text:
particular choice varies with the language. By combining the different methods, a substantial gain is obtained over the best single case for all languages, especially for OOV performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {79–92},
numpages = {14},
keywords = {keyword search, out-of-vocabulary (OOV) words, graphones, subword units, morphological analysis, speech recognition}
}

@article{10.1109/TASLP.2015.2456421,
author = {Brognaux, Sandrine and Drugman, Thomas},
title = {HMM-Based Speech Segmentation: Improvements of Fully Automatic Approaches},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456421},
doi = {10.1109/TASLP.2015.2456421},
abstract = {Speech segmentation refers to the problem of determining the phoneme boundaries from an acoustic recording of an utterance together with its orthographic transcription. This paper focuses on a particular case of hidden Markov model (HMM)-based forced alignment in which the models are directly trained on the corpus to align. The obvious advantage of this technique is that it is applicable to any language or speaking style and does not require manually aligned data. Through a systematic step-by-step study, the role played by various training parameters (e.g. models configuration, number of training iterations) on the alignment accuracy is assessed, with corpora varying in speaking style and language. Based on a detailed analysis of the errors commonly made by this technique, we also investigate the use of additional fully automatic strategies to improve the alignment. Beside the use of supplementary acoustic features, we explore two novel approaches: an initialization of the silence models based on a voice activity detection (VAD) algorithm and the consideration of the forced alignment of the time-reversed sound. The evaluation is carried out on 12 corpora of different sizes, languages (some being under-resourced) and speaking styles. It aims at providing a comprehensive study of the alignment accuracy achieved by the different versions of the speech segmentation algorithm depending on corpus-related specificities. While the baseline method is shown to reach good alignment rates with corpora as small as 2 minutes, we also emphasize the benefit of using a few seconds of bootstrapping data. Regarding improvement methods, our results show that the insertion of additional features outperforms both other strategies. The performance of VAD, however, is shown to be notably striking on very small corpora, correcting more than 60% of the errors superior to 40 ms. Finally, the combination of the three improvement methods is also pointed out as providing the highest alignment rates, with very low variability across the corpora, regardless of their size. This combined technique is shown to outperform available speaker-independent models, improving the alignment rate by 8 to 10% absolute.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {5–15},
numpages = {11},
keywords = {corpora annotation, speech segmentation, hidden markov models, phonetic alignment}
}

@article{10.1109/TASLP.2015.2496226,
author = {Ferrer, Luciana and Lei, Yun and McLaren, Mitchell and Scheffer, Nicolas},
title = {Study of Senone-Based Deep Neural Network Approaches for Spoken Language Recognition},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2496226},
doi = {10.1109/TASLP.2015.2496226},
abstract = {This paper compares different approaches for using deep neural networks (DNNs) trained to predict senone posteriors for the task of spoken language recognition (SLR). These approaches have recently been found to outperform various baseline systems on different datasets, but they have not yet been compared to each other or to a common baseline. Two of these approaches use the DNNs to generate feature vectors which are then processed in different ways to predict the score of each language given a test sample. The features are extracted either from a bottleneck layer in the DNN or from the output layer. In the third approach, the standard i-vector extraction procedure is modified to use the senones as classes and the DNN to predict the zeroth order statistics. We compare these three approaches and conclude that the approach based on bottleneck features followed by i-vector modeling outperform the other two approaches. We also show that score-level fusion of some of these approaches leads to gains over using a single approach for short-duration test samples. Finally, we demonstrate that fusing systems that use DNNs trained with several languages leads to improvements in performance over the best single system, and we propose an adaptation procedure for DNNs trained with languages with less available data. Overall, we show improvements between 40% and 70% relative to a state-of-the-art Gaussian mixture model (GMM) i-vector system on test durations from 3 seconds to 120 seconds on two significantly different tasks: the NIST 2009 language recognition evaluation task and the DARPA RATS language identification task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {105–116},
numpages = {12},
keywords = {deep neural networks (DNNs), spoken language recognition (SLR), senones}
}

@article{10.1109/TASLP.2015.2489558,
author = {Behravan, Hamid and Hautam\"{a}ki, Ville and Siniscalchi, Sabato Marco and Kinnunen, Tomi and Lee, Chin-Hui},
title = {I-Vector Modeling of Speech Attributes for Automatic Foreign Accent Recognition},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2489558},
doi = {10.1109/TASLP.2015.2489558},
abstract = {We propose a unified approach to automatic foreign accent recognition. It takes advantage of recent technology advances in both linguistics and acoustics based modeling techniques in automatic speech recognition (ASR) while overcoming the issue of a lack of a large set of transcribed data often required in designing state-of-the-art ASR systems. The key idea lies in defining a common set of fundamental units "universally" across all spoken accents such that any given spoken utterance can be transcribed with this set of "accent-universal" units. In this study, we adopt a set of units describing manner and place of articulation as speech attributes. These units exist in most spoken languages and they can be reliably modeled and extracted to represent foreign accent cues. We also propose an i-vector representation strategy to model the feature streams formed by concatenating these units. Testing on both the Finnish national foreign language certificate (FSD) corpus and the English NIST 2008 SRE corpus, the experimental results with the proposed approach demonstrate a significant system performance improvement with p-value < 0.05 over those with the conventional spectrum-based techniques. We observed up to a 15% relative error reduction over the already very strong i-vector accented recognition system when only manner information is used. Additional improvement is obtained by adding place of articulation clues along with context information. Furthermore, diagnostic information provided by the proposed approach can be useful to the designers to further enhance the system performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {29–41},
numpages = {13},
keywords = {english corpus, i-vector system, finnish corpus, attribute detectors}
}

@article{10.1109/TASLP.2015.2499038,
author = {Mak, Man-Wai and Pang, Xiaomin and Chien, Jen-Tzung},
title = {Mixture of PLDA for Noise Robust I-Vector Speaker Verification},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2499038},
doi = {10.1109/TASLP.2015.2499038},
abstract = {In real-world environments, noisy utterances with variable noise levels are recorded and then converted to i-vectors for cosine distance or PLDA scoring. This paper investigates the effect of noise-level variability on i-vectors. It demonstrates that noise-level variability causes the i-vectors to shift, causing the noise contaminated i-vectors to form clusters in the i-vector space. It also demonstrates that optimal subspaces for discriminating speakers are noise-level dependent. Based on these observations, this paper proposes using signal-to-noise ratio (SNR) of utterances as guidance for training mixture of PLDA models. To maximize the coordination among the PLDA models, mixtures of PLDA models are trained simultaneously via an EM algorithm using the utterances contaminated with noise at various levels. For scoring, given a test i-vector, the marginal likelihoods from individual PLDA models are linearly combined by the posterior probabilities of the test utterance's SNR. Verification scores are the ratio of the marginal likelihoods. Results based on NIST 2012 SRE suggest that the SNR-dependent mixture of PLDA is not only suitable for the situations where the test utterances exhibit a wide range of SNR, but also beneficial for the test utterances with unknown SNR distribution. Supplementary materials containing full derivations of the EM algorithms and scoring functions can be found in http://bioinfo.eie.polyu.edu.hk/mPLDA/SuppMaterials.pdf.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {130–142},
numpages = {13},
keywords = {speaker verification, noise robustness, mixture of PLDA, probabilistic LDA, i-vectors}
}

@article{10.1109/TASLP.2015.2500018,
author = {Bilbao, Stefan and Hamilton, Brian and Botts, Jonathan and Savioja, Lauri},
title = {Finite Volume Time Domain Room Acoustics Simulation under General Impedance Boundary Conditions},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2500018},
doi = {10.1109/TASLP.2015.2500018},
abstract = {In room acoustics simulation and virtualization applications, accurate wall termination is a perceptually crucial feature. It is particularly important in the setting of wave-based modeling of 3D spaces, using methods such as the finite difference time domain method or finite volume time domain method. In this paper, general locally reactive impedance boundary conditions are incorporated into a 3D finite volume time domain formulation, which may be specialized to the various types of finite difference time domain method under fitted boundary termination. Energy methods are used to determine stability conditions for general room geometries, under a large family of nontrivial wall impedances, for finite volume methods over unstructured grids. Simulation results are presented, highlighting in particular the need for unstructured or fitted cells at the room boundary in the case of the accurate simulation of frequency-dependent room mode decay times.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {161–173},
numpages = {13},
keywords = {room acoustics, finite volume methods, finite difference time domain method}
}

@article{10.1109/TASLP.2015.2502141,
author = {Chien, Jen-Tzung and Yang, Po-Kai},
title = {Bayesian Factorization and Learning for Monaural Source Separation},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2502141},
doi = {10.1109/TASLP.2015.2502141},
abstract = {This paper presents a new Bayesian nonnegative matrix factorization (NMF) for monaural source separation. Using this approach, the reconstruction error based on NMF is represented by a Poisson distribution, and the NMF parameters, consisting of the basis and weight matrices, are characterized by the exponential priors. A variational Bayesian inference procedure is developed to learn variational parameters and model parameters. The randomness in separation process is faithfully represented so that the system robustness to model variations in heterogeneous environments could be achieved. Importantly, the exponential prior parameters are used to impose sparseness in basis representation. The variational lower bound of log marginal likelihood is adopted as the objective to control model complexity. The dependencies of variational objective on model parameters are fully characterized in the derived closed-form solution. A clustering algorithm is performed to find the groups of bases for unsupervised source separation. The experiments on speech/music separation and singing voice separation show that the proposed Bayesian NMF (BNMF) with adaptive basis representation outperforms the NMF with fixed number of bases and the other BNMFs in terms of signal-to-distortion ratio and the global normalized source to distortion ratio.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {185–195},
numpages = {11},
keywords = {bayesian learning, monaural source separation, model complexity, nonnegative matrix factorization}
}

@article{10.1109/TASLP.2015.2502059,
author = {Alon, David Lou and Rafaely, Boaz},
title = {Beamforming with Optimal Aliasing Cancellation in Spherical Microphone Arrays},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2502059},
doi = {10.1109/TASLP.2015.2502059},
abstract = {Spherical microphone arrays facilitate three-dimensional processing and analysis of sound fields in applications such as music recording, beamforming and room acoustics. The frequency bandwidth of operation is constrained by the array configuration. At high frequencies, spatial aliasing leads to side-lobes in the array beam pattern, which limits array performance. Previous studies proposed increasing the number of microphones or changing other characteristics of the array configuration to reduce the effect of aliasing. In this paper we present a method to design beamformers that overcome the effect of spatial aliasing by suppressing the undesired side-lobes through signal processing without physically modifying the configuration of the array. This is achieved by modeling the expected aliasing pattern in a maximum-directivity beamformer design, leading to a higher directivity index at frequencies previously considered to be out of the operating bandwidth, thereby extending the microphone array frequency range of operation. Aliasing cancellation is then extended to other beamformers. A simulation example with a 32-element spherical microphone array illustrates the performance of the proposed method. An experimental example validates the theoretical results in practice.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {196–210},
numpages = {15},
keywords = {aliasing cancellation, spherical microphone arrays, white-noise gain, beamforming, optimal beamformer, array processing, directivity, spatial aliasing, maximum-directiviy beamformer, spherical harmonics}
}

@article{10.1109/TASLP.2015.2493366,
author = {Saeidi, Rahim and Alku, Paavo and B\"{a}ckstr\"{o}m, Tom},
title = {Feature Extraction Using Power-Law Adjusted Linear Prediction with Application to Speaker Recognition under Severe Vocal Effort Mismatch},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2493366},
doi = {10.1109/TASLP.2015.2493366},
abstract = {Linear prediction is one of the most established techniques in signal estimation, and it is widely utilized in speech signal processing. It has been long understood that the nerve firing rate of human auditory system can be approximated by power law nonlinearity, and this has been the motivation behind using perceptual linear prediction in extracting acoustic features in a variety of speech processing applications. In this paper, we revisit the application of power law non-linearity in speech spectrum estimation by compressing/expanding power spectrum in autocorrelation-based linear prediction. The development of so-called LP-α is motivated by a desire to obtain spectral features that present less mismatch than conventionally used spectrum estimation methods when speech of normal loudness is compared to speech under vocal effort. The effectiveness of the proposed approach is demonstrated in a speaker recognition task conducted under severe vocal effort mismatch comparing shouted versus normal speech mode.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {42–53},
numpages = {12},
keywords = {linear prediction, mismatch, speaker recognition, powerlaw, vocal effort, shouting}
}

@article{10.1109/TASLP.2015.2476755,
author = {Xiang, Yong and Natgunanathan, Iynkaran and Rong, Yue and Guo, Song},
title = {Spread Spectrum-Based High Embedding Capacity Watermarking Method for Audio Signals},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2476755},
doi = {10.1109/TASLP.2015.2476755},
abstract = {Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking methods have been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2228–2237},
numpages = {10},
keywords = {PN sequence, spread spectrum, audio watermarking, embedding capacity, copyright protection}
}

@article{10.1109/TASLP.2015.2479940,
author = {Marquardt, Daniel and Hadad, Elior and Gannot, Sharon and Doclo, Simon},
title = {Theoretical Analysis of Linearly Constrained Multi-Channel Wiener Filtering Algorithms for Combined Noise Reduction and Binaural Cue Preservation in Binaural Hearing Aids},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479940},
doi = {10.1109/TASLP.2015.2479940},
abstract = {Besides noise reduction, an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. For the desired speech source and the interfering sources, e.g., competing speakers, this can be achieved by preserving their relative transfer functions (RTFs). It has been shown that the binaural multi-channel Wiener filter (MWF) preserves the RTF of the desired speech source, but typically distorts the RTF of the interfering sources. To this end, in this paper we propose two extensions of the binaural MWF, i.e., the binaural MWF with RTF preservation (MWF-RTF) aiming to preserve the RTF of the interfering source and the binaural MWF with interference rejection (MWF-IR) aiming to completely suppress the interfering source. Analytical expressions for the performance of the binaural MWF, MWF-RTF and MWF-IR in terms of noise reduction, speech distortion and binaural cue preservation are derived, showing that the proposed extensions yield a better performance in terms of the signal-to-interference ratio and preservation of the binaural cues of the directional interference, while the overall noise reduction performance is degraded compared to the binaural MWF. Simulation results using binaural behind-the-ear impulse responses measured in a reverberant environment validate the derived analytical expressions for the theoretically achievable performance of the binaural MWF, MWF-RTF, and MWF-IR, showing that the performance highly depends on the position of the interfering source and the number of microphones. Furthermore, the simulation results show that the MWF-RTF yields a very similar overall noise reduction performance as the binaural MWF, while preserving the binaural cues of both the speech and the interfering source.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2384–2397},
numpages = {14},
keywords = {noise reduction, multi-channel wiener filter (MWF), hearing aids, binaural cues}
}

@article{10.1109/TASLP.2015.2486381,
author = {Hadad, Elior and Marquardt, Daniel and Doclo, Simon and Gannot, Sharon},
title = {Theoretical Analysis of Binaural Transfer Function MVDR Beamformers with Interference Cue Preservation Constraints},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2486381},
doi = {10.1109/TASLP.2015.2486381},
abstract = {The objective of binaural noise reduction algorithms is not only to selectively extract the desired speaker and to suppress interfering sources (e.g., competing speakers) and ambient background noise, but also to preserve the auditory impression of the complete acoustic scene. For directional sources this can be achieved by preserving the relative transfer function (RTF) which is defined as the ratio of the acoustical transfer functions relating the source and the two ears and corresponds to the binaural cues. In this paper, we theoretically analyze the performance of three algorithms that are based on the binaural minimum variance distortionless response (BMVDR) beamformer, and hence, process the desired source without distortion. The BMVDR beamformer preserves the binaural cues of the desired source but distorts the binaural cues of the interfering source. By adding an interference reduction (IR) constraint, the recently proposed BMVDR-IR beamformer is able to preserve the binaural cues of both the desired source and the interfering source. We further propose a novel algorithm for preserving the binaural cues of both the desired source and the interfering source by adding a constraint preserving the RTF of the interfering source, which will be referred to as the BMVDR-RTF beamformer. We analytically evaluate the performance in terms of binaural signal-to-interference-and-noise ratio (SINR), signal-to-interference ratio (SIR), and signal-to-noise ratio (SNR) of the three considered beamformers. It can be shown that the BMVDR-RTF beamformer outperforms the BMVDR-IR beamformer in terms of SINR and outperforms the BMVDR beamformer in terms of SIR. Among all beamformers which are distortionless with respect to the desired source and preserve the binaural cues of the interfering source, the newly proposed BMVDR-RTF beamformer is optimal in terms of SINR. Simulations using acoustic transfer functions measured on a binaural hearing aid validate our theoretical results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2449–2464},
numpages = {16},
keywords = {minimum variance, binaural cues, hearing aids, linearly constrained minimum variance (LCMV) beamformer}
}

@article{10.1109/TASLP.2015.2479045,
author = {Vijayan, Karthika and Murty, K. Sri Rama},
title = {Analysis of Phase Spectrum of Speech Signals Using Allpass Modeling},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479045},
doi = {10.1109/TASLP.2015.2479045},
abstract = {The phase spectrum of Fourier transform has received lesser prominence than its magnitude counterpart in speech processing. In this paper, we propose a method for parametric modeling of the phase spectrum, and discuss its applications in speech signal processing. The phase spectrum is modeled as the response of an allpass (AP) filter, whose coefficients are estimated from the knowledge of speech production process, especially the impulse-like nature of excitation source. A signal retaining only the phase spectral component of speech signal is derived by suppressing the magnitude spectral component, and is modeled as the output of an AP filter excited with a sequence of impulses. Entropy of energy of the input signal is minimized to estimate the coefficients of the AP filter. The resulting objective function, being nonconvex in nature, is minimized using particle swarm optimization. The group delay response of estimated AP filters can be used for accurate analysis of resonances of the vocal-tract system (VTS). The error signal associated with AP modeling provides unambiguous evidence about the instants of significant excitation of the VTS. The applications of the proposed AP modeling include, but not limited to, formant tracking, extraction of glottal closure instants, speaker verification and speech synthesis.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2371–2383},
numpages = {13},
keywords = {phase spectrum, particle swarm optimization, speech processing, entropy minimization, allpass modeling}
}

@article{10.1109/TASLP.2015.2464687,
author = {Graja, M. and Jaoua, M. and Belguith, L. Hadrich},
title = {Statistical Framework with Knowledge Base Integration for Robust Speech Understanding of the Tunisian Dialect},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2464687},
doi = {10.1109/TASLP.2015.2464687},
abstract = {In this paper, we propose a hybrid method for the spoken Tunisian dialect understanding within a limited task. This method couples a discriminative statistical method with a domain ontology. The statistical method is based on conditional random field (CRF) models learned from a little size corpus to perform conceptual labeling task. These models are able to detect the semantic dependency between words. However, the domain ontology is used to add prior knowledge about the task. Our experiments are based on a real spoken Tunisian dialect corpus. The obtained results show that the proposed method is able to improve the performance of CRF models for speech understanding by the integration of the domain ontology. Our method can be exploited for under-resourced languages and Arabic dialects to overcome the lack of linguistic resources.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2311–2321},
numpages = {11},
keywords = {knowledge base, statistical models, domain ontology, speech understanding, tunisian dialect (TD), conditional random field (CRF)}
}

@article{10.1109/TASLP.2015.2443982,
author = {Che, Wanxiang and Zhao, Yanyan and Guo, Honglei and Su, Zhong and Liu, Ting},
title = {Sentence Compression for Aspect-Based Sentiment Analysis},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2443982},
doi = {10.1109/TASLP.2015.2443982},
abstract = {Sentiment analysis, which addresses the computational treatment of opinion, sentiment, and subjectivity in text, has received considerable attention in recent years. In contrast to the traditional coarse-grained sentiment analysis tasks, such as document-level sentiment classification, we are interested in the fine-grained aspect-based sentiment analysis that aims to identify aspects that users comment on and these aspects' polarities. Aspect-based sentiment analysis relies heavily on syntactic features. However, the reviews that this task focuses on are natural and spontaneous, thus posing a challenge to syntactic parsers. In this paper, we address this problem by proposing a framework of adding a sentiment sentence compression (Sent_Comp) step before performing the aspect-based sentiment analysis. Different from the previous sentence compression model for common news sentences, Sent_Comp seeks to remove the sentiment-unnecessary information for sentiment analysis, thereby compressing a complicated sentiment sentence into one that is shorter and easier to parse. We apply a discriminative conditional random field model, with certain special features, to automatically compress sentiment sentences. Using the Chinese corpora of four product domains, Sent_Comp significantly improves the performance of the aspect-based sentiment analysis. The features proposed for Sent_Comp, especially the potential semantic features, are useful for sentiment sentence compression.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2111–2124},
numpages = {14},
keywords = {sentiment analysis, potential semantic features, sentence compression, aspect-based sentiment analysis}
}

@article{10.1109/TASLP.2015.2475155,
author = {Heo, Inseok and Sethares, William A.},
title = {Classification Based on Speech Rhythm via a Temporal Alignment of Spoken Sentences},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2475155},
doi = {10.1109/TASLP.2015.2475155},
abstract = {How much information is contained in the rhythm of speech? Is it possible to tell, just from the rhythm of the speech, whether the speaker is male or female? Is it possible to tell if they are a native or nonnative speaker? This paper provides a new way to address such questions. Traditional investigations into speech rhythm approach the problem by manually annotating the speech, and investigating a preselected collection of features such as the durations of vowels or inter-phoneme timings. This paper presents a method that can automatically align the audio of multiple people when speaking the same sentence. The output of the alignment procedure is a mapping (from the micro-timing of one speaker to that of another) that can be used as a surrogate for speech rhythm. The method is applied to a large online corpus of speakers and shows that it is possible to classify the speakers based on these mappings alone. Several technical aspects are discussed. First, the spectrograms switch between different-length analysis windows (based on whether the speech is voiced or unvoiced) to ameliorate the time-frequency trade-off. These variable window spectrograms are fed into a dynamic time warping algorithm to produce a timing map which represents the speech rhythm. The accuracy of the alignment is evaluated by a technique of transitive validation, and the timing maps are used to form a feature vector for the classification. The method is applied to the online Speech Accent Archive corpus. In the gender discrimination experiments, the proposed method was only about 5% worse than a state-of-the-art classifier based on spectral feature vectors. In the native speaker discrimination task, the speech rhythm was about 15% better than when using spectral information.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2209–2216},
numpages = {8},
keywords = {automated alignment, speech prosody, speech accent, transitive validation, speech rhythm, variable length windows}
}

@article{10.1109/TASLP.2015.2475173,
author = {Samarasinghe, Prasanga and Abhayapala, Thushara and Poletti, Mark and Betlehem, Terence},
title = {An Efficient Parameterization of the Room Transfer Function},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2475173},
doi = {10.1109/TASLP.2015.2475173},
abstract = {This paper proposes an efficient parameterization of the room transfer function (RTF). Typically, the RTF rapidly varies with varying source and receiver positions, hence requires an impractical number of point to point measurements to characterize a given room. Therefore, we derive a novel RTF parameterization that is robust to both receiver and source variations with the following salient features: 1) The parameterization is given in terms of a modal expansion of 3D basis functions. 2) The aforementioned modal expansion can be truncated at a finite number of modes given that the source and receiver locations are from two sizeable spatial regions, which are arbitrarily distributed. 3) The parameter weights/coefficients are independent of the source/receiver positions. Therefore, a finite set of coefficients is shown to be capable of accurately calculating the RTF between any two arbitrary points from a pre-defined spatial region where the source(s) lie and a pre-defined spatial region where the receiver(s) lie. A practical method to measure the RTF coefficients is also provided, which only requires a single microphone unit and a single loudspeaker unit, given that the room characteristics remain stationary over time. The accuracy of the above parameterization is verified using appropriate simulation examples.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2217–2227},
numpages = {11},
keywords = {microphone array processing, room transfer function (RTF), room reverberation}
}

@article{10.1109/TASLP.2015.2481179,
author = {Carlin, Michael A. and Elhilali, Mounya},
title = {A Framework for Speech Activity Detection Using Adaptive Auditory Receptive Fields},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2481179},
doi = {10.1109/TASLP.2015.2481179},
abstract = {One of the hallmarks of sound processing in the brain is the ability of the nervous system to adapt to changing behavioral demands and surrounding soundscapes. It can dynamically shift sensory and cognitive resources to focus on relevant sounds. Neurophysiological studies indicate that this ability is supported by adaptively retuning the shapes of cortical spectro-temporal receptive fields (STRFs) to enhance features of target sounds while suppressing those of task-irrelevant distractors. Because an important component of human communication is the ability of a listener to dynamically track speech in noisy environments, the solution obtained by auditory neurophysiology implies a useful adaptation strategy for speech activity detection (SAD). SAD is an important first step in a number of automated speech processing systems, and performance is often reduced in highly noisy environments. In this paper, we describe how task-driven adaptation is induced in an ensemble of neurophysiological STRFs, and show how speech-adapted STRFs reorient themselves to enhance spectro-temporal modulations of speech while suppressing those associated with a variety of nonspeech sounds. We then show how an adapted ensemble of STRFs can better detect speech in unseen noisy environments compared to an unadapted ensemble and a noise-robust baseline. Finally, we use a stimulus reconstruction task to demonstrate how the adapted STRF ensemble better captures the spectrotemporal modulations of attended speech in clean and noisy conditions. Our results suggest that a biologically plausible adaptation framework can be applied to speech processing systems to dynamically adapt feature representations for improving noise robustness.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2422–2433},
numpages = {12},
keywords = {neural plasticity, adaptive filtering, spectro-temporal receptive fields, stimulus reconstruction, speech activity detection (SAD)}
}

@article{10.1109/TASLP.2015.2473684,
author = {Anderson, Craig A. and Teal, Paul D. and Poletti, Mark A.},
title = {Spatially Robust Far-Field Beamforming Using the von Mises(-Fisher) Distribution},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2473684},
doi = {10.1109/TASLP.2015.2473684},
abstract = {This paper presents spatially robust far-field microphone beamformers and nullformers derived using the von Mises and von Mises-Fisher distributions to model the expected direction of arrival. Simple analytic expressions are presented for 2D and 3D far-field correlation functions and used to design spatially robust beamformers and nullformers. It is demonstrated that the spatially robust beamformers show a modest improvement in tolerating uncertainty in the target direction of arrival without incurring a significant penalty in terms of SINR performance compared with the MVDR beamformer. In addition, the spatially robust formulation shows significantly improved numerical robustness, indicating improved ability in tolerating intrinsic array errors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2189–2197},
numpages = {9},
keywords = {spatial robustness, numerical robustness, robust beamforming}
}

@article{10.1109/TASLP.2015.2469634,
author = {Heidel, Aaron and Lu, Hsiang-Hung and Lee, Lin-Shan},
title = {Finding Complex Features for Guest Language Fragment Recovery in Resource-Limited Code-Mixed Speech Recognition},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2469634},
doi = {10.1109/TASLP.2015.2469634},
abstract = {The rise of mobile devices and online learning brings into sharp focus the importance of speech recognition not only for the many languages of the world but also for code-mixed speech, especially where English is the second language. The recognition of code-mixed speech, where the speaker mixes languages within a single utterance, is a challenge for both computers and humans, not least because of the limited training data. We conduct research on a Mandarin-English code-mixed lecture corpus, where Mandarin is the host language and English the guest language, and attempt to find complex features for the recovery of English segments that were misrecognized in the initial recognition pass. We propose a multi-level framework wherein both low-level and high-level cues are jointly considered; we use phonotactic, prosodic, and linguistic cues in addition to acoustic-phonetic cues to discriminate at the frame level between English- and Chinese-language segments. We develop a simple and exact method for CRF feature induction, and improved methods for using cascaded features derived from the training corpus. By additionally tuning the data imbalance ratio between English and Chinese, we demonstrate highly significant improvements over previous work in the recovery of English-language segments, and demonstrate performance superior to DNN-based methods. We demonstrate considerable performance improvements not only with the traditional GMM-HMM recognition paradigm but also with a state-of-the-art hybrid CD-HMM-DNN recognition framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2148–2161},
numpages = {14},
keywords = {speech recognition, code-mixing, language identification, bilingual}
}

@article{10.1109/TASLP.2015.2467964,
author = {Schr\"{o}der, Jens and Goetze, Stefan and Anem\"{u}ller, J\"{o}rn},
title = {Spectro-Temporal Gabor Filterbank Features for Acoustic Event Detection},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2467964},
doi = {10.1109/TASLP.2015.2467964},
abstract = {Algorithms for the automatic detection and recognition of acoustic events are increasingly gaining relevance for the reliable and robust functioning of consumer, assistive and monitoring systems. The extraction of appropriate task relevant acoustic features from the raw sound signal clearly influences performance of subsequent statistical classification, in particular in adverse acoustic situations. The present contribution investigates the use of biologically-inspired features, derived from a filter-bank of two-dimensional Gabor functions, that decompose the spectro-temporal power density into components which capture spectral, temporal and joint spectro-temporal modulation patterns. It is hypothesized that the comparably large joint spectral and temporal extent of these Gabor functions results in features that allow for robust classification. Evaluation of the proposed feature extraction scheme together with an hidden Markov model (HMM) classifier is conducted on two corpora comprising acoustic events in realistic adverse conditions from the D-CASE and CLEAR'07 evaluation campaigns. Relevance of each Gabor filter for classification is analyzed and an optimized parameter set for the Gabor filterbank (GFB) is identified. Performance of the optimized GFB is evaluated in comparison to other state-of-the-art algorithms on isolated event classification and on the full acoustic event detection (AED) including joint classification and temporal segmentation of events. Results show that Gabor features result in a signal representation that exhibits separated average class-specific patterns. An improvement in classification accuracy of up to 26% relative to the Mel-frequency cepstral coefficient (MFCC) baseline is obtained with the optimized GFB. Further experiments demonstrate that this improvement cannot be explained by purely temporal or purely spectral Gabor basis functions. Rather, a GFB with features extending in joint spectro-temporal directions is required to obtain optimum performance. Performance on AED with the D-CASE challenge dataset is shown to improve on previous algorithms from the recent literature.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2198–2208},
numpages = {11},
keywords = {gabor filter-bank, spectro-temporal filters, acoustic event detection (AED)}
}

@article{10.1109/TASLP.2015.2479041,
author = {Zhong, Xionghu and Hopgood, James R.},
title = {A Time—Frequency Masking Based Random Finite Set Particle Filtering Method for Multiple Acoustic Source Detection and Tracking},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479041},
doi = {10.1109/TASLP.2015.2479041},
abstract = {Considering that multiple talkers may appear simultaneously, a time-frequency (TF) masking based random finite set (RFS) particle filtering (PF) method is developed for multiple acoustic source detection and tracking. The time-delay of arrival (TDOA) measurements of multiple sources are extracted by using a time-frequency masking technique, by which each source's TF bins are clustered and separated in a joint gain-ratio and time-delay histogram. Since a joint detection and tracking problem is considered, both source positions and source numbers are time-varying and need to be estimated. The tracker is built within a RFS Bayesian filtering framework. Essentially, an RFS process is used to characterize the source dynamics that include source appearance/dissappearance and motion trajectories. Latent variables are also introduced to indicate source dynamics and measurement-source associations. Subsequently, a Rao-Blackwellization PF technique is employed so that the source position state can be marginalized and only the latent variables are estimated by using the PF. The main advantage of the proposed approach is that hypothesis-pruning is formulated in a full probabilistic sense. The performance of the proposed approach is demonstrated in real speech recordings as well as in simulated room environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2356–2370},
numpages = {15},
keywords = {acoustic source tracking, particle filtering (PF), random finite set (RFS), room reverberation, time-delay of arrival}
}

@article{10.1109/TASLP.2015.2479037,
author = {Jin, Wenyu and Kleijn, W. Bastiaan},
title = {Theory and Design of Multizone Soundfield Reproduction Using Sparse Methods},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479037},
doi = {10.1109/TASLP.2015.2479037},
abstract = {Multizone soundfield reproduction over an extended spatial region is a challenging problem in acoustic signal processing. We introduce a method of reproducing a multizone soundfield within a desired region in reverberant environments. It is based on the identification of the acoustic transfer function (ATF) from the loudspeaker over the desired reproduction region using a limited number of microphone measurements. We assume that the soundfield is sparse in the domain of planewave decomposition and identify the ATF using sparse methods. The estimates of the ATFs are then used to derive the optimal least-squares solution for the loudspeaker filters that minimize the reproduction error over the entire reproduction region. Simulations confirm that the method leads to a significantly reduced number of required microphones for accurate multizone sound reproduction, while it also facilitates the reproduction over a wide frequency range. Practical experiments are used to verify the sparse planewave representation of the reverberant soundfield in a real-world listening environment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2343–2355},
numpages = {13},
keywords = {acoustic transfer function, planewave decomposition, sparse approximation, multizone soundfield reproduction, reverberation}
}

@article{10.1109/TASLP.2015.2482118,
author = {Fang, Hao and Ostendorf, Mari and Baumann, Peter and Pierrehumbert, Janet},
title = {Exponential Language Modeling Using Morphological Features and Multi-Task Learning},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2482118},
doi = {10.1109/TASLP.2015.2482118},
abstract = {For languages with fast vocabulary growth and limited resources, data sparsity leads to challenges in training a language model. One strategy for addressing this problem is to leverage morphological structure as features in the model. This paper explores different uses of unsupervised morphological features in both the history and prediction space for three word-based exponential models (maximum entropy, logbilinear, and recurrent neural net (RNN)). Multi-task training is introduced as a regularizing mechanism to improve performance in the continuous-space approaches. The models are compared to non-parametric baselines. From using the RNN with morphological features and multi-task learning, experiments with conversational speech from four languages show we can obtain consistent gains of 7-11% in perplexity reduction in a limited-resource scenario (10 hrs speech), and 12-18% when the training size is increased (80 hrs). Results are mixed for all other approaches, compared to a modified Kneser-Ney baseline, but morphology is useful in continuous-space models compared to their word-only baseline. Multi-task learning improves both continuous-space models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2410–2421},
numpages = {12},
keywords = {limited resources, neural network, language model, morphology}
}

@article{10.1109/TASLP.2015.2470597,
author = {Yu, Kai and Sun, Kai and Chen, Lu and Zhu, Su},
title = {Constrained Markov Bayesian Polynomial for Efficient Dialogue State Tracking},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2470597},
doi = {10.1109/TASLP.2015.2470597},
abstract = {Dialogue state tracking (DST) is a process to estimate the distribution of the dialogue states at each dialogue turn given the interaction history. Although data-driven statistical approaches are of most interest, there have been attempts of using rule-based methods for DST, due to their simplicity, efficiency and portability. However, the performance of these methods are usually not competitive to data-driven tracking approaches and it is not possible to improve the DST performance when training data are available. In this paper, a novel hybrid framework, constrained Markov Bayesian polynomial (CMBP), is proposed to formulate rule-based DST in a general way and allow data-driven rule generation. Here, a DST rule is defined as a polynomial function of a set of probabilities satisfying certain linear constraints. Prior knowledge is encoded in these constraints. Under reasonable assumptions, CMBP optimization can be converted to a constrained integer linear programming problem. The integer coefficient CMBP model is further extended to CMBP with real coefficients by applying grid search. CMBP was evaluated on the data corpora of the first, the second, and the third Dialog State Tracking Challenge (DSTC-1/2/3). Experiments showed that CMBP has good generalization ability and can significantly outperform both traditional rule-based approaches and data-driven statistical approaches with similar feature set. Compared with the state-of-the-art statistical DST approaches with much richer features, CMBP is also competitive.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2177–2188},
numpages = {12},
keywords = {data-driven rule, dialogue state tracking (DST), statistical dialogue management, rule-based model}
}

@article{10.1109/TASLP.2015.2479039,
author = {Yeung, Yu Ting and Lee, Tan and Leung, Cheung-Chi},
title = {Supervised Single-Microphone Multi-Talker Speech Separation with Conditional Random Fields},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479039},
doi = {10.1109/TASLP.2015.2479039},
abstract = {We apply conditional random field (CRF) for single-microphone speech separation in a supervised learning scenario. We train the parameters with mixture data in which the sources are competing with the same average signal power. Compared with factorial hidden Markov model (HMM) baselines, the CRF settings require fewer training mixture data to improve objective speech quality measures and speech recognition accuracy of the reconstructed sources, when mixing ratios of training and testing mixture data are matched. The CRF settings also handle minor mixing ratio mismatch after adjusting the gain factors of the sources with non-linear mappings inspired from the mixture-maximization model. When the mixing ratio mismatch further increases such that the speech mixture is dominated by only one source, factorial HMM finally catches up with and performs better than the CRF settings due to improved model accuracy. We also develop a convex statistical inference simplification based on linear-chain CRFs. The simplification achieves the same performance level as the original CRF settings after integrating additional observations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2334–2342},
numpages = {9},
keywords = {conditional random fields (CRFs), statistical model-based methods, single-microphone speech separation}
}

@article{10.1109/TASLP.2015.2479042,
author = {Bayram, Ilker},
title = {A Multichannel Audio Denoising Formulation Based on Spectral Sparsity},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479042},
doi = {10.1109/TASLP.2015.2479042},
abstract = {We consider the estimation of an audio source from multiple noisy observations, where the correlation between noise in the different observations is low. We propose a two-stage method for this estimation problem. The method does not require any information about noise and assumes that the signal of interest has a sparse time-frequency representation. The first stage uses this assumption to obtain the best linear combination of the observations. The second stage estimates the amount of remaining noise and applies a post-filter to further enhance the reconstruction. We discuss the optimality of this method under a specific model and demonstrate its usefulness on synthetic and real data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2272–2285},
numpages = {14},
keywords = {sufficient statistic, spectrogram, post-filter, sparsity, multichannel audio denoising, beamforming, uniformly minimum variance unbiased (UMVU) estimator}
}

@article{10.1109/TASLP.2015.2479040,
author = {Percybrooks, Winston S. and Moore, Elliot},
title = {A New Hmm-Based Voice Conversion Methodology Evaluated on Monolingual and Cross-Lingual Conversion Tasks},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479040},
doi = {10.1109/TASLP.2015.2479040},
abstract = {The work presented here proposes a new voice conversion (VC) approach based on hidden Markov models (HMMs) for spectral conversion and excitation estimation. This paper is divided in two main parts: First, an initial HMM-based VC system is presented and compared to a state-of-the-art ML-GMM VC system in a monolingual conversion scenario with parallel training data; The second part shows the necessary modifications to use the HMM VC system in a cross-lingual conversion scenario and compares it with a cross-lingual VC system based on artificial neural networks (ANNs). The results of the tests show improved performance of the proposed HMM VC system compared with both the ML-GMM and ANN-based VC alternatives, while at the same time keeping most of the flexibility afforded by the ANN approach with respect to training data requirements.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2298–2310},
numpages = {13},
keywords = {hidden markov models (HMMs), excitation estimation, subjective testing, voice conversion, cross-lingual conversion}
}

@article{10.1109/TASLP.2015.2470560,
author = {Z\"{o}hrer, Matthias and Peharz, Robert and Pernkopf, Franz},
title = {Representation Learning for Single-Channel Source Separation and Bandwidth Extension},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2470560},
doi = {10.1109/TASLP.2015.2470560},
abstract = {In this paper, we use deep representation learning for model-based single-channel source separation (SCSS) and artificial bandwidth extension (ABE). Both tasks are ill-posed and source-specific prior knowledge is required. In addition to well-known generative models such as restricted Boltzmann machines and higher order contractive autoencoders two recently introduced deep models, namely generative stochastic networks (GSNs) and sum-product networks (SPNs), are used for learning spectrogram representations. For SCSS we evaluate the deep architectures on data of the 2nd CHiME speech separation challenge and provide results for a speaker dependent, a speaker independent, a matched noise condition and an unmatched noise condition task. GSNs obtain the best PESQ and overall perceptual score on average in all four tasks. Similarly, frame-wise GSNs are able to reconstruct the missing frequency bands in ABE best, measured in frequency-domain segmental SNR. They outperform SPNs embedded in hidden Markov models and the other representation models significantly.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2398–2409},
numpages = {12},
keywords = {single-channel source separation (SCSS), bandwidth extension, sum-product networks, representation learning, generative stochastic networks, deep neural networks (DNNs)}
}

@article{10.1109/TASLP.2015.2468583,
author = {Huang, Po-Sen and Kim, Minje and Hasegawa-Johnson, Mark and Smaragdis, Paris},
title = {Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2468583},
doi = {10.1109/TASLP.2015.2468583},
abstract = {Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including speech separation, singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30-4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30-2.48 dB GNSDR gain and 4.32-5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2136–2147},
numpages = {12},
keywords = {monaural source separation, deep recurrent neural network (DRNN), time-frequency masking, discriminative training}
}

@article{10.1109/TASLP.2015.2478116,
author = {Bai, Hequn and Richard, Ga\"{e}l and Daudet, Laurent},
title = {Late Reverberation Synthesis: From Radiance Transfer to Feedback Delay Networks},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2478116},
doi = {10.1109/TASLP.2015.2478116},
abstract = {In room acoustic modeling, feedback delay networks (FDN) are known to efficiently model late reverberation due to their capacity to generate exponentially decaying dense impulses. However, this method relies on a careful tuning of the different synthesis parameters, either estimated from a pre-recorded impulse response from the real acoustic scene, or set manually from experience. In this paper, we present a new method, which still inherits the efficiency of the FDN structure, but aims at linking the parameters of the FDN directly to the geometry setting. This relation is achieved by studying the sound energy exchange between each delay line using the acoustic radiance transfer method (RTM). Experimental results show that the late reverberation modeled by this method is in good agreement with the virtual geometry setting.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2260–2271},
numpages = {12},
keywords = {reverberation, room acoustics, acoustic radiance transfer, feedback delay networks (FDNs)}
}

@article{10.1109/TASLP.2015.2476762,
author = {Yoo, In-Chul and Lim, Hyeontaek and Yook, Dongsuk},
title = {Formant-Based Robust Voice Activity Detection},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2476762},
doi = {10.1109/TASLP.2015.2476762},
abstract = {Voice activity detection (VAD) can be used to distinguish human speech from other sounds, and various applications can benefit from VAD-including speech coding and speech recognition. To accurately detect voice activity, the algorithm must take into account the characteristic features of human speech and/or background noise. In many real-life applications, noise frequently occurs in an unexpected manner, and in such situations, it is difficult to determine the characteristics of noise with sufficient accuracy. As a result, robust VAD algorithms that depend less on making correct noise estimates are desirable for real-life applications. Formants are the major spectral peaks of the human voice, and these are highly useful to distinguish vowel sounds. The characteristics of the spectral peaks are such that, these peaks are likely to survive in a signal after severe corruption by noise, and so formants are attractive features for voice activity detection under low signal-to-noise ratio (SNR) conditions. However, it is difficult to accurately extract formants from noisy signals when background noise introduces unrelated spectral peaks. Therefore, this paper proposes a simple formant-based VAD algorithm to overcome the problem of detecting formants under conditions with severe noise. The proposed method achieves a much faster processing time and outperforms standard VAD algorithms under various noise conditions. The proposed method is robust against various types of noise and produces a light computational load, so it is suitable for use in various applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2238–2245},
numpages = {8},
keywords = {spectral peaks, formants, voice activity detection (VAD)}
}

@article{10.1109/TASLP.2015.2485663,
author = {Saito, Shinya and Oishi, Kunio and Furukawa, Toshihiro},
title = {Convolutive Blind Source Separation Using an Iterative Least-Squares Algorithm for Non-Orthogonal Approximate Joint Diagonalization},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2485663},
doi = {10.1109/TASLP.2015.2485663},
abstract = {In this paper, we present an approach of recovering signal waveforms of speech sources from observed signals in noisy and reverberant environments. The approach is based on approximate joint diagonalization estimate to provide interference suppression of source signals and reduce echoes and distortions of separated signals. In the proposed approach, the mixing matrix is estimated by minimizing the constrained direct least-squares (LS) criterion in direct model. Exclusively under the condition where the estimated mixing matrix is not of full rank, it is replaced by a full-rank matrix. The unmixing matrix from the estimated mixing matrix is obtained by setting the frequency response of the composite mixing-unmixing filter to identity matrix. The cross-spectral density diagonal matrices of the source signals are precisely estimated by minimizing the indirect LS criterion in indirect model. These operations are fulfilled by using alternating least-squares algorithm. The correlation between the interfrequency power ratios is used to prevent a misalignment permutation of the unmixing matrix. Finally, we compare the proposed BSS with a number of conventional BSS methods in noisy and reverberant environments under both artificial and actual conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2434–2448},
numpages = {15},
keywords = {approximate joint diagonalization (AJD), blind source separation (BSS), convolutive linear mixture, method of Lagrange multipliers, blind identification, alternating least-squares (ALS) algorithm}
}

@article{10.1109/TASLP.2015.2471096,
author = {Marquardt, Daniel and Hohmann, Volker and Doclo, Simon},
title = {Interaural Coherence Preservation in Multi-Channel Wiener Filtering-Based Noise Reduction for Binaural Hearing Aids},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2471096},
doi = {10.1109/TASLP.2015.2471096},
abstract = {Besides noise reduction an important objective of binaural speech enhancement algorithms is the preservation of the binaural cues of all sound sources. To this end, an extension of the binaural multi-channel Wiener filter (MWF), namely the MWF-ITF, has been proposed, which aims to preserve the Interaural Transfer Function (ITF) of the noise sources. However, the MWF-ITF is well-suited only for directional noise sources but not for, e.g., spatially isotropic noise, whose spatial characteristics cannot be properly described by the ITF but rather by the Interaural Coherence (IC). Hence, another extension of the binaural MWF, namely the MWF-IC, has been recently proposed, which aims to preserve the IC of the noise component. Since for the MWF-IC a substantial tradeoff between noise reduction and IC preservation exists, in this paper we propose a perceptually constrained version of the MWF-IC, where the amount of IC preservation is controlled based on the IC discrimination ability of the human auditory system. In addition, a theoretical analysis of the binaural cue preservation capabilities of the binaural MWF and the MWF-ITF for spatially isotropic noise fields is provided. Several simulations in diffuse noise scenarios show that the perceptually constrained MWF-IC yields a controllable preservation of the IC without significantly degrading the output SNR compared to the binaural MWF and the MWF-ITF. Furthermore, contrary to the binaural MWF and MWF-ITF, the proposed algorithm retains the spatial separation between the output speech and noise components while the binaural cues of the speech component are only slightly distorted, such that the binaural hearing advantage for speech intelligibility can still be exploited.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2162–2176},
numpages = {15},
keywords = {hearing aids, noise reduction, multi-channel wiener filter (MWF), binaural cues, interaural coherence}
}

@article{10.1109/TASLP.2015.2488290,
author = {Yang, Guang and Lyon, Richard F. and Drakakis, E. M.},
title = {Psychophysical Evaluation of an Ultra-Low Power, Analog Biomimetic Cochlear Implant Processor Filterbank Architecture with across Channels AGC},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2488290},
doi = {10.1109/TASLP.2015.2488290},
abstract = {This paper evaluated psychophysically an ultra-low-power, analog biomimetic cochlear-implant (CI) processor filterbank architecture, which was recently proposed and demonstrated in hardware. The architecture/strategy emulates the lateral inhibition (LI) mechanism by employing an automatic gain control (AGC) scheme that is coupled across One-Zero-Gamma-tone-Filter (OZGF) channels. The OZGF filtering and the coupled channel AGC were tested respectively in two experiments, where normal-hearing listeners were required to listen to sentences and recognise key words therein. The sentences were mixed with a steady background noise at signal-to-noise ratios (SNRs) of -6, -3 and 0 dB, and processed through a noise-excited envelope vocoder serving as the acoustic simulator of cochlear implants. In the first experiment, the OZGF filtering was compared with cascaded bandpass biquad filtering employed in recent attempts towards fully-implantable CI processing, in terms of the resulting intelligibility. The results showed that the sharply tuned OZGF response did not degrade intelligibility despite the very limited number (16) of channels used. In the second experiment, the sentences were processed in two multi-channel compression systems, one with the channel AGC coupled, whilst another uncoupled. The results showed that the AGC-coupled system was significantly advantageous, and the improvement averaged across the SNRs is 31 percentage points. Furthermore, this compressive system results in no significant decrease in intelligibility when compared to the linear filtering systems investigated in the first experiment. Thus, the coupled channel AGC may be considered as a potential solution to the limited spectral contrast of current CI systems, which may partially account for their noise-susceptibility.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2465–2473},
numpages = {9},
keywords = {spectral enhancement, cochlear implant, lateral inhibition, psychophysical evaluation}
}

@article{10.1109/TASLP.2015.2479043,
author = {Delgado, H\'{e}ctor and Anguera, Xavier and Fredouille, Corinne and Serrano, Javier},
title = {Fast Single- and Cross-Show Speaker Diarization Using Binary Key Speaker Modeling},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479043},
doi = {10.1109/TASLP.2015.2479043},
abstract = {Speaker diarization has become a key process within other speech processing systems which take advantage of single-speaker speech signals. Furthermore, finding recurrent speakers among a set of audio recordings, known as cross-show diarization, is gaining attention in the last years. Current state-of-the-art-systems provide good performance, but usually at the cost of long processing times. This limitation may make current systems not suitable for real-life applications. In this line, the speaker diarization approach based on binary key modeling provides a very fast yet accurate alternative. In this paper, we present the last improvements applied in binary key speaker diarization with the aim of further speeding up the process and improving performance. In addition, we propose a novel method for cross-show speaker diarization based on binary keys. Experimental results show the effectiveness of the proposed improvements for single-show speaker diarization, both in terms of speed and performance, obtaining a real-time factor of 0.0354xRT and a 16.8% relative improvement in performance. Furthermore, our proposed cross-show approach provides very competitive performance, just slightly worse than its single-show diarization counterpart, and exhibits a real time factor of 0.04xRT.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2286–2297},
numpages = {12},
keywords = {cross-show speaker diarization, speaker diarization, binary key speaker modeling, within-class sum of squares}
}

@article{10.1109/TASLP.2015.2464702,
author = {Hueber, Thomas and Girin, Laurent and Alameda-Pineda, Xavier and Bailly, G\'{e}rard},
title = {Speaker-Adaptive Acoustic-Articulatory Inversion Using Cascaded Gaussian Mixture Regression},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2464702},
doi = {10.1109/TASLP.2015.2464702},
abstract = {This paper addresses the adaptation of an acoustic-articulatory model of a reference speaker to the voice of another speaker, using a limited amount of audio-only data. In the context of pronunciation training, a virtual talking head displaying the internal speech articulators (e.g., the tongue) could be automatically animated by means of such a model using only the speaker's voice. In this study, the articulatory-acoustic relationship of the reference speaker is modeled by a gaussian mixture model (GMM). To address the speaker adaptation problem, we propose a new framework called cascaded Gaussian mixture regression (C-GMR), and derive two implementations. The first one, referred to as Split-C-GMR, is a straightforward chaining of two distinct GMRs: one mapping the acoustic features of the source speaker into the acoustic space of the reference speaker, and the other estimating the articulatory trajectories with the reference model. In the second implementation, referred to as Integrated-C-GMR, the two mapping steps are tied together in a single probabilistic model. For this latter model, we present the full derivation of the exact EM training algorithm, that explicitly exploits the missing data methodology of machine learning. Other adaptation schemes based on maximum-a posteriori (MAP), maximum likelihood linear regression (MLLR) and direct cross-speaker acoustic-to-articulatory GMR are also investigated. Experiments conducted on two speakers for different amount of adaptation data show the interest of the proposed C-GMR techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2246–2259},
numpages = {14},
keywords = {speaker adaptation, talking head, pronunciation training, EM algorithm, gaussian mixture regression, speech production, acoustic-articulatory inversion}
}

@article{10.1109/TASLP.2015.2479038,
author = {Strasser, Falco and Puder, Henning},
title = {Adaptive Feedback Cancellation for Realistic Hearing Aid Applications},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2479038},
doi = {10.1109/TASLP.2015.2479038},
abstract = {Acoustic feedback is a well-known phenomenon in hearing aids. Under certain conditions it causes the so-called howling effect, which is highly annoying for the hearing aid user and limits the maximum amplification of the hearing aid. The standard adaptive feedback cancellation algorithms suffer from a biased adaptation if the input signal is spectrally colored, as it is for speech and music signals. Due to this bias distortion artifacts (entrainment) are generated. In this paper, we present a sub-band feedback cancellation system which combines decorrelation methods with a new realization of a known non-parametric variable step size. To apply this step size in the context of adaptive feedback cancellation, a method to estimate the signal power of the desired input signal, i.e., without feedback, is necessary. A major part of this paper is spent with the theoretical derivation of this estimate. Furthermore, the complete system is evaluated extensively for several speech and music signals as well as for different feedback scenarios in simulations with feedback paths measured in concrete applications as well as for real-time simulations with hearing aid dummies. Both use hearing loss compensation methods as applied in physical hearing aids. The performance is measured in terms of being able to prevent entrainment and to react to feedback path changes. For both simulation setups the system shows a good performance with respect to the two performance measures. Furthermore, the overall feedback cancellation method relies only on few parameters, shows a low computational complexity, and therefore has a strong practical relevance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2322–2333},
numpages = {12},
keywords = {normalized least mean square (NLMS) filter adaptation, hearing aids, prediction error filter, variable step size, adaptation control, adaptive feedback cancellation, frequency shift}
}

@article{10.1109/TASLP.2015.2468066,
author = {Sheaffer, Jonathan and Van Walstijn, Maarten and Rafaely, Boaz and Kowalczyk, Konrad},
title = {Binaural Reproduction of Finite Difference Simulations Using Spherical Array Processing},
year = {2015},
issue_date = {December 2015},
publisher = {IEEE Press},
volume = {23},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2468066},
doi = {10.1109/TASLP.2015.2468066},
abstract = {Due to its efficiency and simplicity, the finite-difference time-domain method is becoming a popular choice for solving wideband, transient problems in various fields of acoustics. So far, the issue of extracting a binaural response from finite difference simulations has only been discussed in the context of embedding a listener geometry in the grid. In this paper, we propose and study a method for binaural response rendering based on a spatial decomposition of the sound field. The finite difference grid is locally sampled using a volumetric array of receivers, from which a plane wave density function is computed and integrated with free-field head related transfer functions, in the spherical harmonics domain. The volumetric array is studied in terms of numerical robustness and spatial aliasing. Analytic formulas that predict the performance of the array are developed, facilitating spatial resolution analysis and numerical binaural response analysis for a number of finite difference schemes. Particular emphasis is placed on the effects of numerical dispersion on array processing and on the resulting binaural responses. Our method is compared to a binaural simulation based on the image method. Results indicate good spatial and temporal agreement between the two methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2125–2135},
numpages = {11},
keywords = {finite difference methods, room acoustics, sound reproduction, simulation, finite-difference time-do-main (FDTD), binaural processing, microphone arrays}
}

@article{10.1109/TASLP.2015.2450497,
author = {Tran, Dung T. and Vincent, Emmanuel and Jouvet, Denis},
title = {Nonparametric Uncertainty Estimation and Propagation for Noise Robust ASR},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2450497},
doi = {10.1109/TASLP.2015.2450497},
abstract = {We consider the framework of uncertainty propagation for automatic speech recognition (ASR) in highly nonstationary noise environments. Uncertainty is considered as the variance of speech distortion. Yet, its accurate estimation in the spectral domain and its propagation to the feature domain remain difficult. Existing methods typically rely on a single uncertainty estimator and propagator fixed by mathematical approximation. In this paper, we propose a new paradigm where we seek to learn more powerful mappings to predict uncertainty from data. We investigate two such possible mappings: linear fusion of multiple uncertainty estimators/propagators and nonparametric uncertainty estimation/propagation. In addition, a procedure to propagate the estimated spectral-domain uncertainty to the static Mel frequency cepstral coefficients (MFCCs), to the log-energy, and to their first- and second-order time derivatives is proposed. This results in a full uncertainty covariance matrix over both static and dynamic MFCCs. Experimental evaluation on Tracks 1 and 2 of the 2nd CHiME Challenge resulted in up to 29% and 28% relative keyword error rate reduction with respect to speech enhancement alone.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1835–1846},
numpages = {12},
keywords = {nonparametric estimation, uncertainty estimation, uncertainty decoding, robust speech recognition}
}

@article{10.1109/TASLP.2015.2456413,
author = {Tu, Mei and Zhou, Yu and Zong, Chengqing},
title = {Exploring Diverse Features for Statistical Machine Translation Model Pruning},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456413},
doi = {10.1109/TASLP.2015.2456413},
abstract = {In phrase-based and hierarchical phrase-based statistical machine translation systems, translation performance depends heavily on the size and quality of the translation table. To meet the requirements of making a real-time response, some research has been performed to filter the translation table. However, most existing methods are always based on one or two constraints that act as hard rules, such as not allowing phrase-pairs with low translation probabilities. These approaches sometimes make constraints rigid because they consider only a single factor instead of composite factors. Based on the considerations above, in this paper, we propose a machine learning-based framework that integrates multiple features for translation model pruning. Experimental results show that our framework is effective by pruning 80% of the phrase-pairs and 70% of the hierarchical rules, while retaining the quality of the translation models when using the BLEU evaluation metric. Our study further shows that our method can select the most useful phrase-pairs and rules, including those that are low in frequency but still very useful.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1847–1857},
numpages = {11},
keywords = {syntactic constraints, statistical machine translation (SMT), classification, translation model pruning}
}

@article{10.1109/TASLP.2015.2456426,
author = {Okopal, Greg and Wisdom, Scott and Atlas, Les},
title = {Speech Analysis with the Strong Uncorrelating Transform},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456426},
doi = {10.1109/TASLP.2015.2456426},
abstract = {The strong uncorrelating transform (SUT) provides estimates of independent components from linear mixtures using only second-order information, provided that the components have unique circularity coefficients. We propose a processing framework for generating complex-valued subbands from real-valued mixtures of speech and noise where the objective is to control the likely values of the sample circularity coefficients of the underlying speech and noise components in each subband. We show how several processing parameters affect the noncircularity of speech-like and noise components in the subband, ultimately informing parameter choices that allow for estimation of each of the components in a subband using the SUT. Additionally, because the speech and noise components will have unique sample circularity coefficients, this statistic can be used to identify time--frequency regions that contain voiced speech. We give an example of the recovery of the circularity coefficients of a real speech signal from a two-channel noisy mixture at 25 dB SNR, which demonstrates how the estimates of noncircularity can reveal the time-frequency structure of a speech signal in very high levels of noise. Finally, we present the results of a voice activity detection (VAD) experiment showing that two new circularity-based statistics, one of which is derived from the SUT processing, can achieve improved performance over state-of-the-art VADs in real-world recordings of noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1858–1868},
numpages = {11},
keywords = {short-time fourier transform (STFT), voice activity detection (VAD), noncircularity, improper, circularity coefficients, speech processing}
}

@article{10.1109/TASLP.2015.2447284,
author = {Zhao, Xiaojia and Wang, Yuxuan and Wang, DeLiang},
title = {Cochannel Speaker Identification in Anechoic and Reverberant Conditions},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2447284},
doi = {10.1109/TASLP.2015.2447284},
abstract = {Speaker identification (SID) in cochannel speech, where two speakers are talking simultaneously over a single recording channel, is a challenging problem. Previous studies address this problem in the anechoic environment under the Gaussian mixture model (GMM) framework. On the other hand, cochannel SID in reverberant conditions has not been addressed. This paper studies cochannel SID in both anechoic and reverberant conditions. We first investigate GMM-based approaches and propose a combined system that integrates two cochannel SID methods. Second, we explore deep neural networks (DNNs) for cochannel SID and propose a DNN-based recognition system. Evaluation results demonstrate that our proposed systems significantly improve SID performance over recent approaches in both anechoic and reverberant conditions and various target-to-interferer ratios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1727–1736},
numpages = {10},
keywords = {gaussian mixture model (GMM), cochannel speaker identification, reverberation, target-to-interferer ratio, deep neural network (DNN)}
}

@article{10.1109/TASLP.2015.2456423,
author = {Chen, Zhangli and Hohmann, Volker},
title = {Online Monaural Speech Enhancement Based on Periodicity Analysis and <i>a Priori</i> SNR Estimation},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456423},
doi = {10.1109/TASLP.2015.2456423},
abstract = {This paper describes an online algorithm for enhancing monaural noisy speech. First, a novel phase-corrected low-delay gammatone filterbank is derived for signal subband decomposition and resynthesis; the subband signals are then analyzed frame by frame. Second, a novel feature named periodicity degree (PD) is proposed to be used for detecting and estimating the fundamental period (Po) in each frame and for estimating the signal-to-noise ratio (SNR) in each frame-subband signal unit. The PD is calculated in each unit as the multiplication of the normalized autocorrelation and the comb filter ratio, and shown to be robust in various low-SNR conditions. Third, the noise energy level in each signal unit is estimated recursively based on the estimated SNR for units with high PD and based on the noisy signal energy level for units with low PD. Then the a priori SNR is estimated using a decision-directed approach with the estimated noise level. Finally, a revised Wiener gain is calculated, smoothed, and applied to each unit; the processed units are summed across subbands and frames to form the enhanced signal. The Po detection accuracy of the algorithm was evaluated on two corpora and showed comparable performance on one corpus and better performance on the other corpus when compared to a recently published pitch detection algorithm. The speech enhancement effect of the algorithm was evaluated on one corpus with two objective criteria and showed better performance in one highly non-stationary noise and comparable performance in two other noises when compared to a state-of-the-art statistical-model based algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1904–1916},
numpages = {13},
keywords = {periodicity analysis, a priori signal-to-noise ratio (SNR) estimation, online implementation, monaural speech enhancement}
}

@article{10.1109/TASLP.2015.2469142,
author = {Pan, Chao and Chen, Jingdong and Benesty, Jacob},
title = {Theoretical Analysis of Differential Microphone Array Beamforming and an Improved Solution},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2469142},
doi = {10.1109/TASLP.2015.2469142},
abstract = {Differential microphone arrays (DMAs), which are responsive to the differential sound pressure field, have attracted much attention due to their properties of frequency-invariant beampatterns, small apertures, and potential of maximum directivity. Traditionally, DMAs are designed and implemented in a multistage (cascade) way, where a proper time delay is used in each stage to form a beampattern of interest. Recently, it was reported that DMAs can be designed by solving a linear system of equations formed from the information about the nulls of the desired beampattern. This paper deals with the problem of beamforming with linear DMAs. Its major contributions are as follows. 1) By using the spatial Z transform, we present some theoretical analysis of both the traditional cascade and new null-constrained DMA beamforming. It is shown that the cascade and null-constrained DMAs of the same order with the same number of sensors are theoretically identical. 2) We develop a two-stage approach to the study of the robust DMA beamformer, which is based on the principle of maximizing the white noise gain (WNG). The first-stage of this approach is in the structure of the traditional non-robust DMA while the second-stage filter is optimized for improving the WNG. 3) Using the two-stage approach, we show that the robust DMA beamformer may introduce extra nulls in the beampattern at high frequencies; particularly, it introduces M -- N -- 1 extra nulls if the interelement spacing is equal to half of the wavelength, where M and N are the number of sensors and the DMA order, respectively. 4) We develop a method that can solve the extra-null problem while maximizing the WNG in robust DMA beamforming, i.e., a robust solution with a frequency-invariant beampattern.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2093–2105},
numpages = {13},
keywords = {directivity factor, differential microphone arrays (DMAs), microphone arrays, directivity pattern, frequency-invariant beampattern, differential beamforming, robust DMAs, white noise gain}
}

@article{10.1109/TASLP.2015.2464678,
author = {Cumani, Sandro},
title = {Fast Scoring of Full Posterior PLDA Models},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2464678},
doi = {10.1109/TASLP.2015.2464678},
abstract = {A low-dimensional representation of a speech segment, the so-called i-vector, in combination with probabilistic linear discriminant analysis (PLDA) models, is the current state-of-the-art in speaker recognition. An i-vector is a compact representation of a Gaussian Mixture Model (GMM) supervector, which captures most of the GMM supervectors variability. It is usually obtained by a MAP estimate of the mean of a posterior distribution. A new PLDA model has been recently presented that, unlike the standard one, exploits the intrinsic i-vector uncertainty. This approach, referred to in this paper as Full Posterior Distribution PLDA (FP-PLDA), is particularly effective for speaker detection of short and variable duration speech segments. It is, however, computationally far more expensive than standard PLDA, making it unattractive for real applications. This paper presents three simplifications of FP-PLDA based on approximate diagonalizations of matrices involved in FP-PLDA scoring. Using in sequence these approximations allows obtaining computational costs comparable to PLDA models, with only a small performance degradation with respect to the more accurate, but less efficient, FP-PLDA models. In particular, up to 10% better performance than PLDA is obtained, with similar computational complexity, on short speech segments of variable duration, randomly extracted from the interviews and telephone conversations included in the NIST SRE 2010 extended dataset. The benefits of the proposed diagonalization approaches have also been confirmed on a short utterance text-independent verification task, where approximately 43% and 34% improvement of the EER and minimum DCF08, respectively, has been obtained with respect to PLDA.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2036–2045},
numpages = {10},
keywords = {speaker recognition, i-vector extraction, probabilistic linear discriminant analysis (PLDA), i-vectors}
}

@article{10.1109/TASLP.2015.2456428,
author = {Sim\'{o}n G\'{a}lvez, Marcos F. and Elliott, Stephen J. and Cheer, Jordan},
title = {Time Domain Optimization of Filters Used in a Loudspeaker Array for Personal Audio},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456428},
doi = {10.1109/TASLP.2015.2456428},
abstract = {This paper describes a time domain design method for calculating the coefficients of FIR filters used to drive a loudspeaker array for personal audio. A motivating application is to boost the television audio in a certain spatial region with the aim of increasing the speech intelligibility of the hearing impaired. As the array is of small size, superdirective beamforming is applied to increase the low and mid frequency directional performance of the radiator. The filters for such arrays have previously been designed one frequency at a time, leading to non-causal filters that require a modeling delay for real time implementation. By posing the filter optimization in the time domain, the filter responses can be causally constrained, and the optimization is performed once for all frequencies. This paper considers the performance of such filters by carrying out off-line simulations, firstly using the impulse responses of point sources in the free field, and then with the measured responses of a loudspeaker array in an anechoic chamber. The simulation results show how the time domain optimization allows the creation of filters with either a low order or a low modeling delay.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1869–1878},
numpages = {10},
keywords = {personal audio, time domain optimization, array signal processing}
}

@article{10.1109/TASLP.2015.2456417,
author = {Wu, Chung-Hsien and Shen, Han-Ping and Hsu, Chun-Shan},
title = {Code-Switching Event Detection by Using a Latent Language Space Model and the Delta-Bayesian Information Criterion},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456417},
doi = {10.1109/TASLP.2015.2456417},
abstract = {This paper proposes a new paradigm for code-switching event detection based on latent language space models (LLSMs) and the delta-Bayesian information criterion (ΔBIC). A phone-based Mandarin--English speech recognizer was first employed for obtaining the senone sequence of a speech utterance. For each senone, acoustic features and the posterior probability of the articulatory features (AFs) were extracted and applied to an eigenspace transformation, based on principal component analysis (PCA). Latent semantic analysis (LSA) was then adopted for constructing a matrix to model the importance of each principal component in the eigenspace for the senones and AFs in each language. The spatial relationships among the senones (or AFs) represented by the PCA-transformed eigenvalues in the LSA-based matrix were employed to construct an LLSM for characterizing a language. In code-switching event detection, the language likelihood between the input speech LLSM and each of the language-dependent LLSMs was estimated. The Euclidian-distance-based similarities and cosine-angle-distance-based similarities were adopted for estimating the language likelihood for senones and AFs. The ΔBIC was then used for estimating the language transition score for each hypothesized code-switching event. Finally, the dynamic programming algorithm was employed for obtaining the most likely code-switching language sequence. The proposed approach was evaluated using a Mandarin--English code-switching speech database and outperformed other conventional methods. A duration accuracy of 72.45% can be obtained from the proposed system with optimized parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1892–1903},
numpages = {12},
keywords = {delta-Bayesian information criterion (ΔBIC), articulatory features, senones, code-switching event detection, latent language space model}
}

@article{10.1109/TASLP.2015.2465150,
author = {Hirao, Tsutomu and Nishino, Masaaki and Yoshida, Yasuhisa and Suzuki, Jun and Yasuda, Norihito and Nagata, Masaaki},
title = {Summarizing a Document by Trimming the Discourse Tree},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2465150},
doi = {10.1109/TASLP.2015.2465150},
abstract = {Recent studies on extractive text summarization formulate it as a combinatorial optimization problem, extracting the optimal subset from a set of the textual units that maximizes an objective function without violating the length constraint. Although these methods successfully improve automatic evaluation scores, they do not consider the discourse structure in the source document. Thus, summaries generated by these methods may lack logical coherence. In previous work, we proposed a method that exploits a discourse tree structure to produce coherent summaries. By transforming a traditional discourse tree, namely a rhetorical structure theory-based discourse tree (RST-DT), into a dependency-based discourse tree (DEP-DT), we formulated the summarization procedure as a Tree Knapsack Problem whose tree corresponds to the DEP-DT. This paper extends the work with a detailed discussion of the approach together with a novel efficient dynamic programming algorithm for solving the Tree Knapsack Problem. Experiments show that our method not only achieved the highest score in both automatic and human evaluation, but also obtained good performance in terms of the linguistic qualities of the summaries.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2081–2092},
numpages = {12},
keywords = {tree knapsack problem, single-document summarization, discourse analysis}
}

@article{10.1109/TASLP.2015.2458585,
author = {Deng, Feng and Bao, Changchun and Kleijn, W. Bastiaan},
title = {Sparse Hidden Markov Models for Speech Enhancement in Non-Stationary Noise Environments},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2458585},
doi = {10.1109/TASLP.2015.2458585},
abstract = {We propose a sparse hidden Markov model (HMM)- based single-channel speech enhancement method that models the speech and noise gains accurately in non-stationary noise environments. Autoregressive models are employed to describe the speech and noise in a unified framework and the speech and noise gains are modeled as random processes with memory. The likelihood criterion for finding the model parameters is augmented with an lp regularization term resulting in a sparse autoregressive HMM (SARHMM) system that encourages sparsity in the speech- and noise-modeling. In the SARHMM only a small number of HMM states contribute significantly to the model of each particular observed speech segment. As it eliminates ambiguity between noise and speech spectra, the sparsity of speech and noise modeling helps to improve the tracking of the changes of both spectral shapes and power levels of non-stationary noise. Using the modeled speech and noise SARHMMs, we first construct a noise estimator to estimate the noise power spectrum. Then, a Bayesian speech estimator is derived to obtain the enhanced speech signal. The subjective and objective test results indicate that the proposed speech enhancement scheme can achieve a larger segmental SNR improvement, a lower log-spectral distortion and a better speech quality in stationary noise conditions than state-of-the-art reference methods. The advantage of the new method is largest for non-stationary noise conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1973–1987},
numpages = {15},
keywords = {non-stationary noise, sparse autoregressive hidden Markov model (ARHMM), gain modeling, speech enhancement}
}

@article{10.1109/TASLP.2015.2449088,
author = {Aragonda, Haricharan and Seelamantula, Chandra Sekhar},
title = {Demodulation of Narrowband Speech Spectrograms Using the Riesz Transform},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2449088},
doi = {10.1109/TASLP.2015.2449088},
abstract = {We propose a two-dimensional (2-D) multicomponent amplitude-modulation, frequency-modulation (AM-FM) model for a spectrogram patch corresponding to voiced speech, and develop a new demodulation algorithm to effectively separate the AM, which is related to the vocal tract response, and the carrier, which is related to the excitation. The demodulation algorithm is based on the Riesz transform and is developed along the lines of Hilbert-transform-based demodulation for 1-D AM-FM signals. We compare the performance of the Riesz transform technique with that of the sinusoidal demodulation technique on real speech data. Experimental results show that the Riesz-transform-based demodulation technique represents spectrogram patches accurately. The spectrograms reconstructed from the demodulated AM and carrier are inverted and the corresponding speech signal is synthesized. The signal-to-noise ratio (SNR) of the reconstructed speech signal, with respect to clean speech, was found to be 2 to 4 dB higher in case of the Riesz transform technique than the sinusoidal demodulation technique.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1824–1834},
numpages = {11},
keywords = {Riesz transform, grating compression transform (GCT), multiband AM-FM, sinusoidal demodulation, amplitude modulation model of spectrograms, spectro-temporal analysis}
}

@article{10.1109/TASLP.2015.2464692,
author = {Chu, Y. J. and Chan, S. C.},
title = {A New Local Polynomial Modeling-Based Variable Forgetting Factor RLS Algorithm and Its Acoustic Applications},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2464692},
doi = {10.1109/TASLP.2015.2464692},
abstract = {This paper proposes a new class of local polynomial modeling (LPM)-based variable forgetting factor (VFF) recursive least squares (RLS) algorithms called the LPM-based VFF RLS (LVFF-RLS) algorithms. It models the time-varying channel coefficients as local polynomials so as to obtain the expressions of the bias and variance terms in the mean square error (MSE) of the RLS algorithm. A new locally optimal VFF (LOVFF) is then derived by minimizing the resulting MSE and the theoretical analysis is found to be in good agreement with experimental results. Methods for estimating the parameters involved in this LOVFF are also developed, resulting in an improved RLS algorithm with VFF. The algorithm is further extended to include variable regularization and a QR decomposition (QRD) version which is numerically more stable and amenable to multiplier-less implementation using coordinate rotation digital computer (CORDIC) algorithm. Applications of these algorithms to frequency estimation and adaptive beamforming in time-varying speech and audio signals are also presented to illustrate the effectiveness of the proposed algorithms. Simulations show that the convergence and tracking performance of the proposed algorithms compare favorably with conventional algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2059–2069},
numpages = {11},
keywords = {variable forgetting factor (VFF), local polynomial modeling (LPM), recursive least square (RLS), variable regularization, mean square error (MSE) analysis, adaptive beamforming, frequency estimation}
}

@article{10.1109/TASLP.2015.2461448,
author = {Chen, Ling-Hui and Raitio, Tuomo and Valentini-Botinhao, Cassia and Ling, Zhen-Hua and Yamagishi, Junichi},
title = {A Deep Generative Architecture for Postfiltering in Statistical Parametric Speech Synthesis},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2461448},
doi = {10.1109/TASLP.2015.2461448},
abstract = {The generated speech of hidden Markov model (HMM)-based statistical parametric speech synthesis still sounds "muffled." One cause of this degradation in speech quality may be the loss of fine spectral structures. In this paper, we propose to use a deep generative architecture, a deep neural network (DNN) generatively trained, as a postfilter. The network models the conditional probability of the spectrum of natural speech given that of synthetic speech to compensate for such gap between synthetic and natural speech. The proposed probabilistic postfilter is generatively trained by cascading two restricted Boltzmann machines (RBMs) or deep belief networks (DBNs) with one bidirectional associative memory (BAM). We devised two types of DNN postfilters: one operating in the mel-cepstral domain and the other in the higher dimensional spectral domain. We compare these two new data-driven postfilters with other types of postfilters that are currently used in speech synthesis: a fixed mel-cepstral based postfilter, the global variance based parameter generation, and the modulation spectrum-based enhancement. Subjective evaluations using the synthetic voices of a male and female speaker confirmed that the proposed DNN-based postfilter in the spectral domain significantly improved the segmental quality of synthetic speech compared to that with conventional methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2003–2014},
numpages = {12},
keywords = {modulation spectrum, hidden Markov model (HMM), postfilter, deep generative architecture, segmental quality, speech synthesis}
}

@article{10.1109/TASLP.2015.2449083,
author = {Hoffmann, Falk-Martin and Fazi, Filippo Maria},
title = {Theoretical Study of Acoustic Circular Arrays with Tangential Pressure Gradient Sensors},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2449083},
doi = {10.1109/TASLP.2015.2449083},
abstract = {Microphone arrays as a means of sound field acquisition have been the topic of extensive research for more than eight decades now. A number of designs have been suggested, each trying to overcome difficulties that are inherent to either the decomposition of the sound field, the transducers in use or the presence of the array itself. This work presents a theoretical analysis of circular microphone arrays that do not measure the sound pressure but the component of its gradient that is tangential to a given boundary. Its performance is compared to that of a conventional pressure sensor array as a benchmark. The focus of the analysis and subsequent assessment lies on spatial aliasing and performance in the presence of noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1762–1774},
numpages = {13},
keywords = {microphone arrays, spatial aliasing, tangential pressure gradient, cylindrical arrays, circular arrays, transducer noise}
}

@article{10.1109/TASLP.2015.2464671,
author = {Tourbabin, Vladimir and Rafaely, Boaz},
title = {Direction of Arrival Estimation Using Microphone Array Processing for Moving Humanoid Robots},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2464671},
doi = {10.1109/TASLP.2015.2464671},
abstract = {The auditory system of humanoid robots has gained increased attention in recent years. This system typically acquires the surrounding sound field by means of a microphone array. Signals acquired by the array are then processed using various methods. One of the widely applied methods is direction of arrival estimation. The conventional direction of arrival estimation methods assume that the array is fixed at a given position during the estimation. However, this is not necessarily true for an array installed on a moving humanoid robot. The array motion, if not accounted for appropriately, can introduce a significant error in the estimated direction of arrival. The current paper presents a signal model that takes the motion into account. Based on this model, two processing methods are proposed. The first one compensates for the motion of the robot. The second method is applicable to periodic signals and utilizes the motion in order to enhance the performance to a level beyond that of a stationary array. Numerical simulations and an experimental study are provided, demonstrating that the motion compensation method almost eliminates the motion-related error. It is also demonstrated that by using the motion-based enhancement method it is possible to improve the direction of arrival estimation performance, as compared to that obtained when using a stationary array.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2046–2058},
numpages = {13},
keywords = {robot audition, rotation, microphone array, moving array, spherical harmonics, translation, direction of arrival estimation}
}

@article{10.1109/TASLP.2015.2459599,
author = {Dennis, Jonathan and Tran, Huy Dat and Li, Haizhou},
title = {Generalized Hough Transform for Speech Pattern Classification},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2459599},
doi = {10.1109/TASLP.2015.2459599},
abstract = {While typical hybrid neural network architectures for automatic speech recognition (ASR) use a context window of frame-based features, this may not be the best approach to capture the wider temporal context, which contains phonetic and linguistic information that is equally important. In this paper, we introduce a system that integrates both the spectral and geometrical shape information from the acoustic spectrum, inspired by research in the field of machine vision. In particular, we focus on the Generalized Hough Transform (GHT), which is a sophisticated technique that can model the geometrical distribution of speech information over the wider temporal context. To integrate the GHT as part of a hybrid-ASR system, we propose to use a neural network, with features derived from the probabilistic Hough voting step of the GHT, to implement an improved version of the GHT where the output of the network represents the conventional target class posteriors. A major advantage of our approach is that each step of the GHT is highly interpretable, particularly compared to deep neural network (DNN) systems which are commonly treated as powerful black-box classifiers that give little insight into how the output is achieved. Experiments are carried out on two speech pattern classification tasks. The first is the TIMIT phoneme classification, which demonstrates the performance of the approach on a standard ASR task. The second is a spoken word recognition challenge, which highlights the flexibility of the approach to capture phonetic information within a longer temporal context.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1963–1972},
numpages = {10},
keywords = {generalized hough transform, codebook activation map, TIMIT, speech pattern classification}
}

@article{10.1109/TASLP.2015.2443983,
author = {Islam, Md Tauhidul and Shahnaz, Celia and Zhu, Wei-Ping and Ahmad, M. Omair},
title = {Speech Enhancement Based on Student <i>t</i> Modeling of Teager Energy Operated Perceptual Wavelet Packet Coefficients and a Custom Thresholding Function},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2443983},
doi = {10.1109/TASLP.2015.2443983},
abstract = {This paper presents a speech enhancement approach, where an adaptive threshold is statistically determined based on Student t Modeling of Teager energy (TE) operated perceptual wavelet packet (PWP) coefficients of noisy speech. In order to obtain an enhanced speech, the threshold thus derived is applied upon the PWP coefficients by employing a Student t pdf dependent custom thresholding function, which is designed based on a combination of modified hard and semisoft thresholding functions. Extensive simulations are carried out using the NOIZEUS database to evaluate the effectiveness of the proposed method for car and multi-talker babble noise corrupted speech signals. Several standard objective measures and subjective evaluations including formal listening tests show that the proposed method outperforms some of the state-of-the-art speech enhancement methods at high as well as low levels of SNRs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1800–1811},
numpages = {12},
keywords = {student t, speech enhancement, perceptual wavelet packet transform, Teager energy (TE), Kullback-Liebler divergence}
}

@article{10.1109/TASLP.2015.2450494,
author = {Souvira\`{a}-Labastie, Nathan and Olivero, Anaik and Vincent, Emmanuel and Bimbot, Fr\'{e}d\'{e}ric},
title = {Multi-Channel Audio Source Separation Using Multiple Deformed References},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2450494},
doi = {10.1109/TASLP.2015.2450494},
abstract = {We present a general multi-channel source separation framework where additional audio references are available for one (or more) source(s) of a given mixture. Each audio reference is another mixture which is supposed to contain at least one source similar to one of the target sources. Deformations between the sources of interest and their references are modeled in a linear manner using a generic formulation. This is done by adding transformation matrices to an excitation-filter model, hence affecting different axes, namely frequency, dictionary component or time. A nonnegative matrix co-factorization algorithm and a generalized expectation-maximization algorithm are used to estimate the parameters of the model. Different model parameterizations and different combinations of algorithms are tested on music plus voice mixtures guided by music and/or voice references and on professionally-produced music recordings guided by cover references. Our algorithms improve the signal-to-distortion ratio (SDR) of the sources with the lowest intensity by 9 to 15 decibels (dB) with respect to original mixtures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1775–1787},
numpages = {13},
keywords = {generalized expectation-maximization (GEM) algorithm, source separation}
}

@article{10.1109/TASLP.2015.2449072,
author = {Ngoc Do, Quynh Thi and Bethard, Steven and Moens, Marie-Francine},
title = {Domain Adaptation in Semantic Role Labeling Using a Neural Language Model and Linguistic Resources},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2449072},
doi = {10.1109/TASLP.2015.2449072},
abstract = {We propose a method for adapting Semantic Role Labeling (SRL) systems from a source domain to a target domain by combining a neural language model and linguistic resources to generate additional training examples. We primarily aim to improve the results of Location, Time, Manner and Direction roles. In our methodology, main words of selected predicates and arguments in the source-domain training data are replaced with words from the target domain. The replacement words are generated by a language model and then filtered by several linguistic filters (including Part-Of-Speech (POS), WordNet and Predicate constraints). In experiments on the out-of-domain CoNLL 2009 data, with the Recurrent Neural Network Language Model (RNNLM) and a well-known semantic parser from Lund University, we show enhanced recall and F1 without penalizing precision on the four targeted roles. These results improve the results of the same SRL system without using the language model and the linguistic resources, and are better than the results of the same SRL system that is trained with examples that are enriched with word embeddings. We also demonstrate the importance of using a language model and the vocabulary of the target domain when generating new training examples.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1812–1823},
numpages = {12},
keywords = {open domain, semantic role labeling, language model, linguistic resources}
}

@article{10.1109/TASLP.2015.2464691,
author = {de-la-Calle-Silos, Fernando and Valverde-Albacete, Francisco J. and Gallardo-Antol\'{\i}n, Ascensi\'{o}n and Pel\'{a}ez-Moreno, Carmen},
title = {Morphologically Filtered Power-Normalized Cochleograms as Robust, Biologically Inspired Features for ASR},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2464691},
doi = {10.1109/TASLP.2015.2464691},
abstract = {In this paper, we present advances in the modeling of the masking behavior of the human auditory system (HAS) to enhance the robustness of the feature extraction stage in automatic speech recognition (ASR). The solution adopted is based on a nonlinear filtering of a spectro-temporal representation applied simultaneously to both frequency and time domains---as if it were an image---using mathematical morphology operations. A particularly important component of this architecture is the so-called structuring element (SE) that in the present contribution is designed as a single three-dimensional pattern using physiological facts, in such a way that closely resembles the masking phenomena taking place in the cochlea. A proper choice of spectro-temporal representation lends validity to the model throughout the whole frequency spectrum and intensity spans assuming the variability of the masking properties of the HAS in these two domains. The best results were achieved with the representation introduced as part of the power normalized cepstral coefficients (PNCC) together with a spectral subtraction step. This method has been tested on Aurora 2, Wall Street Journal and ISOLET databases including both classical hidden Markov model (HMM) and hybrid artificial neural networks (ANN)-HMM back-ends. In these, the proposed front-end analysis provides substantial and significant improvements compared to baseline techniques: up to 39.5% relative improvement compared to MFCC, and 18.7% compared to PNCC in the Aurora 2 database.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2070–2080},
numpages = {11},
keywords = {power normalized cepstral coefficients (PNCC), auditory-based features, cochlear masking models, spectro-temporal processing, automatic speech recognition (ASR), morphological filtering}
}

@article{10.1109/TASLP.2015.2446202,
author = {Shin, Ho Seon and Fingscheidt, Tim and Kang, Hong-Goo},
title = {<i>A Priori</i> SNR Estimation Using Air- and Bone-Conduction Microphones},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2446202},
doi = {10.1109/TASLP.2015.2446202},
abstract = {This paper proposes an a priori signal-to-noise ratio (SNR) estimator using an air-conduction (AC) and a bone-conduction (BC) microphone. Among various ways of combining AC and BC microphones for speech enhancement, it is shown that the total enhancement performance can be maximized if the BC microphone is utilized for estimating the power spectral density (PSD) of the desired speech signal. Considering the fact that a small deviation in the speech PSD estimation process brings severe spectral distortion, this paper focuses on controlling weighting factors while estimating the a priori SNR with the decision-directed approach framework. The time--frequency varying weighting factor that is determined by taking a minimum mean square error criterion improves the capability of eliminating residual noise and minimizing speech distortion. Since the weighting factors are also adjusted by measuring the usefulness of the AC and BC microphones, the proposed approach is suitable for tracking the parameter even if the characteristic of environment changes rapidly. The simulation results confirm the superiority of the proposed algorithm to conventional algorithms in high noise environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2015–2025},
numpages = {11},
keywords = {speech enhancement, bone-conduction (BC) microphone, a priori signal-to-noise ratio (SNR)}
}

@article{10.1109/TASLP.2015.2460459,
author = {Ranjan, Rishabh and Gan, Woon-Seng},
title = {Natural Listening over Headphones in Augmented Reality Using Adaptive Filtering Techniques},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2460459},
doi = {10.1109/TASLP.2015.2460459},
abstract = {Augmented reality (AR), which composes of virtual and real world environments, is becoming one of the major topics of research interest due to the advent of wearable devices. Today, AR is commonly used as assistive display to enhance the perception of reality in education, gaming, navigation, sports, entertainment, simulators, etc. However, most of the past works have mainly concentrated on the visual aspects of AR. Auditory events are one of the essential components in human perceptions in daily life but the augmented reality solutions have been lacking in this regard till now compared to visual aspects. Therefore, there is a need of natural listening in AR systems to give a holistic experience to the user. A new headphones configuration is presented in this work with two pairs of binaural microphones attached to headphones (one internal and one external microphone on each side). This paper focuses on enabling natural listening using open headphones employing adaptive filtering techniques to equalize the headset such that virtual sources are perceived as close as possible to sounds emanating from the physical sources. This would also require a superposition of virtual sources with the physical sound sources, as well as ambience. Modified versions of the filtered-x normalized least mean square algorithm (FxNLMS) are proposed in the paper to converge faster to the optimum solution as compared to the conventional FxNLMS. Measurements are carried out with open structure type headphones to evaluate their performance. Subjective test was conducted using individualized binaural room impulse responses (BRIRs) to evaluate the perceptual similarity between real and virtual sounds.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1988–2002},
numpages = {15},
keywords = {natural listening, head related transfer function (HRTF), augmented reality (AR), spatial audio, adaptive filtering}
}

@article{10.1109/TASLP.2015.2449089,
author = {Chen, Liang-Yu and Jang, Jyh-Shing Roger},
title = {Automatic Pronunciation Scoring with Score Combination by Learning to Rank and Class-Normalized DP-Based Quantization},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2449089},
doi = {10.1109/TASLP.2015.2449089},
abstract = {This paper proposes an automatic pronunciation scoring framework using learning to rank and class-normalized, dynamic-programming-based quantization. The goal is to train a model that is able to grade the pronunciation of a second language learner, such that the predicted score is as close as possible to the one given by a human teacher. Under this framework, each utterance is given a score of 1 to 5 by human raters, which is treated as a ground truth rank for the training algorithm. The corpus was rated by qualified English teachers in Taiwan (nonnative speakers). Nine phone-level scores are computed and converted into word-level scores through four conversion methods. We select the 16 best performing scores as the input features to train the learning-to-rank function. The output of the function is then quantized to a discrete rank on a 1-5 scale. The quantization is done with class normalization to alleviate the problem of data imbalance over different classes. Experimental results show that the proposed framework achieves a higher correlation to the human scores than other methods, along with higher accuracy in detecting instances of mispronunciation. We also release a new version of our nonnative corpus with human rankings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1737–1749},
numpages = {13},
keywords = {automatic pronunciation scoring, learning to rank, computer assisted language learning (CALL), computer assisted pronunciation training (CAPT)}
}

@article{10.1109/TASLP.2015.2456431,
author = {Sarreshtedari, Saeed and Akhaee, Mohammad Ali and Abbasfar, Aliazam},
title = {A Watermarking Method for Digital Speech Self-Recovery},
year = {2015},
issue_date = {November 2015},
publisher = {IEEE Press},
volume = {23},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2456431},

license_type: permissive

branch_name: refs/heads/main

extension: bib