238},
abstract = {We study the detection and estimation of the parameters related to the deterministic model of the reflections of room impulse responses measured with a spherical microphone array. More specifically, we investigate the problem of detecting and estimating several reflections of a single snapshot of data in the spherical harmonic SH domain with four detection and four estimation methods, presented previously in the array processing research. Three of the estimation methods are based on Bayesian Maximum a Posteriori MAP estimation, and they employ a prior normal distribution on the signals. The estimation methods are compared with the deterministic maximum likelihood DML method [Tervo & Politis 2015]. In the detection task, two information criteria-based methods, minimum description length MDL and Akaike information criteria AIC, a normalized likelihood NL based method, and a Bayesian detection scheme BDS are explored. The experiments study the performance of the methods with simulated and real data experiments. The simulation results show that the MAP estimation methods have a lower root mean squared error RMSE than DML in the reflection signal amplitude estimation, but all three have similar performance in the direction of arrival DOA and noise variance estimation, although in general RMSE of DOA estimation of MAP methods is slightly lower that that of DML. In addition, in the detection task, the BDS and AIC are the most robust against additive noise, and NL and AIC have the best detection rate when more than two reflections are simulated. The results of the real data experiments show that all the estimation methods have similar performance for DOA and noise variance estimation, while the Bayesian MAP methods have a clearly lower RMSE for reflection signal amplitude estimation than DML. In total, NL has the highest detection rate in the real data experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2466–2480},
numpages = {15}
}

@article{10.1109/TASLP.2016.2615242,
author = {Markovic, Dejan and Antonacci, Fabio and Bianchi, Lucio and Tubaro, Stefano and Sarti, Augusto and Markovic, Dejan and Antonacci, Fabio and Bianchi, Lucio and Tubaro, Stefano and Sarti, Augusto},
title = {Extraction of Acoustic Sources Through the Processing of Sound Field Maps in the Ray Space},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2615242},
doi = {10.1109/TASLP.2016.2615242},
abstract = {Our goal is to develop a model-based approach to acoustic source extraction from microphone array data, which is suitable for both near-field and far-field sources. A signal representation based on plane-wave PW decomposition is suitable for acoustic sources in the far field as the resulting spectrum turns out to be impulsive. When the source approaches the array, however, the curvature of the wavefront causes the spectrum of the PW components to depart from impulsive behavior, thus making source extraction harder to attain. In this paper, we adopt a sound field representation based on the local estimation of the plenacoustic function along the array line. This approach consists of dividing the array into subarrays, and applying the PW analysis on individual subarrays. This has the immediate result of extending the range of validity of the far-field hypothesis, as a source that enters the near-field range of the extended array is still in the far-field range of the subarrays. PW analysis on subarrays allows us to construct the so-called sound field map in a domain of acoustic visibility called ray space. The extraction of the desired source is accomplished through spatial filtering of the sound field map. The design of the spatial filter relies on a linear minimum mean square error criterion defined on the sound field map. The effectiveness of the proposed methodology is proven through an extensive simulation campaign as well as real experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2481–2494},
numpages = {14}
}

@article{10.1109/TASLP.2016.2614140,
author = {Leglaive, Simon and Badeau, Roland and Richard, Gael and Leglaive, Simon and Badeau, Roland and Richard, Gael},
title = {Multichannel Audio Source Separation With Probabilistic Reverberation Priors},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2614140},
doi = {10.1109/TASLP.2016.2614140},
abstract = {Incorporating prior knowledge about the sources and/or the mixture is a way to improve under-determined audio source separation performance. A great number of informed source separation techniques concentrate on taking priors on the sources into account, but fewer works have focused on constraining the mixing model. In this paper, we address the problem of underdetermined multichannel audio source separation in reverberant conditions. We target a semi-informed scenario where some room parameters are known. Two probabilistic priors on the frequency response of the mixing filters are proposed. Early reverberation is characterized by an autoregressive model while according to statistical room acoustics results, late reverberation is represented by an autoregressive moving average model. Both reverberation models are defined in the frequency domain. They aim to transcribe the temporal characteristics of the mixing filters into frequency-domain correlations. Our approach leads to a maximum a posteriori estimation of the mixing filters which is achieved thanks to the expectation-maximization algorithm. We experimentally show the superiority of this approach compared with a maximum likelihood estimation of the mixing filters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2453–2465},
numpages = {13}
}

@article{10.1109/TASLP.2016.2607339,
author = {Movassagh, Mahmood and Kabal, Peter and Movassagh, Mahmood and Kabal, Peter},
title = {Scalable Audio Coding Using Trellis-Based Optimized Joint Entropy Coding and Quantization},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2607339},
doi = {10.1109/TASLP.2016.2607339},
abstract = {There is a considerable performance gap between the current scalable audio coding schemes and a nonscalable coder operating at the same bitrate. This suboptimality results from the independent coding of the layers in these systems. One of the aspects that plays a role in this suboptimality is the entropy coding. In practical audio coding systems including MPEG advanced audio coding AAC, the transform domain coefficients are quantized using an entropy-constrained quantizer. In MPEG-4 scalable AAC S-AAC, the quantization and coding are performed separately at each layer. In case of Huffman coding, the redundancy introduced by the entropy coding at each layer is larger at lower quantization resolutions. Also, the redundancy for the overall coder becomes larger as the number of layers increases. In fact, there is a tradeoff between the overall redundancy and the fine-grain scalability in which the bitrate per layer is smaller and more layers are required. In this paper, a fine-grain scalable coder for audio signals is proposed where the entropy coding of a quantizer is made scalable via joint design of entropy coding and quantization. By constructing a Huffman-like coding tree where the internal nodes can be mapped to the reconstruction points, the tree can be pruned at any internal node to control the rate-distortion RD performance of the encoder in a fine-grain manner. A set of metrics and a trellis-based approach is proposed to create a coding tree so that an appropriate path is generated on the RD plane. The results show the proposed method outperforms the scalable audio coding performed based on reconstruction error quantization as used in practical systems, e.g., in S-AAC.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2288–2300},
numpages = {13}
}

@article{10.1109/TASLP.2016.2556280,
author = {Zhang, Sheng and Zhang, Jiashu and Han, Hongyu and Sheng Zhang and Jiashu Zhang and Hongyu Han},
title = {Robust Variable Step-Size Decorrelation Normalized Least-Mean-Square Algorithm and Its Application to Acoustic Echo Cancellation},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2556280},
doi = {10.1109/TASLP.2016.2556280},
abstract = {In this paper, we present a robust variable step-size decorrelation normalized least-mean-square RVSSDNLMS algorithm. A new constrained minimization problem is developed by minimizing the l2 norm of the a decorrelated posteriori error signal with a constraint on the filter coefficients in the l2 norm sense. Solving this minimization problem gives birth to the efficient RVSSDNLMS algorithm. The convergence performance and computational complexity of RVSSDNLMS algorithm are analyzed. Finally, simulations show that the proposed RVSSDNLMS considerably outperforms the normalized least-mean-square NLMS, robust variable step-size NLMS, and pseudoaffine projection algorithms in terms of convergence rate and steady-state error in Gaussian noise and impulsive noise environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2368–2376},
numpages = {9}
}

@article{10.1109/TASLP.2016.2603006,
author = {Wu, Yi-Chan and Chen, Homer H. and Yi-Chan Wu and Chen, Homer H.},
title = {Generation of Affective Accompaniment in Accordance With Emotion Flow},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2603006},
doi = {10.1109/TASLP.2016.2603006},
abstract = {The emotion expressed by a music piece varies as the music unrolls in time. To create such dynamic expression, we develop an algorithm that automatically generates the accompaniment for a melody according to the emotion flow specified by a user. The emotion flow is given in the form of arousal and valence curves, each as a function of time. The affective accompaniment is composed of chord progression and accompaniment pattern. The chord progression, which controls the valence of the composed music, is generated by dynamic programming using the input melody and valence data as constraints. A mathematical model is developed to describe the temporal relationship between valence and chord progression. The accompaniment pattern, which controls the arousal of the composed music, is determined according to the quantized arousal values. The performance of the system is evaluated by subjective tests. The cross-correlation coefficient between the input arousal valence and the perceived arousal valence of the composed music is 0.85 0.52. It rises to 0.92 for arousal and 0.88 for valence if only musician subjects in the test are considered. Overall, the proposed system is capable of generating subjectively appropriate accompaniments conforming to the user specification.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2277–2287},
numpages = {11}
}

@article{10.1109/TASLP.2016.2616543,
author = {Titze, Ingo R. and Palaparthi, Anil and Titze, Ingo R. and Palaparthi, Anil},
title = {Sensitivity of Source–Filter Interaction to Specific Vocal Tract Shapes},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2616543},
doi = {10.1109/TASLP.2016.2616543},
abstract = {A systematic variation of length and cross-sectional area of specific segments of the vocal tract trachea to lips was conducted computationally to quantify the effects of source-filter interaction. A one-dimensional Navier-Stokes transmission line solution was used to compute peak glottal airflow, maximum flow declination rate, and formant ripple on glottal flow for Level 1 aero-acoustic interactions. For Level 2 tissue movement interaction, peak glottal area, phonation threshold pressure, and deviation in fo were quantified. Results show that the ventricle, the false-fold glottis, the conus elasticus entry, and the laryngeal vestibule are the regions to which acoustic variables are most sensitive. Generally, any narrow section of the vocal tract increases the degree of interaction, both in terms of its length and its cross-sectional area. The closer the narrow section is to the vocal folds, the greater the effect.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2507–2515},
numpages = {9}
}

@article{10.1109/TASLP.2016.2615240,
author = {Papadopoulos, Pavlos and Tsiartas, Andreas and Narayanan, Shrikanth and Papadopoulos, Pavlos and Tsiartas, Andreas and Narayanan, Shrikanth},
title = {Long-Term SNR Estimation of Speech Signals in Known and Unknown Channel Conditions},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2615240},
doi = {10.1109/TASLP.2016.2615240},
abstract = {Many speech processing algorithms and applications rely on the explicit knowledge of signal-to-noise ratio SNR in their design and implementation. Estimating the SNR of a signal can enhance the performance of such technologies. We propose a novel method for estimating the long-term SNR of speech signals based on features, from which we can approximately detect regions of speech presence in a noisy signal. By measuring the energy in these regions, we create sets of energy ratios, from which we train regression models for different types of noise. If the type of noise that corrupts a signal is known, we use the corresponding regression model to estimate the SNR. When the noise is unknown, we use a deep neural network to find the “closest” regression model to estimate the SNR. Evaluations were done based on the TIMIT speech corpus, using noises from the NOISEX-92 noise database. Furthermore, we performed cross-corpora experiments by training on TIMIT and NOISEX-92 and testing on the Wall Street Journal speech corpus and DEMAND noise database. Our results show that our system provides accurate SNR estimations across different noise types, corpora, and that it outperforms other SNR estimation methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2495–2506},
numpages = {12}
}

@article{10.1109/TASLP.2016.2611938,
author = {Li, Bochen and Duan, Zhiyao and Bochen Li and Zhiyao Duan},
title = {An Approach to Score Following for Piano Performances With the Sustained Effect},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2611938},
doi = {10.1109/TASLP.2016.2611938},
abstract = {One challenge in score following for piano music is the sustained effect, i.e., the waveform of a note lasts longer than what is notated in the score. This can be caused by expressive performing styles such as the legato articulation and the usage of the sustain and the sostenuto pedals and can also be caused by the reverberation in the recording environment. This effect creates nonnotated overlappings between sustained notes and latter notes in the audio. It decreases the audio-score alignment accuracy and robustness of score following systems and makes them be prone to delay errors, i.e., aligning audio to a score position that is earlier than the correct position. In this paper, we propose to modify the feature representation of the audio to attenuate the sustained effect. We show that this idea can be applied to both the chromagram and the spectral-peak representations, which are commonly used in score following systems. Experiments on the MAPS dataset show that the proposed method significantly improves the alignment accuracy and robustness of score following systems for piano performances, in both anechoic and highly reverberant environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2425–2438},
numpages = {14}
}

@article{10.1109/TASLP.2016.2608948,
author = {Norholm, Sidsel Marie and Jensen, Jesper Rindom and Christensen, Mads Graesboll and Norholm, Sidsel Marie and Jensen, Jesper Rindom and Christensen, Mads Graesboll},
title = {Instantaneous Fundamental Frequency Estimation With Optimal Segmentation for Nonstationary Voiced Speech},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2608948},
doi = {10.1109/TASLP.2016.2608948},
abstract = {In speech processing, the speech is often considered stationary within segments of 20-30 ms even though it is well known not to be true. In this paper, we take the nonstationarity of voiced speech into account by using a linear chirp model to describe the speech signal. We propose a maximum likelihood estimator of the fundamental frequency and chirp rate of this model, and show that it reaches the Cramer-Rao lower bound. Since the speech varies over time, a fixed segment length is not optimal, and we propose making a segmentation of the signal based on the maximum a posteriori criterion. Using this segmentation method, the segments are on average longer for the chirp model compared to the traditional harmonic model. For the signal under test, the average segment length is 24.4 and 17.1 ms for the chirp model and traditional harmonic model, respectively. This suggests a better fit of the chirp model than the harmonic model to the speech signal. The methods are based on an assumption of white Gaussian noise, and, therefore, two prewhitening filters are also proposed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2354–2367},
numpages = {14}
}

@article{10.1109/TASLP.2016.2618003,
author = {Huang, Gongping and Benesty, Jacob and Chen, Jingdong and Gongping Huang and Benesty, Jacob and Jingdong Chen},
title = {Superdirective Beamforming Based on the Krylov Matrix},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2618003},
doi = {10.1109/TASLP.2016.2618003},
abstract = {Superdirective beamforming has attracted a significant amount of research interest in speech and audio applications, since it can maximize the directivity factor DF given an array geometry and, therefore, is efficient in dealing with signal acquisition in diffuse-like noise environments. However, this beamformer is very sensitive to sensor self-noise and mismatch among sensors, which considerably restricts its use in practical systems. This paper develops an approach to superdirective beamforming based on the Krylov matrix. We show that the columns of a proposed Krylov matrix, which span a chosen dimension of the whole space, are interesting beamformers; consequently, all different linear combinations of those columns lead to beamformers that have good properties. In particular, we develop the Krylov maximum white noise gain and Krylov maximum DF beamformers, which are obtained by maximizing the WNG and the DF, respectively. By properly choosing the dimension of the Krylov subspace, the developed beamformers that can make a compromise between reasonable values of the DF and white noise amplification. We also extend the basic idea to the design of the Krylov maximum front-to-back ratio, parametric superdirective, and parametric supercardioid beamformers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2531–2543},
numpages = {13}
}

@article{10.1109/TASLP.2016.2602542,
author = {Kelly, Finnian and Hansen, John H. L. and Kelly, Finnian and Hansen, John H. L.},
title = {Score-Aging Calibration for Speaker Verification},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2602542},
doi = {10.1109/TASLP.2016.2602542},
abstract = {The gradual changes that occur in the human voice due to aging create challenges for speaker verification. This study presents an approach to calibrating the output scores of a speaker verification system using the time interval between comparison samples as additional information. Several functions are proposed for the incorporation of this time information, which is viewed as aging information, in a conventional linear score calibration transformation. Experiments are presented on data with shortterm aging intervals ranging between 2 months and 3 years, and long-term aging intervals of up to 30 years. The aging calibration proposal is shown to offset the decreased discrimination and calibration performance for both shortand long-term intervals, and to extrapolate well to unseen aging intervals. Relative reductions in Cuℓℓr cost of log-likelihood ratio of 1-4% and 10-43% are obtained at shortand long-term intervals, respectively. Assuming that a system has knowledge of the time interval between samples under comparison, this approach represents a straightforward means of compensating for the detrimental impact of aging on speaker verification performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2414–2424},
numpages = {11}
}

@article{10.1109/TASLP.2016.2601146,
author = {Samarakoon, Lahiru and Sim, Khe Chai and Samarakoon, Lahiru and Khe Chai Sim},
title = {Factorized Hidden Layer Adaptation for Deep Neural Network Based Acoustic Modeling},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2601146},
doi = {10.1109/TASLP.2016.2601146},
abstract = {In this paper, we propose the factorized hidden layer FHL approach to adapt the deep neural network DNN acoustic models for automatic speech recognition ASR. FHL aims at modeling speaker dependent SD hidden layers by representing an SD affine transformation as a linear combination of bases. The combination weights are low-dimensional speaker parameters that can be initialized using speaker representations like i-vectors and then reliably refined in an unsupervised adaptation fashion. Therefore, our method provides an efficient way to perform both adaptive training and test-time adaptation. Experimental results have shown that the FHL adaptation improves the ASR performance significantly, compared to the standard DNN models, as well as other state-of-the-art DNN adaptation approaches, such as training with the speaker-normalized CMLLR features, speaker-aware training using i-vector and learning hidden unit contributions LHUC. For Aurora 4, FHL achieves 3.8% and 2.3% absolute improvements over the standard DNNs trained on the LDA + STC and CMLLR features, respectively. It also achieves 1.7% absolute performance improvement over a system that combines the i-vector adaptive training with LHUC adaptation. For the AMI dataset, FHL achieved 1.4% and 1.9% absolute improvements over the sequence-trained CMLLR baseline systems, for the IHM and SDM tasks, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2241–2250},
numpages = {10}
}

@article{10.1109/TASLP.2016.2603599,
author = {Ogawa, Atsunori and Hori, Takaaki and Nakamura, Atsushi and Ogawa, Atsunori and Hori, Takaaki and Nakamura, Atsushi},
title = {Estimating Speech Recognition Accuracy Based on Error Type Classification},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2603599},
doi = {10.1109/TASLP.2016.2603599},
abstract = {Methods for estimating the speech recognition accuracy without using manually transcribed references are beneficial to the research and development of automatic speech recognition technology. This paper proposes recognition accuracy estimation methods based on error type classification ETC. ETC is an extension of confidence estimation. In ETC, each word in the recognition results recognized word sequences for the target speech data is probabilistically classified into three categories: the correct recognition C, substitution error S, and insertion error I. Deletion errors D that can occur at interword positions in the recognition results are also probabilistically detected. By summing these CSID probabilities individually, the numbers of CSIDs and, as a result, the two standard recognition accuracy measures, i.e., the percent correct and word accuracy WAcc, for the speech data can be estimated without using the reference transcriptions. Two recognition accuracy estimation methods based on ETC are proposed. In the first easy-to-use method, ETC is performed by converting the recognition results represented as word confusion networks into word alignment networks WANs. In the second and more accurate method, the WAN-based ETC results are refined with conditional random fields CRFs using various types of additional features extracted for each of the recognized words. Experiments using English and Japanese lecture speech corpora show that the recognition accuracy can be accurately estimated with the CRF-based method. The correlation coefficient and root mean square error between the lecture-level true WAccs calculated using the reference transcriptions and those estimated with the CRF-based method are 0.97 and lower than 2%, respectively. A series of additional experiments and analyses are also conducted to better understand the effectiveness of the CRF-based method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2400–2413},
numpages = {14}
}

@article{10.1109/TASLP.2016.2604566,
author = {Cernak, Milos and Lazaridis, Alexandros and Asaei, Afsaneh and Garner, Philip N. and Cernak, Milos and Lazaridis, Alexandros and Asaei, Afsaneh and Garner, Philip N.},
title = {Composition of Deep and Spiking Neural Networks for Very Low Bit Rate Speech Coding},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2604566},
doi = {10.1109/TASLP.2016.2604566},
abstract = {Most current very low bit rate VLBR speech coding systems use hidden Markov model HMM based speech recognition and synthesis techniques. This allows transmission of information such as phonemes segment by segment; this decreases the bit rate. However, an encoder based on a phoneme speech recognition may create bursts of segmental errors; these would be further propagated to any suprasegmental such as syllable information coding. Together with the errors of voicing detection in pitch parametrization, HMM-based speech coding leads to speech discontinuities and unnatural speech sound artifacts. In this paper, we propose a novel VLBR speech coding framework based on neural networks NNs for end-to-end speech analysis and synthesis without HMMs. The speech coding framework relies on a phonological subphonetic representation of speech. It is designed as a composition of deep and spiking NNs: a bank of phonological analyzers at the transmitter, and a phonological synthesizer at the receiver. These are both realized as deep NNs, along with a spiking NN as an incremental and robust encoder of syllable boundaries for coding of continuous fundamental frequency F0. A combination of phonological features defines much more sound patterns than phonetic features defined by HMM-based speech coders; this finer analysis/synthesis code contributes to smoother encoded speech. Listeners significantly prefer the NN-based approach due to fewer discontinuities and speech artifacts of the encoded speech. A single forward pass is required during the speech encoding and decoding. The proposed VLBR speech coding operates at a bit rate of approximately 360 bits/s.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2301–2312},
numpages = {12}
}

@article{10.1109/TASLP.2016.2598305,
author = {Cogliati, Andrea and Duan, Zhiyao and Wohlberg, Brendt and Cogliati, Andrea and Zhiyao Duan and Wohlberg, Brendt},
title = {Context-Dependent Piano Music Transcription With Convolutional Sparse Coding},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598305},
doi = {10.1109/TASLP.2016.2598305},
abstract = {This paper presents a novel approach to automatic transcription of piano music in a context-dependent setting. This approach employs convolutional sparse coding to approximate the music waveform as the summation of piano note waveforms dictionary elements convolved with their temporal activations onset transcription. The piano note waveforms are pre-recorded for the specific piano to be transcribed in the specific environment. During transcription, the note waveforms are fixed and their temporal activations are estimated and post-processed to obtain the pitch and onset transcription. This approach works in the time domain, models temporal evolution of piano notes, and estimates pitches and onsets simultaneously in the same framework. Experiments show that it significantly outperforms a state-of-the-art music transcription method trained in the same context-dependent setting, in both transcription accuracy and time precision, in various scenarios including synthetic, anechoic, noisy, and reverberant environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2218–2230},
numpages = {13}
}

@article{10.1109/TASLP.2016.2566919,
author = {Dov, David and Talmon, Ronen and Cohen, Israel and Dov, David and Talmon, Ronen and Cohen, Israel},
title = {Kernel Method for Voice Activity Detection in the Presence of Transients},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2566919},
doi = {10.1109/TASLP.2016.2566919},
abstract = {Voice activity detection in the presence of transient interferences is a challenging problem since transients are often detected incorrectly as speech by existing detectors. In this paper, we deviate from traditional approaches and take a geometric standpoint, in which the key element in obtaining an accurate voice activity detection is finding a metric that appropriately distinguishes between speech and transients. For example, speech and transients may often appear similar through the Euclidean distance when represented, e.g., by the Mel-frequency cepstral coefficients, thereby resulting in incorrect speech detection. To address this challenge, we propose to use a metric based on the statistics of the signal in short temporal windows and justify its use by modeling speech and transients by their latent generating variables. These latent variables may be related to physical constraints controlling the generation of the signal, and, as such, they accurately represent the content of the signal - speech or transient. We show that the Euclidean distance between the latent variables is approximated by the proposed metric. Then, by incorporating this metric into a kernel-based manifold learning method, we devise a measure of voice activity and show it leads to improved detection scores compared with competing detectors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2313–2326},
numpages = {14}
}

@article{10.1109/TASLP.2016.2607341,
author = {Sailor, Hardik B. and Patil, Hemant A. and Sailor, Hardik B. and Patil, Hemant A.},
title = {Novel Unsupervised Auditory Filterbank Learning Using Convolutional RBM for Speech Recognition},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2607341},
doi = {10.1109/TASLP.2016.2607341},
abstract = {To learn auditory filterbanks, recently, we have proposed an unsupervised learning model based on convolutional restricted Boltzmann machine RBM with rectified linear units. In this paper, theory, training algorithm of our proposed model, and detailed analysis of learned filterbank are being presented. Learning of the model with different databases shows that the model is able to learn cochlear-like impulse responses that are localized in frequency-domain. An auditory-like scale obtained from filterbanks learned from clean and noisy datasets resembles the Mel scale, which is known to mimic perceptually relevant aspect of speech. We have experimented with both cepstral denoted as ConvRBM-CC as well as filterbank features denoted as ConvRBM-BANK. On large vocabulary continuous speech recognition task, we achieved relative improvement of 7.21-17.8% in word error rate WER compared to Mel frequency cepstral coefficient MFCC features and 1.35-6.82% compared to Mel filterbank FBANK features. On AURORA 4 multicondition training database, the relative improvement in WER by 4.8-13.65% was achieved using a Hybrid Deep Neural Network-Hidden Markov Model DNN-HMM system with ConvRBM-CC features. Using ConvRBM-BANK features, we achieve absolute reduction of 1.25-3.85% in WER on AURORA 4 test sets compared to FBANK features. A context-dependent DNN-HMM system further improves performance with a relative improvement of 3.6-4.6% on an average for bigram 5k and tri-gram 5k language models. Hence, our proposed learned filterbank performs better than traditional MFCC and Mel-filterbank features for both clean and multicondition automatic speech recognition ASR tasks. A system combination of ConvRBM-BANK and FBANK features further improve performance in all ASR tasks. Cross-domain experiments where subband filters trained on one database are used for the ASR task of another database show that model learns generalized representations of speech signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2341–2353},
numpages = {13}
}

@article{10.1109/TASLP.2016.2598308,
author = {Qian, Yanmin and Tan, Tian and Yu, Dong and Yanmin Qian and Tian Tan and Dong Yu},
title = {Neural Network Based Multi-Factor Aware Joint Training for Robust Speech Recognition},
year = {2016},
issue_date = {December 2016},
publisher = {IEEE Press},
volume = {24},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598308},
doi = {10.1109/TASLP.2016.2598308},
abstract = {Although great progress has been made in automatic speech recognition ASR, significant performance degradation still exists in noisy environments. In this paper, a novel factor-aware training framework, named neural network-based multifactor aware joint training, is proposed to improve the recognition accuracy for noise robust speech recognition. This approach is a structured model which integrates several different functional modules into one computational deep model. We explore and extract speaker, phone, and environment factor representations using deep neural networks DNNs, which are integrated into the main ASR DNN to improve classification accuracy. In addition, the hidden activations in the main ASR DNN are used to improve factor extraction, which in turn helps the ASR DNN. All the model parameters, including those in the ASR DNN and factor extraction DNNs, are jointly optimized under the multitask learning framework. Unlike prior traditional techniques for the factor-aware training, our approach requires no explicit separate stages for factor extraction and adaptation. Moreover, the proposed neural network-based multifactor aware joint training can be easily combined with the conventional factor-aware training which uses the explicit factors, such as i-vector, noise energy, and T60 value to obtain additional improvement. The proposed method is evaluated on two main noise robust tasks: the AMI single distant microphone task in which reverberation is the main concern, and the Aurora4 task in which multiple noise types exist. Experiments on both tasks show that the proposed model can significantly reduce word error rate WER. The best configuration achieved more than 15% relative reduction in WER over the baselines on these two tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2231–2240},
numpages = {10}
}

@article{10.1109/TASLP.2016.2598306,
author = {Wang, Jia-Ching and Lee, Yuan-Shan and Lin, Chang-Hong and Wang, Shu-Fan and Shih, Chih-Hao and Wu, Chung-Hsien and Jia-Ching Wang and Yuan-Shan Lee and Chang-Hong Lin and Shu-Fan Wang and Chih-Hao Shih and Chung-Hsien Wu},
title = {Compressive Sensing-Based Speech Enhancement},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598306},
doi = {10.1109/TASLP.2016.2598306},
abstract = {This study proposes a speech enhancement method based on compressive sensing. The main procedures involved in the proposed method are performed in the frequency domain. First, an overcomplete dictionary is constructed from the trained speech frames. The atoms of this redundant dictionary are spectrum vectors that are trained by the K-SVD algorithm to ensure the sparsity of the dictionary. For a noisy speech spectrum, formant detection and a quasi-SNR criterion are first utilized to determine whether a frequency bin in the spectrogram is reliable, and a corresponding mask is designed. The mask-extracted reliable components in a speech spectrum are regarded as partial observations and a measurement matrix is constructed. The problem can therefore be treated as a compressive sensing problem. The K atoms of a K-sparsity speech spectrum are found using an orthogonal matching pursuit algorithm. Because the K atoms form the speech signal subspace, the removal of the noise projected onto these K atoms is achieved by multiplying the noisy spectrum with the optimized gain that corresponds to each selected atom. The proposed method is experimentally compared with the baseline methods and demonstrates its superiority.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2122–2131},
numpages = {10}
}

@article{10.1109/TASLP.2016.2598323,
author = {Jao, Ping-Keng and Su, Li and Yang, Yi-Hsuan and Wohlberg, Brendt and Ping-Keng Jao and Li Su and Yi-Hsuan Yang and Wohlberg, Brendt},
title = {Monaural Music Source Separation Using Convolutional Sparse Coding},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598323},
doi = {10.1109/TASLP.2016.2598323},
abstract = {We present a comprehensive performance study of a new time-domain approach for estimating the components of an observed monaural audio mixture. Unlike existing time-frequency approaches that use the product of a set of spectral templates and their corresponding activation patterns to approximate the spectrogram of the mixture, the proposed approach uses the sum of a set of convolutions of estimated activations with prelearned dictionary filters to approximate the audio mixture directly in the time domain. The approximation problem can be solved by an efficient convolutional sparse coding algorithm. The effectiveness of this approach for source separation of musical audio has been demonstrated in our prior work, but under rather restricted and controlled conditions, requiring the musical score of the mixture being informed a priori and little mismatch between the dictionary filters and the source signals. In this paper, we report an evaluation that considers wider, and more practical, experimental settings. This includes the use of an audio-based multipitch estimation algorithm to replace the musical score, and an external dataset of audio single notes to construct the dictionary filters. Our result shows that the proposed approach remains effective with a larger dictionary, and compares favorably with the state-of-the-art nonnegative matrix factorization approach. However, in the absence of the score and in the case of a small dictionary, our approach may not be better.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2158–2170},
numpages = {13}
}

@article{10.1109/TASLP.2016.2593801,
author = {Ewert, Sebastian and Sandler, Mark and Ewert, Sebastian and Sandler, Mark},
title = {Piano Transcription in the Studio Using an Extensible Alternating Directions Framework},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2593801},
doi = {10.1109/TASLP.2016.2593801},
abstract = {Given a musical audio recording, the goal of automatic music transcription is to determine a score-like representation of the piece underlying the recording. Despite significant interest within the research community, several studies have reported on a “glass ceiling” effect, an apparent limit on the transcription accuracy that current methods seem incapable of overcoming. In this paper, we explore how much this effect can be mitigated by focusing on a specific instrument class and making use of additional information on the recording conditions available in studio or home recording scenarios. In particular, exploiting the availability of single note recordings for the instrument in use, we develop a novel signal model employing variable-length spectro-temporal patterns as its central building blocks-tailored for pitched percussive instruments such as the piano. Temporal dependencies between spectral templates are modeled, resembling characteristics of factorial scaled hidden Markov models FS-HMM and other methods combining nonnegative matrix factorization with Markov processes. In contrast to FS-HMMs, our parameter estimation is developed in a global, relaxed form within the extensible alternating direction method of multipliers framework, which enables the systematic combination of basic regularizers propagating sparsity and local stationarity in note activity with more complex regularizers imposing temporal semantics. The proposed method achieves an f -measure of 93-95% for note onsets on pieces recorded on a Yamaha Disklavier MAPS DB.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1983–1997},
numpages = {15}
}

@article{10.1109/TASLP.2016.2594282,
author = {Chien, Yu-Ren and Wang, Hsin-Min and Jeng, Shyh-Kang and Yu-Ren Chien and Hsin-Min Wang and Shyh-Kang Jeng},
title = {Alignment of Lyrics With Accompanied Singing Audio Based on Acoustic-Phonetic Vowel Likelihood Modeling},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2594282},
doi = {10.1109/TASLP.2016.2594282},
abstract = {This study addresses the task of aligning lyrics with accompanied singing recordings. With a vowel-only representation of lyric syllables, our approach evaluates likelihood scores of vowel types with glottal pulse shapes and formant frequencies extracted from a small set of singing examples. The proposed vowel likelihood model is used in conjunction with a prior model of frame-wise syllable sequence in determining an optimal evolution of syllabic position. In lyrics alignment experiments, we optimized numerical parameters on two independent development sets and then tested the optimized system on two other datasets. New objective performance measures are introduced in the evaluation to provide further insight into the quality of alignment. Use of glottal pulse shapes and formant frequencies is shown by a controlled experiment to account for a 0.07 difference in average normalized alignment error. Another controlled experiment demonstrates that, with a difference of 0.03, F0-invariant glottal pulse shape gives a lower average normalized alignment error than does F0-invariant spectrum envelope, the latter being assumed by MFCC-based timbre models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1998–2008},
numpages = {11}
}

@article{10.1109/TASLP.2016.2594287,
author = {Wang, Jin and Yu, Liang-Chih and Lai, K. Robert and Zhang, Xuejie and Jin Wang and Liang-Chih Yu and Lai, K. Robert and Xuejie Zhang},
title = {Community-Based Weighted Graph Model for Valence-Arousal Prediction of Affective Words},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2594287},
doi = {10.1109/TASLP.2016.2594287},
abstract = {Compared to the categorical approach that represents affective states as several discrete classes e.g., positive and negative, the dimensional approach represents affective states as continuous numerical values in multiple dimensions, such as the valence-arousal VA space, thus allowing for more fine-grained sentiment analysis. In building dimensional sentiment applications, affective lexicons with VA ratings are useful resources but are still very rare. Several semi-supervised methods such as the kernel method, linear regression, and the pagerank algorithm have been investigated to automatically determine the VA ratings of affective words from a set of semantically similar seed words. These methods suffer from two major limitations. First, they apply an equal weight to all seeds similar to an unseen word in predicting its VA ratings. Second, even similar seeds may have quite different ratings or an inverse polarity of valence/arousal to the unseen word, thus reducing prediction performance. To overcome these limitations, this study proposes a community-based weighted graph model that can select seeds which are both similar to and have similar ratings or the same polarity with each unseen word to form a community subgraph so that its VA ratings can be estimated from such high-quality seeds using a weighted propagation scheme. That is, seeds more similar to unseen words contribute more to the estimation process. Experimental results show that the proposed method yields better prediction performance for both English and Chinese datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1957–1968},
numpages = {12}
}

@article{10.1109/TASLP.2016.2594383,
author = {Xiao, Tong and Wong, Derek F. and Zhu, Jingbo and Tong Xiao and Wong, Derek F. and Jingbo Zhu},
title = {A Loss-Augmented Approach to Training Syntactic Machine Translation Systems},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2594383},
doi = {10.1109/TASLP.2016.2594383},
abstract = {Current syntactic machine translation MT systems implicitly use beam-width unlimited search in learning model parameters e.g., feature values for each translation rule. However, a limited beam-width has to be adopted in decoding new sentences, and the MT output is in general evaluated by various metrics, such as BLEU and TER. In this paper, we address: 1 the mismatch of adopted beam-widths between training and decoding; and 2 the mismatch of training criteria and MT evaluation metrics. Unlike previous work, we model the two problems in a single training paradigm simultaneously. We design a loss-augmented approach that explicitly considers the limited beam-width and evaluation metric in training, and present a simple but effective method to learn the model. By using beam search and BLEU-related losses, our approach improves a state-of-the-art syntactic MT system by +1.0 BLEU on Chinese-to-English and English-to-Chinese translation tasks. It even outperforms seven previous training approaches over 0.8 BLEU points. More interestingly, promising improvements are observed when our approach works with TER.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2069–2083},
numpages = {15}
}

@article{10.1109/TASLP.2016.2593803,
author = {Carini, Alberto and Cecchi, Stefania and Romoli, Laura and Carini, Alberto and Cecchi, Stefania and Romoli, Laura},
title = {Robust Room Impulse Response Measurement Using Perfect Sequences for Legendre Nonlinear Filters},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2593803},
doi = {10.1109/TASLP.2016.2593803},
abstract = {The paper proposes a novel approach for measuring the room impulse response that is robust toward the nonlinearities affecting the power amplifier or the loudspeaker. The approach is implemented by modeling the acoustic path as a Legendre nonlinear filter and by measuring the first-order kernel using perfect periodic sequences and the cross-correlation method. Perfect sequences for Legendre filters are periodic sequences that guarantee the orthogonality of the Legendre basis functions over a period. They ensure the robustness of the first kernel measurement toward nonlinear distortions. The paper also explains how perfect periodic sequences for Legendre filters that are suitable for room impulse response identification can be developed. Experimental results involving both simulated and real environments illustrate the effectiveness and the robustness of the proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1969–1982},
numpages = {14}
}

@article{10.1109/TASLP.2016.2554283,
author = {Gkiokas, Aggelos and Katsouros, Vassilis and Carayannis, George and Gkiokas, Aggelos and Katsouros, Vassilis and Carayannis, George},
title = {Towards Multi-Purpose Spectral Rhythm Features: An Application to Dance Style, Meter and Tempo Estimation},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2554283},
doi = {10.1109/TASLP.2016.2554283},
abstract = {This paper addresses the extraction of multipurpose spectral rhythm features that simultaneously tackle a variety of rhythm analysis tasks, namely, dance style classification, meter estimation, and tempo estimation. The term spectral rhythm features emanates from the origin of the extracted features, which is the periodicity function PF, a spectral representation that encapsulates the salience of the rhythm frequencies. Two dimensionality reduction techniques applied on the PF to extract expressive and compact features are compared, namely, a linear transformation resulting from Principal Component Analysis and a nonlinear mapping derived from a Restricted Boltzmann Machine. Subsequently, the derived features were used as input to an SVM classifier for each task. Moreover, an additional method is proposed that reformulates the well-studied tempo estimation task as a combination of multiple binary classification sub-problems. Evaluation was performed on a large number of datasets demonstrating that the same set of features learned from the PF provide a robust rhythmic representation that achieved comparable results to the current state-of-the-art methods for the aforementioned tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1885–1896},
numpages = {12}
}

@article{10.1109/TASLP.2016.2593800,
author = {Liu, Yuzong and Kirchhoff, Katrin and Yuzong Liu and Kirchhoff, Katrin},
title = {Graph-Based Semisupervised Learning for Acoustic Modeling in Automatic Speech Recognition},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2593800},
doi = {10.1109/TASLP.2016.2593800},
abstract = {In this paper, we investigate how to apply graph-based semisupervised learning to acoustic modeling in speech recognition. Graph-based semisupervised learning is a widely used transductive semisupervised learning method in which labeled and unlabeled data are jointly represented as a weighted graph; the resulting graph structure is then used as a constraint during the classification of unlabeled data points. We investigate suitable graph-based learning algorithms for speech data and evaluate two different frameworks for integrating graph-based learning into state-of-the-art, deep neural network DDN-based speech recognition systems. The first framework utilizes graph-based learning in parallel with a DNN classifier within a lattice-rescoring framework, whereas the second framework relies on an embedding of graph neighborhood information into continuous space using an autoencoder. We demonstrate significant improvements in framelevel phonetic classification accuracy and consistent reductions in word error rate on large-vocabulary conversational speech recognition tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1946–1956},
numpages = {11}
}

@article{10.1109/TASLP.2016.2588727,
author = {Huang, Yi-Chin and Wu, Chung-Hsien and Weng, Si-Ting and Yi-Chin Huang and Chung-Hsien Wu and Si-Ting Weng},
title = {Improving Mandarin Prosody Generation Using Alternative Smoothing Techniques},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2588727},
doi = {10.1109/TASLP.2016.2588727},
abstract = {Prosody plays a vital role for conveying both communicative meanings and specific speaking styles in speech communication. In recent years, Hidden Markov Model HMM-based synthesis system HTS has been developed in triumph, which can synthesize stable and smooth speech. However, the prosody of the synthesized speech suffers from the over-smoothing problem. Thus, a better prosodic model is required to improve the natural variability of the synthesized speech. This study exploits a hybrid method to alleviate this problem by combining the statistical and the template-based unit selection methods. First, a two-level clustering approach is proposed to obtain representative prosodic patterns denoted by codewords of the hierarchical prosodic structure modeled by a modified Fujisaki model. The prosodic codewords are then used to represent the prosody of each sentence in the parallel corpus consisting of the real speech corpus and the synthesized counterpart obtained from the HTS. The synthesized speech utterance is then used as the query for retrieving the prosodic codewords of the utterances in the synthesized corpus. The retrieved synthesized prosodic codewords are mapped to the prosodic codewords of the real speech based on linear mapping rules obtained from the parallel corpus. The prosodic codeword language models for prosodic word and prosodic phrase are employed respectively to choose the optimal codeword sequence of the real speech. Finally, the most likely sequence of prosodic codewords can be obtained based on the NURBS-based continuity measure for synthesizing speech with natural prosody. The experimental results of subjective and objective tests demonstrate that the proposed prosodic model substantially improves naturalness of the intonation of the synthesized speech compared to that of the HMM-based method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1897–1907},
numpages = {11}
}

@article{10.1109/TASLP.2016.2590146,
author = {Zhang, Qiaoling and Chen, Zhe and Yin, Fuliang and Qiaoling Zhang and Zhe Chen and Fuliang Yin},
title = {Distributed Marginalized Auxiliary Particle Filter for Speaker Tracking in Distributed Microphone Networks},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2590146},
doi = {10.1109/TASLP.2016.2590146},
abstract = {In this paper, a distributed marginalized auxiliary particle filter DMAPF is proposed for speaker tracking in distributed microphone networks. After marginalizing the state-space model, the speaker's velocity and position are estimated using the distributed Kalman filter and the distributed auxiliary particle filter APF, respectively. To overcome the adverse effects of noise and reverberation, a time difference of arrival selection scheme is presented to construct the local observation vector, based on the generalized cross-correlation function of the microphone pair signals at each node. Next, the multiple-hypothesis model is used as the local likelihood function of the DMAPF. Finally, the DMAPF is employed to estimate the time-varying positions of a moving speaker. The proposed method combines the strengths of the marginalized particle filter, APF, and distributed estimation. It can track the speaker successfully in noisy and reverberant environments. Moreover, it requires only local communication among neighboring nodes, and is scalable for speaker tracking. Experimental results reveal the validity of the proposed speaker tracking method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1921–1934},
numpages = {14}
}

@article{10.1109/TASLP.2016.2588002,
author = {Andersen, Asger Heidemann and de Haan, Jan Mark and Tan, Zheng-Hua and Jensen, Jesper and Andersen, Asger Heidemann and de Haan, Jan Mark and Zheng-Hua Tan and Jensen, Jesper},
title = {Predicting the Intelligibility of Noisy and Nonlinearly Processed Binaural Speech},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2588002},
doi = {10.1109/TASLP.2016.2588002},
abstract = {Objective speech intelligibility measures are gaining popularity in the development of speech enhancement algorithms and speech processing devices such as hearing aids. Such devices may process the input signals nonlinearly and modify the binaural cues presented to the user. We propose a method for predicting the intelligibility of noisy and nonlinearly processed binaural speech. This prediction is based on the noisy and processed signal as well as a clean speech reference signal. The method is obtained by extending a modified version of the short-time objective intelligibility STOI measure with a modified equalization-cancellation EC stage. We evaluate the performance of the method by comparing the predictions with measured intelligibility from four listening experiments. These comparisons indicate that the proposed measure can provide accurate predictions of 1 the intelligibility of diotic speech with an accuracy similar to that of the original STOI measure, 2 speech reception thresholds SRTs in conditions with a frontal target speaker and a single interferer in the horizontal plane, 3 SRTs in conditions with a frontal target and a single interferer when ideal time frequency segregation ITFS is applied to the left and right ears separately, and 4 the advantage of two-microphone beamforming as applied in state-of-the-art hearing aids. A MATLAB implementation of the proposed measure is available online1.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1908–1920},
numpages = {13}
}

@article{10.1109/TASLP.2016.2593944,
author = {Parthasarathy, Srinivas and Cowie, Roddy and Busso, Carlos and Parthasarathy, Srinivas and Cowie, Roddy and Busso, Carlos},
title = {Using Agreement on Direction of Change to Build Rank-Based Emotion Classifiers},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2593944},
doi = {10.1109/TASLP.2016.2593944},
abstract = {Automatic emotion recognition in realistic domains is a challenging task given the subtle expressive behaviors that occur during human interactions. The challenges start with noisy emotional descriptors provided by multiple evaluators, which are characterized by low interevaluator agreement. Studies have suggested that evaluators are more consistent in detecting qualitative relations between episodes i.e., emotional contrasts, rather than absolute scores i.e., the actual emotion. Based on these observations, this study explores the use of relative labels to train machine learning algorithms that can rank expressive behaviors. Instead of deriving relative labels from expensive and time-consuming subjective evaluations, the labels are extracted from existing time-continuous evaluations over expressive attributes annotated with FEELTRACE. We rely on the qualitative agreement QA analysis to estimate relative labels which are used to train rank-based classifiers rankers. The experimental evaluation on the SEMAINE database demonstrates the benefits of the proposed approach. The ranking performance using the QA-based labels compare favorably against preference learning rankers trained with relative labels obtained by simply aggregating the absolute values of the emotional traces across evaluators, which is the common approach used by other studies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2108–2121},
numpages = {14}
}

@article{10.1109/TASLP.2016.2577879,
author = {Ikemiya, Yukara and Itoyama, Katsutoshi and Yoshii, Kazuyoshi and Ikemiya, Yukara and Itoyama, Katsutoshi and Yoshii, Kazuyoshi},
title = {Singing Voice Separation and Vocal F0 Estimation Based on Mutual Combination of Robust Principal Component Analysis and Subharmonic Summation},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2577879},
doi = {10.1109/TASLP.2016.2577879},
abstract = {This paper presents a new method of singing voice analysis that performs mutually-dependent singing voice separation and vocal fundamental frequency F0 estimation. Vocal F0 estimation is considered to become easier if singing voices can be separated from a music audio signal, and vocal F0 contours are useful for singing voice separation. This calls for an approach that improves the performance of each of these tasks by using the results of the other. The proposed method first performs robust principal component analysis RPCA for roughly extracting singing voices from a target music audio signal. The F0 contour of the main melody is then estimated from the separated singing voices by finding the optimal temporal path over an F0 saliency spectrogram. Finally, the singing voices are separated again more accurately by combining a conventional time-frequency mask given by RPCA with another mask that passes only the harmonic structures of the estimated F0s. Experimental results showed that the proposed method significantly improved the performances of both singing voice separation and vocal F0 estimation. The proposed method also outperformed all the other methods of singing voice separation submitted to an international music analysis competition called MIREX 2014.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2084–2095},
numpages = {12}
}

@article{10.1109/TASLP.2016.2598428,
author = {Le, Duc and Licata, Keli and Persad, Carol and Provost, Emily Mower and Le, Duc and Licata, Keli and Persad, Carol and Provost, Emily Mower},
title = {Automatic Assessment of Speech Intelligibility for Individuals With Aphasia},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598428},
doi = {10.1109/TASLP.2016.2598428},
abstract = {Traditional in-person therapy may be difficult to access for individuals with aphasia due to the shortage of speech-language pathologists and high treatment cost. Computerized exercises offer a promising low-cost and constantly accessible supplement to in-person therapy. Unfortunately, the lack of feedback for verbal expression in existing programs hinders the applicability and effectiveness of this form of treatment. A prerequisite for producing meaningful feedback is speech intelligibility assessment. In this work, we investigate the feasibility of an automated system to assess three aspects of aphasic speech intelligibility: clarity, fluidity, and prosody. We introduce our aphasic speech corpus, which contains speech-based interaction between individuals with aphasia and a tablet-based application designed for therapeutic purposes. We present our method for eliciting reliable ground-truth labels for speech intelligibility based on the perceptual judgment of nonexpert human evaluators. We describe and analyze our feature set engineered for capturing pronunciation, rhythm, and intonation. We investigate the classification performance of our system under two conditions, one using human-labeled transcripts to drive feature extraction, and another using transcripts generated automatically. We show that some aspects of aphasic speech intelligibility can be estimated at human-level performance. Our results demonstrate the potential for the computerized treatment of aphasia and lay the groundwork for bridging the gap between human and automatic intelligibility assessment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2187–2199},
numpages = {13}
}

@article{10.1109/TASLP.2016.2594255,
author = {Cui, Xiaodong and Goel, Vaibhava},
title = {Maximum Likelihood Nonlinear Transformations Based on Deep Neural Networks},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2594255},
doi = {10.1109/TASLP.2016.2594255},
abstract = {Feature transformations are commonly used in speech recognition to account for distribution mismatches between the source and target domains also referred to as covariate shift. Linear affine or piecewise linear transformations are typically considered. In this paper, we present deep neural network DNN based nonlinear feature transformations estimated under the maximum likelihood criterion. We use the hidden Markov model HMM to model speech feature sequences and features in each HMM state assume a Gaussian mixture model GMM distribution. The network is pre-trained close to a linear transformation followed by a fine-tuning using the gradient descent algorithm. Due to the nonlinearity, the gradients and the partition functions of GMM-HMM state distributions are evaluated using the Monte Carlo MC method based on importance sampling. In addition, a deep stacked architecture is proposed to hierarchically build a DNN as a series of sub-networks with each representing a nonlinear transformation itself, which can be learned using a block-wise learning strategy. Applications of the proposed nonlinear transformations in speaker/environment adaptation and acoustic modeling in large vocabulary continuous speech recognition tasks show its superior performance over the widely-used constrained maximum likelihood linear regression CMLLR.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2023–2031},
numpages = {9}
}

@article{10.1109/TASLP.2016.2598307,
author = {Liao, I-Bin and Chiang, Chen-Yu and Wang, Yih-Ru and Chen, Sin-Horng and I-Bin Liao and Chen-Yu Chiang and Yih-Ru Wang and Sin-Horng Chen},
title = {Speaker Adaptation of SR-HPM for Speaking Rate-Controlled Mandarin TTS},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598307},
doi = {10.1109/TASLP.2016.2598307},
abstract = {In this paper, a structural maximum a posteriori SMAP speaker adaptation approach to adjusting the speaking rate SR-dependent hierarchical prosodic model SR-HPM of an existing SR-controlled Mandarin text-to-speech system to a new speaker's data for producing a new voice is discussed. Two main issues are addressed. One is the small SR coverage of the adaptation data and is solved by using the existing SR-HPM that was trained from a speech corpus of wide SR coverage as an informative prior. Another is the data sparseness problem resulting from the large number of parameters of the SR-HPM to be adjusted. It is solved by hierarchically organizing the SR-HPM parameters into decision trees so as to be efficiently adjusted by the SMAP method. The effectiveness of the proposed approach is evaluated on speech databases of five new speakers. Both objective and subjective evaluations show that the proposed method not only performs better than the maximum likelihood-based method in the observed SR range of the target speaker's data, but also is much better in the unseen SR ranges.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2046–2058},
numpages = {13}
}

@article{10.1109/TASLP.2016.2592698,
author = {Sigtia, Siddharth and Stark, Adam M. and Krstulovic, Sacha and Plumbley, Mark D. and Sigtia, Siddharth and Stark, Adam M. and Krstulovic, Sacha and Plumbley, Mark D.},
title = {Automatic Environmental Sound Recognition: Performance Versus Computational Cost},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2592698},
doi = {10.1109/TASLP.2016.2592698},
abstract = {In the context of the Internet of Things, sound sensing applications are required to run on embedded platforms where notions of product pricing and form factor impose hard constraints on the available computing power. Whereas Automatic Environmental Sound Recognition AESR algorithms are most often developed with limited consideration for computational cost, this paper seeks which AESR algorithm can make the most of a limited amount of computing power by comparing the sound classification performance as a function of its computational cost. Results suggest that Deep Neural Networks yield the best ratio of sound classification accuracy across a range of computational costs, while Gaussian Mixture Models offer a reasonable accuracy at a consistently small cost, and Support Vector Machines stand between both in terms of compromise between accuracy and computational cost.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2096–2107},
numpages = {12}
}

@article{10.1109/TASLP.2016.2593263,
author = {Nakashika, Toru and Takiguchi, Tetsuya and Minami, Yasuhiro and Nakashika, Toru and Takiguchi, Tetsuya and Minami, Yasuhiro},
title = {Non-Parallel Training in Voice Conversion Using an Adaptive Restricted Boltzmann Machine},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2593263},
doi = {10.1109/TASLP.2016.2593263},
abstract = {In this paper, we present a voice conversion VC method that does not use any parallel data while training the model. VC is a technique where only speaker-specific information in source speech is converted while keeping the phonological information unchanged. Most of the existing VC methods rely on parallel data-pairs of speech data from the source and target speakers uttering the same sentences. However, the use of parallel data in training causes several problems: 1 the data used for the training are limited to the predefined sentences, 2 the trained model is only applied to the speaker pair used in the training, and 3 mismatches in alignment may occur. Although it is, thus, fairly preferable in VC not to use parallel data, a nonparallel approach is considered difficult to learn. In our approach, we achieve nonparallel training based on a speaker adaptation technique and capturing latent phonological information. This approach assumes that speech signals are produced from a restricted Boltzmann machine-based probabilistic model, where phonological information and speaker-related information are defined explicitly. Speaker-independent and speaker-dependent parameters are simultaneously trained under speaker adaptive training. In the conversion stage, a given speech signal is decomposed into phonological and speaker-related information, the speaker-related information is replaced with that of the desired speaker, and then voice-converted speech is obtained by mixing the two. Our experimental results showed that our approach outperformed another nonparallel approach, and produced results similar to those of the popular conventional Gaussian mixture models-based method that used parallel data in subjective and objective criteria.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2032–2045},
numpages = {14}
}

@article{10.1109/TASLP.2016.2598319,
author = {Li, Xiaofei and Girin, Laurent and Horaud, Radu and Gannot, Sharon and Xiaofei Li and Girin, Laurent and Horaud, Radu and Gannot, Sharon},
title = {Estimation of the Direct-Path Relative Transfer Function for Supervised Sound-Source Localization},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598319},
doi = {10.1109/TASLP.2016.2598319},
abstract = {This paper addresses the problem of sound-source localization of a single speech source in noisy and reverberant environments. For a given binaural microphone setup, the binaural response corresponding to the direct-path propagation of a single source is a function of the source direction. In practice, this response is contaminated by noise and reverberations. The direct-path relative transfer function DP-RTF is defined as the ratio between the direct-path acoustic transfer function of the two channels. We propose a method to estimate the DP-RTF from the noisy and reverberant microphone signals in the short-time Fourier transform STFT domain. First, the convolutive transfer function approximation is adopted to accurately represent the impulse response of the sensors in the STFT domain. Second, the DP-RTF is estimated by using the auto- and cross-power spectral densities at each frequency and over multiple frames. In the presence of stationary noise, an interframe spectral subtraction algorithm is proposed, which enables to achieve the estimation of noise-free auto- and cross-power spectral densities. Finally, the estimated DP-RTFs are concatenated across frequencies and used as a feature vector for the localization of speech source. Experiments with both simulated and real data show that the proposed localization method performs well, even under severe adverse acoustic conditions, and outperforms state-of-the-art localization methods under most of the acoustic conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2171–2186},
numpages = {16}
}

@article{10.1109/TASLP.2016.2585878,
author = {Jensen, Jesper and Taal, Cees H. and Jensen, Jesper and Taal, Cees H.},
title = {An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2585878},
doi = {10.1109/TASLP.2016.2585878},
abstract = {Intelligibility listening tests are necessary during development and evaluation of speech processing algorithms, despite the fact that they are expensive and time consuming. In this paper, we propose a monaural intelligibility prediction algorithm, which has the potential of replacing some of these listening tests. The proposed algorithm shows similarities to the short-time objective intelligibility STOI algorithm, but works for a larger range of input signals. In contrast to STOI, extended STOI ESTOI does not assume mutual independence between frequency bands. ESTOI also incorporates spectral correlation by comparing complete 400ms length spectrograms of the noisy/processed speech and the clean speech signals. As a consequence, ESTOI is also able to accurately predict the intelligibility of speech contaminated by temporally highly modulated noise sources in addition to noisy signals processed with time-frequency weighting. We show that ESTOI can be interpreted in terms of an orthogonal decomposition of short-time spectrograms into intelligibility subspaces, i.e., a ranking of spectrogram features according to their importance to intelligibility. A free MATLAB implementation of the algorithm is available for noncommercial use at http://kom.aau.dk/~jje/.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2009–2022},
numpages = {14}
}

@article{10.1109/TASLP.2016.2599275,
author = {van de Laar, Thijs and de Vries, Bert and van de Laar, Thijs and de Vries, Bert},
title = {A Probabilistic Modeling Approach to Hearing Loss Compensation},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2599275},
doi = {10.1109/TASLP.2016.2599275},
abstract = {Hearing Aid HA algorithms need to be tuned “fitted” to match the impairment of each specific patient. The lack of a fundamental HA fitting theory is a strong contributing factor to an unsatisfying sound experience for about 20% of HA patients. This paper proposes a probabilistic modeling approach to the design of HA algorithms. The proposed method relies on a generative probabilistic model for the hearing loss problem and provides for automated inference of the corresponding 1 signal processing algorithm, 2 the fitting solution as well as 3 a principled performance evaluation metric. All three tasks are realized as message passing algorithms in a factor graph representation of the generative model, which in principle allows for fast implementation on HA or mobile device hardware. The methods are theoretically worked out and simulated with a custom-built factor graph toolbox for a specific hearing loss model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2200–2213},
numpages = {14}
}

@article{10.1109/TASLP.2016.2590139,
author = {Ferras, Marc and Madikeri, Srikanth and Bourlard, Herve and Ferras, Marc and Madikeri, Srikanth and Bourlard, Herve},
title = {Speaker Diarization and Linking of Meeting Data},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2590139},
doi = {10.1109/TASLP.2016.2590139},
abstract = {Finding who spoke when in a collection of recordings, with speakers being uniquely identified across the database, is a challenging task. In this scenario, reasonable computing times and acoustic variation across recordings remain two major concerns to address in state-of-the-art speaker diarization systems. This paper extends prior work on diarizing large speech datasets using algorithms that scale well with increasing amounts of data while compensating for across-recording variability. We follow a two-stage approach performing speaker diarization and speaker linking, the former focusing on local within-recording speaker changes and the latter focusing on global speaker changes across the database. In this study, we explore how these two modules interact with each other, while proposing a diarization fusion approach that prevents diarization errors from propagating to the linking stage. We further explore the diarization fusion for speaker linking using different linking strategies and speaker modeling variants. Evaluation is performed on single distant microphone data from the augmented multiparty interaction corpus show the effectiveness of the fusion approach after speaker linking and intersession variability modeling via joint factor analysis.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1935–1945},
numpages = {11}
}

@article{10.1109/TASLP.2016.2598310,
author = {Ouchi, Hiroki and Duh, Kevin and Shindo, Hiroyuki and Matsumoto, Yuji and Ouchi, Hiroki and Duh, Kevin and Shindo, Hiroyuki and Matsumoto, Yuji},
title = {Transition-Based Dependency Parsing Exploiting Supertags},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598310},
doi = {10.1109/TASLP.2016.2598310},
abstract = {Lexical information, including surface word form and part-of-speech POS information, plays a crucial role when predicting ambiguous dependency relationships in dependency parsing. However, for resolving dependency ambiguities, surface word information may be too sparse, while POS information may be too coarse. Supertags, which are lexical templates that represent rich syntactic information, have been shown to provide effective features at an intermediate level on the coarse-to-fine scale. In this work, we present a supertag design framework that allows us to instantiate various supertag sets based on the dependency structures. Using this framework, we instantiate various supertag sets and utilize them as features in transition-based dependency parsing systems. Performing experiments on the Penn Treebank and Universal Dependencies data sets, we show that our supertags are effective for transition-based parsers in multilingual parsing as well as English parsing. The comparison of the results of the different supertag sets shows that it is crucial to incorporate the head directionality, head labels, and dependent possession information in supertags to improve the parser performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2059–2068},
numpages = {10}
}

@article{10.1109/TASLP.2016.2598318,
author = {Wang, Siying and Ewert, Sebastian and Dixon, Simon and Siying Wang and Ewert, Sebastian and Dixon, Simon},
title = {Robust and Efficient Joint Alignment of Multiple Musical Performances},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598318},
doi = {10.1109/TASLP.2016.2598318},
abstract = {The goal of music alignment is to map each temporal position in one version of a piece of music to the corresponding positions in other versions of the same piece. Despite considerable improvements in recent years, state-of-the-art methods still often fail to identify a correct alignment if versions differ substantially with respect to acoustic conditions or musical interpretation. To increase the robustness for these cases, we exploit in this work the availability of multiple versions of the piece to be aligned. By processing these jointly, we can supply the alignment process with additional examples of how a section might be interpreted or which acoustic conditions may arise. This way, we can use alignment information between two versions transitively to stabilize the alignment with a third version. Extending our previous work [1], we present two such joint alignment methods, progressive alignment and probabilistic profile, and discuss their fundamental differences and similarities on an algorithmic level. Our systematic experiments using 376 recordings of 9 pieces demonstrate that both methods can indeed improve the alignment accuracy and robustness over comparable pairwise methods. Further, we provide an in-depth analysis of the behavior of both joint alignment methods, studying the influence of parameters such as the number of performances available, comparing their computational costs, and investigating further strategies to increase both.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2132–2145},
numpages = {14}
}

@article{10.1109/TASLP.2016.2598304,
author = {Chen, Xie and Liu, Xunying and Wang, Yongqiang and Gales, Mark J. F. and Woodland, Philip C. and Xie Chen and Xunying Liu and Yongqiang Wang and Gales, Mark J. F. and Woodland, Philip C.},
title = {Efficient Training and Evaluation of Recurrent Neural Network Language Models for Automatic Speech Recognition},
year = {2016},
issue_date = {November 2016},
publisher = {IEEE Press},
volume = {24},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2598304},
doi = {10.1109/TASLP.2016.2598304},
abstract = {Recurrent neural network language models RNNLMs are becoming increasingly popular for a range of applications including automatic speech recognition. An important issue that limits their possible application areas is the computational cost incurred in training and evaluation. This paper describes a series of new efficiency improving approaches that allows RNNLMs to be more efficiently trained on graphics processing units GPUs and evaluated on CPUs. First, a modified RNNLM architecture with a nonclass-based, full output layer structure F-RNNLM is proposed. This modified architecture facilitates a novel spliced sentence bunch mode parallelization of F-RNNLM training using large quantities of data on a GPU. Second, two efficient RNNLM training criteria based on variance regularization and noise contrastive estimation are explored to specifically reduce the computation associated with the RNNLM output layer softmax normalisation term. Finally, a pipelined training algorithm utilizing multiple GPUs is also used to further improve the training speed. Initially, RNNLMs were trained on a moderate dataset with 20M words from a large vocabulary conversational telephone speech recognition task. The training time of RNNLM is reduced by up to a factor of 53 on a single GPU over the standard CPU-based RNNLM toolkit. A 56 times speed up in test time evaluation on a CPU was obtained over the baseline F-RNNLMs. Consistent improvements in both recognition accuracy and perplexity were also obtained over C-RNNLMs. Experiments on Google's one billion corpus also reveals that the training of RNNLM scales well.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2146–2157},
numpages = {12}
}

@article{10.1109/TASLP.2016.2584705,
author = {Kortlang, Steffen and Grimm, Giso and Hohmann, Volker and Kollmeier, Birger and Ewert, Stephan D. and Kortlang, Steffen and Grimm, Giso and Hohmann, Volker and Kollmeier, Birger and Ewert, Stephan D. and Grimm, Giso and Ewert, Stephan D. and Hohmann, Volker and Kollmeier, Birger and Kortlang, Steffen},
title = {Auditory Model-Based Dynamic Compression Controlled by Subband Instantaneous Frequency and Speech Presence Probability Estimates},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2584705},
doi = {10.1109/TASLP.2016.2584705},
abstract = {Sensorineural hearing loss typically results in elevated thresholds and steepened loudness growth significantly conditioned by a damage of outer hair cells OHC. In hearing aids, amplification and dynamic compression aim at widening the limited available dynamic range. However, speech perception particularly in complex acoustic scenes often remains difficult. Here, a physiologically motivated, fast acting, model-based dynamic compression algorithm MDC is introduced which aims at restoring the behaviorally estimated basilar membrane input-output BM I/O function in normal-hearing listeners. A system-specific gain prescription rule is suggested, based on the same model BM I/O function and a behavioral estimate of the individual OHC loss. Cochlear off-frequency component suppression is mimicked using an instantaneous frequency IF estimate. Increased loudness as a consequence of widened filters in the impaired system is considered in a further compensation stage. In an extended version, a subband estimate of the speech presence probability MDC+SPP additionally provides speech-selective amplification in stationary noise. Instrumental evaluation revealed that the IF control enhances the spectral contrast of vowels and benefits in quality predictions at higher signal-to-noise ratios SNRs were observed. Compared with a conventional multiband dynamic compressor, MDC achieved objective quality and intelligibility benefits for a competing talker at lower SNRs. MDC+SPP outperformed the conventional compressor in the quality predictions and reached comparable instrumental speech intelligibility as achieved with linear amplification. The proposed algorithm provides a first promising basis for auditory model-based compression with signal-type- and bandwidth-dependent gains.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1759–1772},
numpages = {14}
}

@article{10.1109/TASLP.2016.2585863,
author = {Ma, Xi and Wang, Dong and Tejedor, Javier and Xi Ma and Dong Wang and Tejedor, Javier and Tejedor, Javier and Ma, Xi and Wang, Dong},
title = {Similar Word Model for Unfrequent Word Enhancement in 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.2016.2585863},
doi = {10.1109/TASLP.2016.2585863},
abstract = {The popular n-gram language model LM is weak for unfrequent words. Conventional approaches such as class-based LMs pre-define some sharing structures e.g., word classes to solve the problem. However, defining such structures requires prior knowledge, and the context sharing based on these structures is generally inaccurate. This paper presents a novel similar word model to enhance unfrequent words. In principle, we enrich the context of an unfrequent word by borrowing context information from some “similar words.” Compared to conventional class-based methods, this new approach offers a fine-grained context sharing by referring to words that best match the target word, and it is more flexible as no sharing structures need to be defined by hand. Experiments on a large-scale Chinese speech recognition task demonstrated that the similar word approach can improve performance on unfrequent words significantly, while keeping the performance on general tasks almost unchanged.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1819–1830},
numpages = {12}
}

@article{10.1109/TASLP.2016.2584700,
author = {Swietojanski, Pawel and Renals, Steve and Swietojanski, Pawel and Renals, Steve and Swietojanski, Pawel and Renals, Steve},
title = {Differentiable Pooling for Unsupervised Acoustic Model Adaptation},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2584700},
doi = {10.1109/TASLP.2016.2584700},
abstract = {We present a deep neural network DNN acoustic model that includes parametrised and differentiable pooling operators. Unsupervised acoustic model adaptation is cast as the problem of updating the decision boundaries implemented by each pooling operator. In particular, we experiment with two types of pooling parametrisations: learned Lp-norm pooling and weighted Gaussian pooling, in which the weights of both operators are treated as speaker-dependent. We perform investigations using three different large vocabulary speech recognition corpora: AMI meetings, TED talks, and Switchboard conversational telephone speech. We demonstrate that differentiable pooling operators provide a robust and relatively low-dimensional way to adapt acoustic models, with relative word error rates reductions ranging from 5-20% with respect to unadapted systems, which themselves are better than the baseline fully-connected DNN-based acoustic models. We also investigate how the proposed techniques work under various adaptation conditions including the quality of adaptation data and complementarity to other feature- and model-space adaptation methods, as well as providing an analysis of the characteristics of each of the proposed approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1773–1784},
numpages = {12}
}

@article{10.1109/TASLP.2016.2585879,
author = {Niwa, Kenta and Hioka, Yusuke and Kobayashi, Kazunori and Niwa, Kenta and Hioka, Yusuke and Kobayashi, Kazunori and Niwa, Kenta and Hioka, Yusuke and Kobayashi, Kazunori},
title = {Optimal Microphone Array Observation for Clear Recording of Distant Sound Sources},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2585879},
doi = {10.1109/TASLP.2016.2585879},
abstract = {We propose the principle for deriving an optimum design for a microphone array that uses mutual information to segregate distant sound sources. Many conventional studies on array signal processing have focused on methods for estimating sound sources from array observations. To record distant sound sources clearly, designing an optimum array structure to segregate a target from other noise is also necessary. In this study, we reveal that the optimum array observation was achieved by receiving signals that are physically decorrelated between microphones, which homogenizes the eigenvalues of the spatial correlation matrix. We theoretically explain this underlying principle using mutual information between sound sources and microphone observations whose relation to the existing minimum mean square error criterion for source separation is also discussed. The implementation of such a microphone array is possible by placing microphones in front of parabolic reflectors since the phase/amplitude around the focal point of the reflectors drastically varies with small perturbation of the microphone position. Crosscorrelation between observed signals can be reduced by optimally placing microphones. An array structure based on our proposed principle was tested by implementing minimum variance distortion-less response beamforming and postfiltering in the observations of a prototype microphone array. We experimentally confirmed that 1 the eigenvalues of the spatial correlation matrix were asymptotically homogenized and 2 the target source could be extracted clearly even when the sound sources were positioned 16.5 m from the array.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1785–1795},
numpages = {11}
}

@article{10.1109/TASLP.2016.2585864,
author = {Zorila, Tudor-Catalin and Stylianou, Yannis and Ishihara, Tatsuma and Akamine, Masami and Zorila, Tudor-Catalin and Stylianou, Yannis and Ishihara, Tatsuma and Akamine, Masami and Ishihara, Tatsuma and Stylianou, Yannis and Akamine, Masami and Zorila, Tudor-Catalin},
title = {Near and Far Field Speech-in-Noise Intelligibility Improvements Based on a Time–Frequency Energy Reallocation Approach},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2585864},
doi = {10.1109/TASLP.2016.2585864},
abstract = {An algorithm designed to enhance the intelligibility of speech signals before they are presented in noisy environments was evaluated. The processed and unprocessed speech had the same root-mean square level. Spectral energy was redistributed to increase the signal-to-noise ratio SNR in the mid- and high-frequency bands, while the softer segments of speech were increased in level by applying time-domain dynamic range compression. Noise level adaptation was introduced to increase the subjective quality of signals at high SNRs. Evaluations were conducted both in near field headphones and in far field outdoor conditions using listeners with normal hearing and two types of background. The results showed: a In the near field test, the proposed algorithm yielded significant intelligibility improvements relative to the unprocessed speech for both stationary and nonstationary backgrounds; b In the far field test, the proposed algorithm increased the intelligibility of unprocessed speech by a factor of seven at 200-m distance from the sound source; and c When the background level did not alter intelligibility, listeners preferred the quality of the speech processed by the noise-dependent version of the algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1808–1818},
numpages = {11}
}

@article{10.1109/TASLP.2016.2577502,
author = {Eaton, James and Gaubitch, Nikolay D. and Moore, Alastair H. and Naylor, Patrick A. and Eaton, James and Gaubitch, Nikolay D. and Moore, Alastair H. and Naylor, Patrick A. and Gaubitch, Nikolay D. and Eaton, James and Naylor, Patrick A. and Moore, Alastair H.},
title = {Estimation of Room Acoustic Parameters: The ACE Challenge},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2577502},
doi = {10.1109/TASLP.2016.2577502},
abstract = {Reverberation time T60 and Direct-to-reverberant ratio DRR are important parameters which together can characterize sound captured by microphones in nonanechoic rooms. These parameters are important in speech processing applications such as speech recognition and dereverberation. The values of T60 and DRR can be estimated directly from the acoustic impulse response AIR of the room. In practice, the AIR is not normally available, in which case these parameters must be estimated blindly from the observed speech in the microphone signal. The acoustic characterization of environments ACE challenge aimed to determine the state-of-the-art in blind acoustic parameter estimation and also to stimulate research in this area. A summary of the ACE challenge, and the corpus used in the challenge is presented together with an analysis of the results. Existing algorithms were submitted alongside novel contributions, the comparative results for which are presented in this paper. The challenge showed that T60 estimation is a mature field where analytical approaches dominate whilst DRR estimation is a less mature field where machine learning approaches are currently more successful.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1681–1693},
numpages = {13}
}

@article{10.1109/TASLP.2016.2580298,
author = {Nose, Takashi and Nose, Takashi and Nose, Takashi},
title = {Efficient Implementation of Global Variance Compensation for Parametric Speech Synthesis},
year = {2016},
issue_date = {October 2016},
publisher = {IEEE Press},
volume = {24},
number = {10},
issn = {2329

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr