text:
,
pages = {1126–1139},
numpages = {14}
}

@article{10.1109/TASLP.2018.2813011,
author = {Leglaive, Simon and Badeau, Roland and Richard, Gael},
title = {Student's t Source and Mixing Models for Multichannel Audio Source Separation},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2813011},
doi = {10.1109/TASLP.2018.2813011},
abstract = {This paper presents a Bayesian framework for under-determined audio source separation in multichannel reverberant mixtures. We model the source signals as Student's t latent random variables in a time-frequency domain. The specific structure of musical signals in this domain is exploited by means of a nonnegative matrix factorization model. Conversely, we design the mixing model in the time domain. In addition to leading to an exact representation of the convolutive mixing process, this approach allows us to develop simple probabilistic priors for the mixing filters. Indeed, as those filters correspond to room responses they exhibit a simple characteristic structure in the time domain that can be used to guide their estimation. We also rely on the Student's t distribution for modeling the impulse response of the mixing filters. From this model, we develop a variational inference algorithm in order to perform source separation. The experimental evaluation demonstrates the potential of this approach for separating multichannel reverberant mixtures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1150–1164},
numpages = {15}
}

@article{10.1109/TASLP.2018.2806745,
author = {Benaroya, Elie Laurent and Obin, Nicolas and Liuni, Marco and Roebel, Axel and Raumel, Wilson and Argentieri, Sylvain},
title = {Binaural Localization of Multiple Sound Sources by Non-Negative Tensor Factorization},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2806745},
doi = {10.1109/TASLP.2018.2806745},
abstract = {This paper presents non-negative factorization of audio signals for the binaural localization of multiple sound sources within realistic and unknown sound environments. Non-negative tensor factorization NTF provides a sparse representation of multichannel audio signals in time, frequency, and space that can be exploited in computational audio scene analysis and robot audition for the separation and localization of sound sources. In the proposed formulation, each sound source is represented by means of spectral dictionaries, temporal activation, and its distribution within each channel here, left and right ears. This distribution, being dependent on the frequency, can be interpreted as an explicit estimation of the Head-Related Transfer Function HRTF of a binaural head which can then be converted into the estimated sound source position. Moreover, the semisupervised formulation of the non-negative factorization allows us to integrate prior knowledge about some sound sources of interest whose dictionaries can be learned in advance, whereas the remaining sources are considered as background sound, which remains unknown and is estimated on the fly. The proposed NTF-based sound source localization is applied here to binaural sound source localization of multiple speakers within realistic sound environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1068–1078},
numpages = {11}
}

@article{10.1109/TASLP.2018.2809864,
author = {Perraudin, Nathanael and Holighaus, Nicki and Majdak, Piotr and Balazs, Peter},
title = {Inpainting of Long Audio Segments With Similarity Graphs},
year = {2018},
issue_date = {June 2018},
publisher = {IEEE Press},
volume = {26},
number = {6},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2809864},
doi = {10.1109/TASLP.2018.2809864},
abstract = {We present a novel method for the compensation of long duration data loss in audio signals, in particular music. The concealment of such signal defects is based on a graph that encodes signal structure in terms of time-persistent spectral similarity. A suitable candidate segment for the substitution of the lost content is proposed by an intuitive optimization scheme and smoothly inserted into the gap, i.e., the lost or distorted signal region. Extensive listening tests show that the proposed algorithm provides highly promising results when applied to a variety of real-world music signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1079–1090},
numpages = {12}
}

@article{10.1109/TASLP.2018.2798811,
author = {Ling, Zhen-Hua and Ai, Yang and Gu, Yu and Dai, Li-Rong},
title = {Waveform Modeling and Generation Using Hierarchical Recurrent Neural Networks for Speech Bandwidth Extension},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2798811},
doi = {10.1109/TASLP.2018.2798811},
abstract = {This paper presents a waveform modeling and generation method using hierarchical recurrent neural networks HRNN for speech bandwidth extension BWE. Different from conventional BWE methods that predict spectral parameters for reconstructing wideband speech waveforms, this BWE method models and predicts waveform samples directly without using vocoders. Inspired by SampleRNN, which is an unconditional neural audio generator, the HRNN model represents the distribution of each wideband or high-frequency waveform sample conditioned on the input narrowband waveform samples using a neural network composed of long short-term memory LSTM layers and feed-forward layers. The LSTM layers form a hierarchical structure and each layer operates at a specific temporal resolution to efficiently capture long-span dependencies between temporal sequences. Furthermore, additional conditions, such as the bottleneck features derived from narrowband speech using a deep neural network based state classifier, are employed as auxiliary input to further improve the quality of generated wideband speech. The experimental results of comparing several waveform modeling methods show that the HRNN-based method can achieve better speech quality and run-time efficiency than the dilated convolutional neural network based method and the plain sample-level recurrent neural network based method. Our proposed method also outperforms the conventional vocoder-based BWE method using LSTM-RNNs in terms of the subjective quality of the reconstructed wideband speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {883–894},
numpages = {12}
}

@article{10.1109/TASLP.2018.2800290,
author = {Chang, Jiho and Marschall, Marton},
title = {Periphony-Lattice Mixed-Order Ambisonic Scheme for Spherical Microphone Arrays},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2800290},
doi = {10.1109/TASLP.2018.2800290},
abstract = {Most methods for sound field reconstruction and spherical beamforming with spherical microphone arrays are mathematically based on the spherical harmonics expansion. In many cases, this expansion is truncated at a certain order as in higher order ambisonics HOA. This truncation leads to performance that is independent of the incident direction of the sound waves. On the other hand, mixed-order ambisonic MOA schemes that select an appropriate subset of spherical harmonics can improve the performance for horizontal directions at the expense of other directions. This paper proposes an MOA scheme called Periphony-Lattice to improve sound field reconstruction performance for horizontally incident sound waves. The proposed scheme is compared with the previously introduced MOA and HOA schemes in terms of theoretical truncation error and performance in sound field reconstruction and spherical beamforming. Computer simulations and measurements are conducted with a spherical array of 52 microphones with a nonuniform layout. The results show that the proposed MOA scheme has better performance in sound field reconstruction and spherical beamforming for horizontal sound waves than the other schemes for a given number of microphones. This scheme can be applied to other spherical array layouts if the number of microphones is greater than that of the required spherical harmonics coefficients, and may improve the horizontal performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {924–936},
numpages = {13}
}

@article{10.1109/TASLP.2018.2800283,
author = {Zheng, Chengshi and Deleforge, Antoine and Li, Xiaodong and Kellermann, Walter},
title = {Statistical Analysis of the Multichannel Wiener Filter Using a Bivariate Normal Distribution for Sample Covariance Matrices},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2800283},
doi = {10.1109/TASLP.2018.2800283},
abstract = {This paper studies the statistical performance of the multichannel Wiener filter MWF when the weights are computed using estimates of the sample covariance matrices of the noisy and the noise signals. It is well known that the optimal weights of the minimum variance distortionless response beamformer are only determined by the noisy sample covariance matrix or the noise sample covariance matrix, while those of the MWF are determined by both of them. Therefore, the difficulty increases dramatically in statistically analyzing the MWF when compared to analyzing the MVDR, where the main reason is that expressing the general joint probability density function p.d.f. of the two sample covariance matrices presented a Hitherto unsolved problem, to the best of our knowledge. For a deeper insight into the statistical performance of the MWF, this paper first introduces a bivariate normal distribution to approximately model the joint p.d.f. of the noisy and the noise sample covariance matrices. Each sample covariance matrix is approximately modeled by a random scalar multiplied by its true covariance matrix. This approximation is designed to preserve both the bias and the mean squared error of the matrix with respect to a natural distance on covariance matrices. The correlation of the bivariate normal distribution, referred to as the sample covariance matrices intrinsic correlation coefficient, captures all second-order dependencies of the noisy and the noise sample covariance matrices. By using the proposed bivariate normal distribution, the performance of the MWF can be predicted from the derived analytical expressions and many interesting results are revealed. As an example, the theoretical analysis demonstrates that the MWF performance may degrade in terms of noise reduction and signal-to-noise-ratio improvement when using more sensors in some noise scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {951–966},
numpages = {16}
}

@article{10.1109/TASLP.2018.2806305,
author = {Cumani, Sandro and Laface, Pietro},
title = {Scoring Heterogeneous Speaker Vectors Using Nonlinear Transformations and Tied PLDA Models},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2806305},
doi = {10.1109/TASLP.2018.2806305},
abstract = {Most current state-of-the-art text-independent speaker recognition systems are based on i-vectors, and on probabilistic linear discriminant analysis PLDA. PLDA assumes that the i-vectors of a trial are homogeneous, i.e., that they have been extracted by the same system. In other words, the enrollment and test i-vectors belong to the same class. However, it is sometimes important to score trials including “heterogeneous” i-vectors, for instance, enrollment i-vectors extracted by an old system, and test i-vectors extracted by a newer, more accurate, system. In this paper, we introduce a PLDA model that is able to score heterogeneous i-vectors independent of their extraction approach, dimensions, and any other characteristics that make a set of i-vectors of the same speaker belong to different classes. The new model, which will be referred to as nonlinear tied-PLDA NL-Tied-PLDA, is obtained by a generalization of our recently proposed nonlinear PLDA approach, which jointly estimates the PLDA parameters and the parameters of a nonlinear transformation of the i-vectors. The generalization consists of estimating a class-dependent nonlinear transformation of the i-vectors, with the constraint that the transformed i-vectors of the same speaker share the same speaker factor. The resulting model is flexible and accurate, as assessed by the results of a set of experiments performed on the extended core NIST SRE 2012 evaluation. In particular, NL-Tied-PLDA provides better results on heterogeneous trials with respect to the corresponding homogeneous trials scored by the old system, and, in some configurations, it also reaches the accuracy of the new system. Similar results were obtained on the female-extended core NIST SRE 2010 telephone condition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {995–1009},
numpages = {15}
}

@article{10.1109/TASLP.2018.2798821,
author = {Delcroix, Marc and Kinoshita, Keisuke and Ogawa, Atsunori and Huemmer, Christian and Nakatani, Tomohiro},
title = {Context Adaptive Neural Network Based Acoustic Models for Rapid Adaptation},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2798821},
doi = {10.1109/TASLP.2018.2798821},
abstract = {The adaptation of automatic speech recognition systems to a speaker or an environment is important if we are to achieve high speech recognition performance ubiquitously. Recently, deep neural network DNN based acoustic models have been made adaptive to speakers or environments by the addition of an auxiliary feature representing the acoustic context information such as speaker or noise characteristics to the network input. The addition of such auxiliary features to the input realizes only the adaptation of the bias term of the input layer. In this paper, we introduce “context adaptive neural networks,” which are an alternative approach for exploiting auxiliary features that can achieve adaptation of all the parameters of a layer including the linear transformation matrices and the bias terms. A context adaptive neural network is a neural network with one of its layers factorized into sublayers, each associated with an acoustic context class representing a class of speakers or noise conditions. The output of the factorized layer is obtained as a weighted sum of the contributions of all of the sublayers. The weighting coefficients, or context class weights, are derived from the auxiliary features, by transforming them through an auxiliary network. The auxiliary network and the main network can be trained jointly, which enables the context classes that optimize the training criterion to be learned automatically. We perform experiments on three tasks, i.e., two speaker adaptation experiments using DNN models with medium-sized Wall Street Journal and large Continuous Spontaneous Japanese training datasets, and one environmental adaptation of a convolutional neural network based acoustic model with CHiME3 data. These experiments confirm the potential of the proposed approach in various settings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {895–908},
numpages = {14}
}

@article{10.1109/TASLP.2018.2798822,
author = {Tran, Linh Thi Thuc and Nordholm, Sven Erik and Schepker, Henning and Dam, Hai Huyen and Doclo, Simon},
title = {Two-Microphone Hearing Aids Using Prediction Error Method for Adaptive Feedback Control},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2798822},
doi = {10.1109/TASLP.2018.2798822},
abstract = {A challenge in hearing aids is adaptive feedback control which often uses an adaptive filter to estimate the feedback path. This estimate of the feedback path usually results in a bias due to the correlation between the loudspeaker signal and the incoming signal. The prediction error method PEM is a popular method for reducing this bias for adaptive feedback control AFC in hearing aids, providing a significant performance improvement compared to conventional adaptive feedback control techniques. However, the PEM-based AFC PEM-AFC applications are still limited to single-microphone single-loudspeaker SMSL systems. This paper investigates the application of the PEM-AFC to a two-microphone single-loudspeaker hearing aid with detailed theoretical analysis as well as practical experiments. In the proposed method, PEM-AFC2, we use the two-microphone adaptive feedback control AFC2 method with two microphones and one loudspeaker. The incoming signals at the two microphones are related by a relative transfer function RTF which is used to predict the incoming signal at the main microphone. In addition, a prefilter is employed to prewhiten the loudspeaker and the microphone signals before the adaptive filter estimates. As a result, the proposed method obtains a lower bias and a faster tracking rate compared to the PEM-AFC and the AFC2 method, while still maintaining a good quality of the incoming signal. A new derivation for optimal filters in the AFC2 method will also be provided. The performance of the proposed method is evaluated for speech shaped noise as incoming signal and with undermodeling the RTF as well as with perfect modeling the RTF. Moreover, different types of incoming signals and a sudden change of feedback paths are also considered. The experimental results show that the proposed approach yields a significant performance improvement compared to existing state-of-the-art AFC methods such as the PEM-AFC and the AFC2.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {909–923},
numpages = {15}
}

@article{10.1109/TASLP.2018.2803263,
author = {Wang, Lin and Cavallaro, Andrea},
title = {Pseudo-Determined Blind Source Separation for Ad-Hoc Microphone Networks},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2803263},
doi = {10.1109/TASLP.2018.2803263},
abstract = {We propose a pseudo-determined blind source separation framework that exploits the information from a large number of microphones in an ad-hoc network to extract and enhance sound sources in a reverberant scenario. After compensating for the time offsets and sampling rate mismatch between asynchronous signals, we interpret as a determined $Mtimes M$ mixture the over-determined $Mtimes N$ mixture, where $M>N$ is the number of microphones and $N$ is the number of sources. Next, we propose a pseudodetermined mixture model that can apply an $Mtimes M$ independent component analysis ICA directly to the $M$ -channel recordings. Moreover, we propose a reference-based permutation alignment scheme that aligns the permutation of the ICA outputs and classifies them into target channels, which contain the $N$ sources, and nontarget channels, which contain reverberation residuals. Finally, using the signals from nontarget channels, we estimate in each target channel the power spectral density of the noise component that we suppress with a spectral postfilter. Interestingly, we also obtain late-reverberation suppression as by-product. Experiments show that each processing block improves incrementally source separation and that the performance of the proposed pseudodetermined separation improves as the number of microphones increases.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {981–994},
numpages = {14}
}

@article{10.1109/TASLP.2018.2800280,
author = {Vaz, Colin and Ramanarayanan, Vikram and Narayanan, Shrikanth},
title = {Acoustic Denoising Using Dictionary Learning With Spectral and Temporal Regularization},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2800280},
doi = {10.1109/TASLP.2018.2800280},
abstract = {We present a method for speech enhancement of data collected in extremely noisy environments, such as those obtained during magnetic resonance imaging scans. We propose an algorithm based on dictionary learning to perform this enhancement. We use complex nonnegative matrix factorization with intrasource additivity CMF-WISA to learn dictionaries of the noise and speech+noise portions of the data and use these to factor the noisy spectrum into estimated speech and noise components. We augment the CMF-WISA cost function with spectral and temporal regularization terms to improve the noise modeling. Based on both objective and subjective assessments, we find that our algorithm significantly outperforms traditional techniques such as least mean squares filtering, while not requiring prior knowledge or specific assumptions such as periodicity of the noise waveforms that current state-of-the-art algorithms require.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {967–980},
numpages = {14}
}

@article{10.1109/TASLP.2017.2784298,
author = {Baba, Youssef El and Walther, Andreas and Habets, Emanuel A. P.},
title = {3D Room Geometry Inference Based on Room Impulse Response Stacks},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2784298},
doi = {10.1109/TASLP.2017.2784298},
abstract = {Room geometry inference is concerned with the localization of reflective boundaries in an enclosed space. This paper outlines a method for inferring room geometry based on the positions of loudspeakers and real or image microphones, which are computed using sets of times of arrival TOAs obtained from room impulse responses RIRs. These RIRs describe the acoustic propagation between the loudspeakers in an array and a single microphone. First, peaks corresponding to TOAs in these RIRs are detected and labeled using an automated method. Second, the labeled TOA sets are used to estimate the real and image microphone positions, with knowledge of the loudspeaker array geometry. Third, using all these positions, the positions of reflection points on the available reflectors in the room are determined. The reflection points determine the reflectors’ locations and orientations. This approach is largely automated and usable in real-world scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {857–872},
numpages = {16}
}

@article{10.1109/TASLP.2018.2808042,
author = {Bernardi, Giuliano and van Waterschoot, Toon and Wouters, Jan and Moonen, Marc},
title = {Subjective and Objective Sound-Quality Evaluation of Adaptive Feedback Cancellation Algorithms},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2808042},
doi = {10.1109/TASLP.2018.2808042},
abstract = {Objective measures are widely used for the perceptual sound-quality evaluation of audio signal processing algorithms. Nevertheless, the use of subjective-evaluation measures remains relevant, in particular when application-specific objective measures are lacking. In this paper, we present a perceptual sound-quality evaluation of different algorithms for adaptive feedback cancellation AFC, with both speech and music signals. Three algorithms are compared: the block normalized least mean squares algorithm, the prediction-error method PEM based frequency-domain adaptive filter, and the PEM-based frequency-domain Kalman filter PEM-FDKF. The subjective evaluation results for the tested algorithms suggest that there is a large difference in statistical significance, and a corresponding large effect size, between the PEM-FDKF and the other algorithms, when using speech signals. A smaller statistical significance, and a lower effect size, is reported when using music signals. The subjective evaluation results are then compared with the results obtained with several objective measures. The correlation between subjective and objective scores shows that objective measures can be effectively used to predict the sound-quality degradation caused by acoustic feedback and AFC artifacts.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {1010–1024},
numpages = {15}
}

@article{10.1109/TASLP.2018.2797420,
author = {Zhang, Qian and Hansen, John H. L.},
title = {Language/Dialect Recognition Based on Unsupervised Deep Learning},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2797420},
doi = {10.1109/TASLP.2018.2797420},
abstract = {Over the past decade, bottleneck features within an i-Vector framework have been used for state-of-the-art language/dialect identification LID/DID. However, traditional bottleneck feature extraction requires additional transcribed speech information. Alternatively, two types of unsupervised deep learning methods are introduced in this study. To address this limitation, an unsupervised bottleneck feature extraction approach is proposed, which is derived from the traditional bottleneck structure but trained with estimated phonetic labels. In addition, based on a generative modeling autoencoder, two types of latent variable learning algorithms are introduced for speech feature processing, which have been previous considered for image processing/reconstruction. Specifically, a variational autoencoder and adversarial autoencoder are utilized on alternative phase of speech processing. To demonstrate the effectiveness of the proposed methods, three corpora are evaluated: 1 a four Chinese dialect dataset, 2 a five Arabic dialect corpus, and 3 multigenre broadcast challenge corpus MGB-3 for arabic DID. The proposed features are shown to outperform traditional acoustic feature MFCCs consistently across three corpora. Taken collectively, the proposed features achieve up to a relative +58% improvement in $C_{text{avg}}$ for LID/DID without the need of any secondary speech corpora.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {873–882},
numpages = {10}
}

@article{10.1109/TASLP.2018.2800525,
author = {Dionelis, Nikolaos and Brookes, Mike},
title = {Phase-Aware Single-Channel Speech Enhancement With Modulation-Domain Kalman Filtering},
year = {2018},
issue_date = {May 2018},
publisher = {IEEE Press},
volume = {26},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2800525},
doi = {10.1109/TASLP.2018.2800525},
abstract = {We present a speech enhancement algorithm that performs modulation-domain Kalman filtering to track the speech phase using circular statistics, along with the spectral log-amplitudes of speech and noise. In the proposed algorithm, the speech phase posterior is used to create an enhanced speech phase spectrum for the signal reconstruction of speech. The Kalman filter prediction step separately models the temporal inter-frame correlation of the speech and noise spectral log-amplitudes and of the speech phase, while the Kalman filter update step models their nonlinear relations under the assumption that speech and noise add in the complex short-time Fourier transform domain. The phase-sensitive enhancement algorithm is evaluated with speech quality and intelligibility metrics, using a variety of noise types over a range of SNRs. Instrumental measures predict that tracking the speech log-spectrum and phase with modulation-domain Kalman filtering leads to consistent improvements in speech quality, over both conventional enhancement algorithms and other algorithms that perform modulation-domain Kalman filtering.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {937–950},
numpages = {14}
}

@article{10.1109/TASLP.2017.2789320,
author = {Itakura, Kousuke and Bando, Yoshiaki and Nakamura, Eita and Itoyama, Katsutoshi and Yoshii, Kazuyoshi and Kawahara, Tatsuya},
title = {Bayesian Multichannel Audio Source Separation Based on Integrated Source and Spatial Models},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2789320},
doi = {10.1109/TASLP.2017.2789320},
abstract = {This paper presents new statistical methods of multichannel audio source separation based on unified source and spatial models that, respectively, represent the generative process of latent source spectrograms and that of observed mixture spectrograms. One possibility of the source model is a factor model based on nonnegative matrix factorization that represents each time-frequency TF bin as the weighted sum of basis spectra. Another possibility is a mixture model inspired by latent Dirichlet allocation that exclusively classifies each TF bin into one of basis spectra. Similarly, the spatial model can either be a factor model that represents each TF bin as the weighted sum of source spectra or a mixture model that classifies each bin into one of those spectra. To unify these models in a principled manner and incorporate prior knowledge of a microphone array, we propose hierarchical Bayesian models of all the source–spatial combinations factor–factor, mixture–factor, factor–mixture, and mixture–mixture models and derive efficient Gibbs sampling algorithms for posterior inference. Experimental results showed that the proposed unified models outperformed the state-of-the-art method using only the spatial mixture model. Among the four unified models, the spatial factor model tended to work better than the spatial mixture model in exchange for larger computational cost, and the choice of source models had a little impact on the performance and computational cost.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {831–846},
numpages = {16}
}

@article{10.1109/TASLP.2018.2795749,
author = {Luo, Yi and Chen, Zhuo and Mesgarani, Nima},
title = {Speaker-Independent Speech Separation With Deep Attractor Network},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2795749},
doi = {10.1109/TASLP.2018.2795749},
abstract = {Despite the recent success of deep learning for many speech processing tasks, single-microphone, speaker-independent speech separation remains challenging for two main reasons. The first reason is the arbitrary order of the target and masker speakers in the mixture permutation problem, and the second is the unknown number of speakers in the mixture output dimension problem. We propose a novel deep learning framework for speech separation that addresses both of these issues. We use a neural network to project the time-frequency representation of the mixture signal into a high-dimensional embedding space. A reference point attractor is created in the embedding space to represent each speaker which is defined as the centroid of the speaker in the embedding space. The time-frequency embeddings of each speaker are then forced to cluster around the corresponding attractor point which is used to determine the time-frequency assignment of the speaker. We propose three methods for finding the attractors for each source in the embedding space and compare their advantages and limitations. The objective function for the network is standard signal reconstruction error which enables end-to-end operation during both training and test phases. We evaluated our system using the Wall Street Journal dataset WSJ0 on two and three speaker mixtures and report comparable or better performance than other state-of-the-art deep learning methods for speech separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {787–796},
numpages = {10}
}

@article{10.1109/TASLP.2018.2795756,
author = {Zhang, Jihui and Abhayapala, Thushara D. and Zhang, Wen and Samarasinghe, Prasanga N. and Jiang, Shouda},
title = {Active Noise Control Over Space: A Wave Domain Approach},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2795756},
doi = {10.1109/TASLP.2018.2795756},
abstract = {Noise control and cancellation over a spatial region is a fundamental problem in acoustic signal processing. In this paper, we utilize wave-domain adaptive algorithms to iteratively calculate the secondary source driving signals and to cancel the primary noise field over the control region. We propose wave-domain active noise control algorithms based on two minimization problems: first, minimizing the wave-domain residual signal coefficients, and second, minimizing the acoustic potential energy over the region, and derive the update equations with respect to two variables, the loudspeaker weights and wave-domain secondary source coefficients. Simulation results demonstrate the effectiveness of the proposed algorithms, more specifically the convergence speed and the noise cancellation performance in terms of the noise reduction level and acoustic potential energy reduction level over the entire spatial region.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {774–786},
numpages = {13}
}

@article{10.1109/TASLP.2018.2793670,
author = {Xu, Longting and Lee, Kong Aik and Li, Haizhou and Yang, Zhen},
title = {Generalizing I-Vector Estimation for Rapid Speaker Recognition},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2793670},
doi = {10.1109/TASLP.2018.2793670},
abstract = {An i-vector is a compact representation that captures both the speaker and session variabilities rendered in a spoken utterance. Over the past years, it has prevailed over other techniques and is now the de facto representation for text-independent speaker recognition. Standard i-vector extraction requires intense computation at run-time. Reducing the computation will allow effective use of i-vector in more applications. Such intense computation arises from the posterior covariance matrix, when estimating the i-vector. There have been studies on how to simplify the computation of posterior covariance matrix with modest success. In this paper, we propose a novel approach to i-vector extraction without the need to evaluate the full posterior covariance thereby speeding up the run-time extraction process. This is achieved by generalizing the i-vector estimation in two ways. First, we introduce the use of occupancy reweighting in conjunction with whitening over the Baum–Welch statistics as part of the preprocessing step. Second, we introduce the so-called subspace-orthogonalizing prior SOP to replace the standard Gaussian prior in i-vector formulation. Experiments conducted on the extended-core task of NIST SRE’10 show that the proposed rapid SOP approach achieves considerable speed-up over the standard i-vector with comparable equal error rates.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {749–759},
numpages = {11}
}

@article{10.1109/TASLP.2018.2791804,
author = {Hu, Ya-Jun and Ling, Zhen-Hua},
title = {Extracting Spectral Features Using Deep Autoencoders With Binary Distributed Hidden Units for Statistical Parametric Speech Synthesis},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2791804},
doi = {10.1109/TASLP.2018.2791804},
abstract = {This paper presents a spectral feature extraction method using deep autoencoders DAEs with binary distributed hidden units BDAE for statistical parametric speech synthesis SPSS. Conventional DAEs are trained to minimize the error of reconstructing raw features. In this paper, we investigate another important property of DAEs that may influence their performances as feature extractors for regression tasks, i.e., the degree of binarization of hidden units. Our analysis shows that making the hidden units of DAEs to be binary may help alleviate the over-smoothing effect caused by acoustic modeling and parameter generation, which are one of the main deficiencies of current SPSS systems. This paper further proposes an effective BDAE training method by adding noise to the input of hidden units during model training and applying DBN-based pretraining strategies. Our experiments adopt feedforward deep neural networks as acoustic models for SPSS and compare the performances of different spectral feature extractors. Experimental results show that when extracting low-dimensional spectral features by BDAEs, the predicted spectral features can reconstruct spectral envelopes closer to natural samples than using conventional DAEs. Subjective evaluations on the synthetic voices of a Chinese speaker and an English speaker demonstrate that BDAEs achieve better naturalness of synthetic speech than conventional mel-cepstra and other neural network based feature extractors, such as DAEs and DBNs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {713–724},
numpages = {12}
}

@article{10.1109/TASLP.2018.2791105,
author = {Tan, Zhili and Mak, Man-Wai and Mak, Brian Kan-Wing},
title = {DNN-Based Score Calibration With Multitask Learning for Noise Robust Speaker Verification},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2791105},
doi = {10.1109/TASLP.2018.2791105},
abstract = {This paper proposes and investigates several deep neural network DNN based score compensation, transformation, and calibration algorithms for enhancing the noise robustness of i-vector speaker verification systems. Unlike conventional calibration methods where the required score shift is a linear function of SNR or log-duration, the DNN approach learns the complex relationship between the score shifts and the combination of i-vector pairs and uncalibrated scores. Furthermore, with the flexibility of DNNs, it is possible to explicitly train a DNN to recover the clean scores without having to estimate the score shifts. To alleviate the overfitting problem, multitask learning is applied to incorporate auxiliary information such as SNRs and speaker ID of training utterances into the DNN. Experiments on NIST 2012 SRE show that score calibration derived from multitask DNNs can improve the performance of the conventional score-shift approch significantly, especially under noisy conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {700–712},
numpages = {13}
}

@article{10.1109/TASLP.2018.2791806,
author = {Cumani, Sandro and Laface, Pietro},
title = {Speaker Recognition Using E–Vectors},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2791806},
doi = {10.1109/TASLP.2018.2791806},
abstract = {Systems based on i–vectors represent the current state–of–the–art in text-independent speaker recognition. Unlike joint factor analysis JFA, which models both speaker and intersession subspaces separately, in the i–vector approach all the important variability is modeled in a single low-dimensional subspace. This paper is based on the observation that JFA estimates a more informative speaker subspace than the “total variability” i–vector subspace, because the latter is obtained by considering each training segment as belonging to a different speaker. We propose a speaker modeling approach that extracts a compact representation of a speech segment, similar to the speaker factors of JFA and to i–vectors, referred to as “e–vector.” Estimating the e–vector subspace follows a procedure similar to i–vector training, but produces a more accurate speaker subspace, as confirmed by the results of a set of tests performed on the NIST 2012 and 2010 Speaker Recognition Evaluations. Simply replacing the i–vectors with e–vectors we get approximately 10% average improvement of the C $_{text{primary}}$ cost function, using different systems and classifiers. It is worth noting that these performance gains come without any additional memory or computational costs with respect to the standard i–vector systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {736–748},
numpages = {13}
}

@article{10.1109/TASLP.2018.2790707,
author = {Laufer-Goldshtein, Bracha and Talmon, Ronen and Gannot, Sharon},
title = {A Hybrid Approach for Speaker Tracking Based on TDOA and Data-Driven Models},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2790707},
doi = {10.1109/TASLP.2018.2790707},
abstract = {The problem of speaker tracking in noisy and reverberant enclosures is addressed in this paper. We present a hybrid algorithm, combining traditional tracking schemes with a new learning-based approach. A state-space representation, consisting of a propagation and observation models, is learned from signals measured by several distributed microphone pairs. The proposed representation is based on two data modalities corresponding to high-dimensional acoustic features representing the full reverberant acoustic channels as well as low-dimensional time difference of arrival TDOA estimates. The state-space representation is accompanied by a statistical model based on a Gaussian process used to relate the variations of the acoustic channels to the physical variations of the associated source positions, thereby forming a data-driven propagation model for the source movement. In the observation model, the source positions are nonlinearly mapped to the associated TDOA readings. The obtained propagation and observation models establish the basis for employing an extended Kalman filter. The simulation results demonstrate the robustness of the proposed method in noisy and reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {725–735},
numpages = {11}
}

@article{10.1109/TASLP.2018.2796844,
author = {Buchris, Yaakov and Cohen, Israel and Benesty, Jacob},
title = {Frequency-Domain Design of Asymmetric Circular Differential Microphone Arrays},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2796844},
doi = {10.1109/TASLP.2018.2796844},
abstract = {Circular differential microphone arrays CDMAs facilitate compact superdirective beamformers whose beampatterns are nearly frequency invariant. In contrast to linear differential microphone arrays where the optimal steering direction is at the endfire, CDMAs provide perfect steering for all azimuthal directions. Herein, we extend the traditional symmetric model of DMAs and establish an analytical asymmetric model for $N$th-order CDMAs. This model exploits the circular geometry to eliminate the inherent limitation of symmetric beampatterns associated with a linear geometry and allows also asymmetric beampatterns. This new model is then used to develop asymmetric versions of two optimal commonly used beampatterns namely the hypercardioid and the supercardioid. Experimental results demonstrate the advantages of the asymmetric model compared to the traditional symmetric one, when additional directional constraints are imposed. The proposed model yields superior performance in terms of white noise gain, directivity factor, and front-to-back ratio, as well as more flexible design of nulls for the interfering signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {760–773},
numpages = {14}
}

@article{10.1109/TASLP.2018.2795754,
author = {Joy, Neethu Mariam and Kothinti, Sandeep Reddy and Umesh, Srinivasan},
title = {FMLLR Speaker Normalization With I-Vector: In Pseudo-FMLLR and Distillation Framework},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2795754},
doi = {10.1109/TASLP.2018.2795754},
abstract = {When an automatic speech recognition ASR system is deployed for real-world applications, it often receives only one utterance at a time for decoding. This single utterance could be of short duration depending on the ASR task. In these cases, robust estimation of speaker normalizing methods like feature-space maximum likelihood linear regression FMLLR and i-vectors may not be feasible. In this paper, we propose two unsupervised speaker normalization techniques—one at feature level and other at model level of acoustic modeling—to overcome the drawbacks of FMLLR and i-vectors in real-time scenarios. At feature level, we propose the use of deep neural networks DNN to generate pseudo-FMLLR features from time-synchronous pair of filterbank and FMLLR features. These pseudo-FMLLR features can then be used for DNN acoustic model training and decoding. At model level, we propose a generalized distillation framework, where a teacher DNN trained on FMLLR features guides the training and optimization of a student DNN trained on filterbank features. In both the proposed methods, the ambiguity in choosing the speaker-specific FMLLR transform can be reduced by augmenting i-vectors to the input filterbank features. Experiments conducted on 33-h and 110-h subsets of Switchboard corpus show that the proposed methods provide significant gains over DNNs trained on FMLLR, i-vector appended FMLLR, filterbank and i -vector appended filterbank features, in real-time scenario.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {797–805},
numpages = {9}
}

@article{10.1109/TASLP.2018.2796843,
author = {Tan, Zhili and Mak, Man-Wai and Mak, Brian Kan-Wing and Zhu, Yingke},
title = {Denoised Senone I-Vectors for Robust Speaker Verification},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2796843},
doi = {10.1109/TASLP.2018.2796843},
abstract = {Recently, it has been shown that senone i-vectors, whose posteriors are produced by senone deep neural networks DNNs, outperform the conventional Gaussian mixture model GMM i-vectors in both speaker and language recognition tasks. The success of senone i-vectors relies on the capability of the DNN to incorporate phonetic information into the i-vector extraction process. In this paper, we argue that to apply senone i-vectors in noisy environments, it is important to robustify the phonetically discriminative acoustic features and senone posteriors estimated by the DNN. To this end, we propose a deep architecture formed by stacking a deep belief network on top of a denoising autoencoder DAE. After backpropagation fine-tuning, the network, referred to as denoising autoencoder–deep neural network DAE–DNN, facilitates the extraction of robust phonetically-discriminitive bottleneck BN features and senone posteriors for i-vector extraction. We refer to the resulting i-vectors as denoised BN-based senone i-vectors. Results on NIST 2012 SRE show that senone i-vectors outperform the conventional GMM i-vectors. More interestingly, the BN features are not only phonetically discriminative, results suggest that they also contain sufficient speaker information to produce BN-based senone i-vectors that outperform the conventional senone i-vectors. This work also shows that DAE training is more beneficial to BN feature extraction than senone posterior estimation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {820–830},
numpages = {11}
}

@article{10.1109/TASLP.2018.2797425,
author = {Chandna, Swati and Wang, Wenwu},
title = {Bootstrap Averaging for Model-Based Source Separation in Reverberant Conditions},
year = {2018},
issue_date = {April 2018},
publisher = {IEEE Press},
volume = {26},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2797425},
doi = {10.1109/TASLP.2018.2797425},
abstract = {Recently proposed model-based methods use time-frequency T-F masking for source separation, where the T-F masks are derived from various cues described by a frequency domain Gaussian mixture model GMM. These methods work well for separating mixtures recorded in low-to-medium level of reverberation, however, their performance degrades as the level of reverberation is increased. We note that the relatively poor performance of these methods under reverberant conditions can be attributed to the high variance of the frequency-dependent GMM parameter estimates. To address this limitation, a novel bootstrap-based approach is proposed to improve the accuracy of expectation maximization estimates of a frequency-dependent GMM based on an a priori chosen initialization scheme. It is shown how the proposed technique allows us to construct time-frequency masks which lead to improved model-based source separation for reverberant speech mixtures. Experiments and analysis are performed on speech mixtures formed using real room-recorded impulse responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {806–819},
numpages = {14}
}

@article{10.1109/TASLP.2017.2779787,
author = {Wang, Syu-Siang and Lin, Payton and Tsao, Yu and Hung, Jeih-Weih and Su, Borching},
title = {Suppression by Selecting Wavelets for Feature Compression in Distributed Speech Recognition},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2779787},
doi = {10.1109/TASLP.2017.2779787},
abstract = {Distributed speech recognition DSR splits the processing of data between a mobile device and a network server. In the front-end, features are extracted and compressed to transmit over a wireless channel to a back-end server, where the incoming stream is received and reconstructed for recognition tasks. In this paper, we propose a feature compression algorithm termed suppression by selecting wavelets SSW to achieve the two main goals of DSR: Minimizing memory and device requirements while also maintaining or even improving the recognition performance. The SSW approach first applies the discrete wavelet transform DWT to filter the incoming speech feature sequence into two temporal subsequences at the client terminal. Feature compression is achieved by keeping the low modulation frequency subsequence while discarding the high frequency counterpart. The low-frequency subsequence is then transmitted across the remote network for specific feature statistics normalization. Wavelets are favorable for resolving the temporal properties of the feature sequence, and the down-sampling process in DWT achieves data compression by reducing the amount of data at the terminal prior to transmission across the network. Once the compressed features have arrived at the server, the feature sequence can be enhanced by statistics normalization, reconstructed with inverse DWT, and compensated with a simple post filter to alleviate any over-smoothing effects from the compression stage. Results on a standard robustness task Aurora-4 and on a Mandarin Chinese news corpus showed SSW outperforms conventional noise-robustness techniques while also providing nearly a 50% compression rate during the transmission stage of DSR systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {564–579},
numpages = {16}
}

@article{10.1109/TASLP.2017.2778562,
author = {Salvador, Cesar D. and Sakamoto, Shuichi and Trevino, Jorge and Suzuki, Yoiti},
title = {Boundary Matching Filters for Spherical Microphone and Loudspeaker Arrays},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2778562},
doi = {10.1109/TASLP.2017.2778562},
abstract = {Conversion of microphone array signals into loudspeaker array signals is an essential process in high-definition spatial audio. This paper presents the theory of boundary matching filters BMFs for spherical array signal conversion. BMFs adapt the physical boundary conditions used during recording to the ones required for reproduction by relying on a theoretical framework provided by the Kirchhoff–Helmholtz integral equation KHIE. Computationally, array signal conversion is performed in a transform domain where sound fields are represented in terms of spherical harmonic functions. Related research on transform-domain signal conversion filters is interpreted in the context of the KHIE. The case of a rigid recording boundary and an open reproduction boundary is addressed. The proposed rigid-to-open BMFs provide a suitable basis for designing gain-limited filters to deal with the problem of excessive gains at certain frequency bands, observed when using high-resolution arrays. Spatial, spectral, and temporal effects in sound field reconstruction when finite numbers of transducers are used in anechoic conditions are investigated analytically and exemplified numerically. Results show that the proposed gain-limited rigid-to-open BMFs outperform the existing gain-limited filters based on Tikhonov regularization because they reduce the spatial discretization effects and yield impulse responses that are more localized around their main peaks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {461–474},
numpages = {14}
}

@article{10.1109/TASLP.2017.2782360,
author = {Do, Van Hai and Chen, Nancy F. and Lim, Boon Pang and Hasegawa-Johnson, Mark A.},
title = {Multitask Learning for Phone Recognition of Underresourced Languages Using Mismatched Transcription},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2782360},
doi = {10.1109/TASLP.2017.2782360},
abstract = {It is challenging to obtain large amounts of native matched labels for speech audio in underresourced languages. This challenge is often due to a lack of literate speakers of the language, or in extreme cases, a lack of universally acknowledged orthography as well. One solution is to increase the amount of labeled data by using mismatched transcription, which employs transcribers who do not speak the underresourced language of interest called the target language in place of native speakers, to transcribe what they hear as nonsense speech in their own annotation language $ne$ target language. Previous uses of mismatched transcription converted it to a probabilistic transcription PT, but PT is limited by the errors of nonnative perception. This paper proposes, instead, a multitask learning framework in which one deep neural network DNN is trained to optimize two separate tasks: acoustic modeling of a small number of matched transcription with matched target-language graphemes; and acoustic modeling of a large number of mismatched transcription with mismatched annotation-language graphemes. We find that: first, the multitask learning framework gives significant improvement over monolingual, semisupervised learning, multilingual DNN training, and transfer learning baselines; second, a Gaussian Mixture Model-Hidden-Markov Model GMM-HMM model adapted using PT improves alignments, thereby improving training; and third, bottleneck features trained on the mismatched transcriptions lead to even better alignments, resulting in further performance gains of the multitask DNN. Our experiments are conducted on the IARPA Georgian and Vietnamese BABEL corpora as well as on our newly collected speech corpus of Singapore Hokkien, an underresourced language with no standard written form.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {501–514},
numpages = {14}
}

@article{10.1109/TASLP.2017.2782004,
author = {Emura, Satoru},
title = {Residual Echo Reduction for Multichannel Acoustic Echo Cancelers With a Complex-Valued Residual Echo Estimate},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2782004},
doi = {10.1109/TASLP.2017.2782004},
abstract = {This paper proposes a new method to reduce residual echo for multichannel acoustic echo cancelers AECs. This method estimates complex-valued residual echo and corrects its overestimated amplitude on the basis of a bias model of coherence estimation. Unlike conventional residual echo reduction methods for single-channel AECs, this method takes into account not only the magnitudes but also the phases of far-end signals and frequency responses. The inclusion of the phases gives the correct model of residual echo for multichannel AECs. The correction of the overestimated amplitude of residual echo decreases the distortion of near-end speech during double talk. The proposed method reduces the residual echo in the AEC output by 5–10 dB immediately after environmental changes such as a far-end talker change and an echo-path change. The proposed method provides less distorted near-end speech than the square-root Wiener filter during double talk.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {485–500},
numpages = {16}
}

@article{10.1109/TASLP.2017.2786863,
author = {Wang, Yu and Brookes, Mike},
title = {Model-Based Speech Enhancement in the Modulation Domain},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2786863},
doi = {10.1109/TASLP.2017.2786863},
abstract = {This paper presents an algorithm for modulation-domain speech enhancement using a Kalman filter. The proposed estimator jointly models the estimated dynamics of the spectral amplitudes of speech and noise to obtain an MMSE estimation of the speech amplitude spectrum with the assumption that the speech and noise are additive in the complex domain. In order to include the dynamics of noise amplitudes with those of speech amplitudes, we propose a statistical “Gaussring” model that comprises a mixture of Gaussians whose centers lie in a circle on the complex plane. The performance of the proposed algorithm is evaluated using the perceptual evaluation of speech quality measure, segmental SNR measure, and short-time objective intelligibility measure. For speech quality measures, the proposed algorithm is shown to give a consistent improvement over a wide range of SNRs when compared to competitive algorithms. Speech recognition experiments also show that the Gaussring-model-based algorithm performs well for two types of noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {580–594},
numpages = {15}
}

@article{10.1109/TASLP.2017.2788183,
author = {Huemmer, Christian and Hofmann, Christian and Maas, Roland and Kellermann, Walter},
title = {Estimating Parameters of Nonlinear Systems Using the Elitist Particle Filter Based on Evolutionary Strategies},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2788183},
doi = {10.1109/TASLP.2017.2788183},
abstract = {In this paper, we present the elitist particle filter based on evolutionary strategies EPFES as an efficient approach to estimate the statistics of a latent state vector capturing the relevant information of a nonlinear system. Similar to classical particle filtering, the EPFES consists of a set of particles and respective weights which represent different realizations of the latent state vector and their likelihood of being the solution of the optimization problem. As main innovation, the EPFES includes an evolutionary elitist-particle selection scheme which combines long-term information with instantaneous sampling from an approximated continuous posterior distribution. In this paper, we propose two advancements of the previously published elitist-particle selection process. Further, the EPFES is shown to be a generalization of the widely-used Gaussian particle filter and thus evaluated with respect to the latter: First, we consider the univariate nonstationary growth model with time-variant latent state variable to evaluate the tracking capabilities of the EPFES for instantaneously calculated particle weights. This is followed by addressing the problem of single-channel nonlinear acoustic echo cancellation as a challenging benchmark task for identifying an unknown system of large search space: the nonlinear acoustic echo path is modeled by a cascade of a parameterized preprocessor to model the loudspeaker signal distortions and a linear FIR filter to model the sound wave propagation and the microphone. By using long-term information, we highlight the efficacy of the well-generalizing EPFES in estimating the preprocessor parameters for a simulated scenario and a real smartphone recording. Finally, we illustrate similarities between the EPFES and evolutionary algorithms to outline future improvements by fusing the achievements of both fields of research.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {595–608},
numpages = {14}
}

@article{10.1109/TASLP.2017.2783545,
author = {Abdelaziz, Ahmed Hussen},
title = {Comparing Fusion Models for DNN-Based Audiovisual Continuous Speech Recognition},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2783545},
doi = {10.1109/TASLP.2017.2783545},
abstract = {Audiovisual fusion is one of the most challenging tasks that continues to attract substantial research interest in the field of audiovisual automatic speech recognition AV-ASR. In the last few decades, many approaches for integrating the audio and video modalities were proposed to enhance the performance of automatic speech recognition in both clean and noisy conditions. However, very few studies can be found in the literature that compare different fusion models for AV-ASR. Even less research work compares audiovisual fusion models for large vocabulary continuous speech recognition LVCSR models using deep neural networks DNNs. This paper reviews and compares the performance of five audiovisual fusion models: the feature fusion model, the decision fusion model, the multistream hidden Markov model HMM, the coupled HMM, and the turbo decoders. A complete evaluation of these fusion models is conducted using a standard speaker-independent DNN-based LVCSR Kaldi recipe in three experimental setups: a clean-train-clean-test, a clean-train-noisy-test, and a matched-training setup. All experiments have been applied to the recently released NTCD-TIMIT audiovisual corpus. The task of NTCD-TIMIT is phone recognition in continuous speech. Using NTCD-TIMIT with its freely available visual features and 37 clean and noisy acoustic signals allows for this study to be a common benchmark, to which novel LVCSR AV-ASR models and approaches can be compared.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {475–484},
numpages = {10}
}

@article{10.1109/TASLP.2017.2789321,
author = {Salvati, Daniele and Drioli, Carlo and Foresti, Gian Luca},
title = {A Low-Complexity Robust Beamforming Using Diagonal Unloading for Acoustic Source Localization},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2789321},
doi = {10.1109/TASLP.2017.2789321},
abstract = {In acoustic array processing, beamforming is a class of algorithms commonly used to estimate the position of a radiating sound source. This paper presents a diagonal unloading DU transformation method for the conventional response power beamforming to achieve robust localization with low computational complexity. The transformation is obtained by subtracting an opportune diagonal matrix from the covariance matrix of the array output vector. Specifically, the DU beamformer aims at subtracting the signal subspace from the noisy signal space. It is, hence, a data-dependent covariance matrix conditioning method. We show how to calculate precisely the unloading parameters, and we present a comparison of the proposed DU beamforming, the robust minimum variance distortionless response MVDR filter, and the multiple signal classification MUSIC method, in terms of their respective eigenanalyses. Theoretical analysis and experiments conducted on both simulated and real acoustic data demonstrate that the DU beamformer localization performance is comparable to that of robust MVDR and MUSIC. Since its computational cost is equivalent to that of a conventional beamformer, the proposed DU beamformer method can, thus, be very attractive due to its effectiveness and computational efficiency.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {609–622},
numpages = {14}
}

@article{10.1109/TASLP.2017.2786544,
author = {Zhang, Jie and Chepuri, Sundeep Prabhakar and Hendriks, Richard Christian and Heusdens, Richard},
title = {Microphone Subset Selection for MVDR Beamformer Based Noise Reduction},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2786544},
doi = {10.1109/TASLP.2017.2786544},
abstract = {In large-scale wireless acoustic sensor networks WASNs, many of the sensors will only have a marginal contribution to a certain estimation task. Involving all sensors increases the energy budget unnecessarily and decreases the lifetime of the WASN. Using microphone subset selection, also termed as sensor selection, the most informative sensors can be chosen from a set of candidate sensors to achieve a prescribed inference performance. In this paper, we consider microphone subset selection for minimum variance distortionless response MVDR beamformer based noise reduction. The best subset of sensors is determined by minimizing the transmission cost while constraining the output noise power or signal-to-noise ratio. Assuming the statistical information on correlation matrices of the sensor measurements is available, the sensor selection problem for this model-driven scheme is first solved by utilizing convex optimization techniques. In addition, to avoid estimating the statistics related to all the candidate sensors beforehand, we also propose a data-driven approach to select the best subset using a greedy strategy. The performance of the greedy algorithm converges to that of the model-driven method, while it displays advantages in dynamic scenarios as well as on computational complexity. Compared to a sparse MVDR or radius-based beamformer, experiments show that the proposed methods can guarantee the desired performance with significantly less transmission costs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {550–563},
numpages = {14}
}

@article{10.1109/TASLP.2018.2789399,
author = {Kheder, Waad Ben and Matrouf, Driss and Ajili, Moez and Bonastre, Jean-Francois},
title = {A Unified Joint Model to Deal With Nuisance Variabilities in the I-Vector Space},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2789399},
doi = {10.1109/TASLP.2018.2789399},
abstract = {The past decade has witnessed a significant improvement in speaker recognition SR technology in terms of performance with the introduction of the i-vectors framework. Despite these advances, the performance of SR systems considerably suffers in the presence of acoustic nuisances and variabilities. In this paper, we develop a data-driven nuisance compensation technique in the i-vector space without referring to the effects of the targeted nuisances in the temporal domain. This approach is nonparametric as it does not suppose a specific relationship between a “good” version of an i-vector and its corrupted version. Instead, our algorithm models directly the joint distribution of both representations the good i-vector and its corrupted version and takes advantage of the reproducibility of acoustic corruptions to generate the corrupted i-vectors. We then build an MMSE estimator that computes an improved version of a corrupted test i-vector, given this joint distribution. Experiments are carried out on NIST SRE 2010 and speakers in the wild databases where the proposed algorithm is used to deal with additive noise and short utterances. Our technique is shown to be efficient, improving the baseline system performance in terms of equal-error rate by up to 70% when used on known test noises and up to 65% in the context of unseen noises using a generic model. It was also proven efficient in the context of duration mismatch reaching up to 40% of relative improvement when used on short utterances using multiple models corresponding to different durations and up to 36% when used on arbitrary duration test segments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {633–645},
numpages = {13}
}

@article{10.1109/TASLP.2017.2782487,
author = {Xiang, Yong and Natgunanathan, Iynkaran and Peng, Dezhong and Hua, Guang and Liu, Bo},
title = {Spread Spectrum Audio Watermarking Using Multiple Orthogonal PN Sequences and Variable Embedding Strengths and Polarities},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2782487},
doi = {10.1109/TASLP.2017.2782487},
abstract = {Copyright protection of audio data is a serious problem and spread spectrum SS based audio watermarking is a promising technology to tackle this problem. Although a number of SS-based audio watermarking methods have been reported in the literature, they cannot achieve high robustness and embedding capacity at the same time. In this paper, we propose a novel SS-based audio watermarking method that can embed a large number of watermark bits into an audio signal without compromising the robustness against common attacks. Compared with the existing audio watermarking methods, the proposed one is especially robust against severe noise addition and compression attacks, while achieving high embedding capacity. Moreover, the new audio watermarking method is computationally efficient. The validity of the proposed SS-based audio watermarking method is demonstrated by simulation results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {529–539},
numpages = {11}
}

@article{10.1109/TASLP.2017.2780993,
author = {Taseska, Maja and Habets, Emanuel A. P.},
title = {Blind Source Separation of Moving Sources Using Sparsity-Based Source Detection and Tracking},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2780993},
doi = {10.1109/TASLP.2017.2780993},
abstract = {Sparsity-based blind source separation BSS algorithms in the short time–frequency TF domain have received a lot of attention due to their versatility and noise reduction capabilities. In most of these algorithms, the estimation of the BSS filters relies on the accurate association of each time–frequency bin to the dominant source at that bin. The TF bin associations are then used to estimate the statistics of the source signals, and BSS is achieved by optimal spatial filters computed using the estimated statistics. The main objective of this paper is to apply such a framework to scenarios with an unknown number of moving sources. While state-of-the-art approaches employ online clustering algorithms to solve the problem for moving sources, we propose an approximate Bayesian tracker and perform the association of each TF bin to the dominant source using the tracker's measurement-to-source association probabilities. Therefore, the choice of the underlying narrowband models and measurements for the tracker as well as the resulting tracking algorithm constitute the main contributions of this paper. The TF bin associations obtained from the tracker are then used to estimate the statistics of the source signals. The performance of the resulting BSS filters is compared to the performance of state-of-the-art sparsity-based and independent vector analysis-based BSS algorithms. Our proposed approach targets scenarios with at least two spatially separated microphone arrays, with known microphone positions and relative orientations. The framework also allows for efficient management of a time-varying number of sources.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {657–670},
numpages = {14}
}

@article{10.1109/TASLP.2017.2769220,
author = {Gelly, Gregory and Gauvain, Jean-Luc},
title = {Optimization of RNN-Based Speech Activity Detection},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2769220},
doi = {10.1109/TASLP.2017.2769220},
abstract = {Speech activity detection SAD is an essential component of automatic speech recognition systems impacting the overall system performance. This paper investigates an optimization process for recurrent neural network RNN based SAD. This process optimizes all system parameters including those used for feature extraction, the NN weights, and the back-end parameters. Three cost functions are considered for SAD optimization: the frame error rate, the NIST detection cost function, and the word error rate of a downstream speech recognizer. Different types of RNN models and optimization methods are investigated. Three types of RNNs are compared: a basic RNN, long short-term memory LSTM network with peepholes, and a coordinated-gate LSTM CG-LSTM network introduced by Gelly and Gauvain. Well suited for nondifferentiable optimization problems, quantum-behaved particle swarm optimization is used to optimize feature extraction and posterior smoothing, as well as for the initial training of the neural networks. Experimental SAD results are reported on the NIST 2015 SAD evaluation data as well as REPERE and AMI meeting corpora. Speech recognition results are reported on the OpenKWS’13 test data. For all tasks and conditions, the proposed optimization method significantly improves the SAD performance and among all the tested SAD methods the CG-LSTM model gives the best results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {646–656},
numpages = {11}
}

@article{10.1109/TASLP.2017.2788182,
author = {Yu, Liang-Chih and Wang, Jin and Lai, K. Robert and Zhang, Xuejie},
title = {Refining Word Embeddings Using Intensity Scores for Sentiment Analysis},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2788182},
doi = {10.1109/TASLP.2017.2788182},
abstract = {Word embeddings that provide continuous low-dimensional vector representations of words have been extensively used for various natural language processing tasks. However, existing context-based word embeddings such as Word2vec and GloVe typically fail to capture sufficient sentiment information, which may result in words with similar vector representations having an opposite sentiment polarity e.g., good and bad, thus degrading sentiment analysis performance. To tackle this problem, recent studies have suggested learning sentiment embeddings to incorporate the sentiment polarity positive and negative information from labeled corpora. This study adopts another strategy to learn sentiment embeddings. Instead of creating a new word embedding from labeled corpora, we propose a word vector refinement model to refine existing pretrained word vectors using real-valued sentiment intensity scores provided by sentiment lexicons. The idea of the refinement model is to improve each word vector such that it can be closer in the lexicon to both semantically and sentimentally similar words i.e., those with similar intensity scores and further away from sentimentally dissimilar words i.e., those with dissimilar intensity scores. An obvious advantage of the proposed method is that it can be applied to any pretrained word embeddings. In addition, the intensity scores can provide more fine-grained real-valued sentiment information than binary polarity labels to guide the refinement process. Experimental results show that the proposed refinement model can improve both conventional word embeddings and previously proposed sentiment embeddings for binary, ternary, and fine-grained sentiment classification on the SemEval and Stanford Sentiment Treebank datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {671–681},
numpages = {11}
}

@article{10.1109/TASLP.2018.2789721,
author = {Su, Jinsong and Zeng, Jiali and Xiong, Deyi and Liu, Yang and Wang, Mingxuan and Xie, Jun},
title = {A Hierarchy-to-Sequence Attentional Neural Machine Translation Model},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2789721},
doi = {10.1109/TASLP.2018.2789721},
abstract = {Although sequence-to-sequence attentional neural machine translation NMT has achieved great progress recently, it is confronted with two challenges: learning optimal model parameters for long parallel sentences and well exploiting different scopes of contexts. In this paper, partially inspired by the idea of segmenting a long sentence into short clauses, each of which can be easily translated by NMT, we propose a hierarchy-to-sequence attentional NMT model to handle these two challenges. Our encoder takes the segmented clause sequence as input and explores a hierarchical neural network structure to model words, clauses, and sentences at different levels, particularly with two layers of recurrent neural networks modeling semantic compositionality at the word and clause level. Correspondingly, the decoder sequentially translates segmented clauses and simultaneously applies two types of attention models to capture contexts of interclause and intraclause for translation prediction. In this way, we can not only improve parameter learning, but also well explore different scopes of contexts for translation. Experimental results on Chinese–English and English–German translation demonstrate the superiorities of the proposed model over the conventional NMT model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {623–632},
numpages = {10}
}

@article{10.1109/TASLP.2017.2782491,
author = {Zohourian, Mehdi and Enzner, Gerald and Martin, Rainer},
title = {Binaural Speaker Localization Integrated Into an Adaptive Beamformer for Hearing Aids},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2782491},
doi = {10.1109/TASLP.2017.2782491},
abstract = {In this paper, we present and compare novel algorithms to localize simultaneous speakers using four microphones distributed on a pair of binaural hearing aids. The framework consists of two groups of localization algorithms, namely, beamforming-based and statistical model based localization algorithms. We first generalize our previously proposed methods based on beamforming techniques to the binaural configuration with 2 $times$ 2 microphones. Next, we contribute two statistical model based methods for binaural localization using the maximum likelihood approach that also takes head-related transfer functions and unknown noise conditions into account. The methods enable the localization of multiple source positions for all azimuth angles and do not require prior training of binaural cues. The proposed localization algorithms are integrated into a generalized side-lobe canceller GSC to extract the desired speaker in the presence of competing speakers and background noise and when the head of the listener turns. The GSC components are adapted with the frequency-wise target presence probability and the frame-wise broadband direction-of-arrival DOA estimates that track the turns of the listener's head. We evaluate the performance of the localization algorithms individually and also in the context of the adaptive binaural beamformer in various noisy and reverberant conditions. Finally, we introduce a new adaptive beamformer, which combines the GSC with multichannel speech presence probability estimation and achieves superior source separation performance in noisy environment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {515–528},
numpages = {14}
}

@article{10.1109/TASLP.2017.2788198,
author = {Dorfan, Yuval and Plinge, Axel and Hazan, Gershon and Gannot, Sharon},
title = {Distributed Expectation-Maximization Algorithm for Speaker Localization in Reverberant Environments},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2788198},
doi = {10.1109/TASLP.2017.2788198},
abstract = {Localization of acoustic sources has attracted a considerable amount of research attention in recent years. A major obstacle to achieving high localization accuracy is the presence of reverberation, the influence of which obviously increases with the number of active speakers in the room. Human hearing is capable of localizing acoustic sources even in extreme conditions. In this study, we propose to combine a method based on human hearing mechanisms and a modified incremental distributed expectation-maximization IDEM algorithm. Rather than using phase difference measurements that are modeled by a mixture of complex-valued Gaussians, as proposed in the original IDEM framework, we propose to use time difference of arrival measurements in multiple subbands and model them by a mixture of real-valued truncated Gaussians. Moreover, we propose to first filter the measurements in order to reduce the effect of the multipath conditions. The proposed method is evaluated using both simulated data and real-life recordings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {682–695},
numpages = {14}
}

@article{10.1109/TASLP.2017.2785283,
author = {Tan, Chuanqi and Wei, Furu and Zhou, Qingyu and Yang, Nan and Du, Bowen and Lv, Weifeng and Zhou, Ming},
title = {Context-Aware Answer Sentence Selection With Hierarchical Gated Recurrent Neural Networks},
year = {2018},
issue_date = {March 2018},
publisher = {IEEE Press},
volume = {26},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2785283},
doi = {10.1109/TASLP.2017.2785283},
abstract = {In this paper, we study the task of reading comprehension style answer sentence selection that aims to select the best sentence from a given passage to answer a question. Unlike most previous works that match the question and each candidate sentence separately, we observe that the context information among sentences in the same passage plays a vital role in this task. We propose modeling context information with hierarchical gated recurrent neural networks. Specifically, we first apply a word level recurrent neural network to model the context independent matching between the question and each candidate sentence. We then employ a sentence level recurrent neural network to incorporate the context information among all candidate sentences. Moreover, we introduce the gate mechanism to select matching information before feeding into recurrent neural networks at both word and sentence level. Experiments on the WikiQA and SQuAD datasets show that our model outperforms state-of-the-art methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {540–549},
numpages = {10}
}

@article{10.1109/TASLP.2017.2774925,
author = {Nikunen, Joonas and Diment, Aleksandr and Virtanen, Tuomas},
title = {Separation of Moving Sound Sources Using Multichannel NMF and Acoustic Tracking},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2774925},
doi = {10.1109/TASLP.2017.2774925},
abstract = {In this paper, we propose a method for separation of moving sound sources. The method is based on first tracking the sources and then estimation of source spectrograms using multichannel nonnegative matrix factorization NMF and extracting the sources from the mixture by single-channel Wiener filtering. We propose a novel multichannel NMF model with time-varying mixing of the sources denoted by spatial covariance matrices SCM and provide update equations for optimizing model parameters minimizing squared Frobenius norm. The SCMs of the model are obtained based on estimated directions of arrival of tracked sources at each time frame. The evaluation is based on established objective separation criteria and using real recordings of two and three simultaneous moving sound sources. The compared methods include conventional beamforming and ideal ratio mask separation. The proposed method is shown to exceed the separation quality of other evaluated blind approaches according to all measured quantities. Additionally, we evaluate the method's susceptibility toward tracking errors by comparing the separation quality achieved using annotated ground truth source trajectories.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {281–295},
numpages = {15}
}

@article{10.1109/TASLP.2017.2774919,
author = {Wu, Chunyang and Gales, Mark J. F. and Ragni, Anton and Karanasou, Penny and Sim, Khe Chai},
title = {Improving Interpretability and Regularization in Deep Learning},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2774919},
doi = {10.1109/TASLP.2017.2774919},
abstract = {Deep learning approaches yield state-of-the-art performance in a range of tasks, including automatic speech recognition. However, the highly distributed representation in a deep neural network DNN or other network variations is difficult to analyze, making further parameter interpretation and regularization challenging. This paper presents a regularization scheme acting on the activation function output to improve the network interpretability and regularization. The proposed approach, referred to as activation regularization, encourages activation function outputs to satisfy a target pattern. By defining appropriate target patterns, different learning concepts can be imposed on the network. This method can aid network interpretability and also has the potential to reduce overfitting. The scheme is evaluated on several continuous speech recognition tasks: the Wall Street Journal continuous speech recognition task, eight conversational telephone speech tasks from the IARPA Babel program and a U.S. English broadcast news task. On all the tasks, the activation regularization achieved consistent performance gains over the standard DNN baselines.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {256–265},
numpages = {10}
}

@article{10.1109/TASLP.2017.2775800,
author = {Sward, Johan and Li, Hongbin and Jakobsson, Andreas},
title = {Off-Grid Fundamental Frequency Estimation},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2775800},
doi = {10.1109/TASLP.2017.2775800},
abstract = {In this paper, we propose a gridless method for estimating an unknown number of fundamental frequencies. Starting with a conventional dictionary matrix, containing sets of candidate fundamental frequencies and their corresponding harmonics, a nonconvex log-sum cost function is formed such that it imposes the harmonic structure and treats every fundamental frequency in the dictionary as a parameter. The cost function is iteratively decreased by minimizing a surrogate function, and, in each iteration, the fundamental frequencies are refined, whereas redundant parameters are omitted from the dictionary. The proposed method is tested on both real and simulated data, showing its preferred performance as compared to other state-of-the-art multipitch estimators.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {296–303},
numpages = {8}
}

@article{10.1109/TASLP.2017.2779862,
author = {Chien, Jen-Tzung},
title = {Bayesian Nonparametric Learning for Hierarchical and Sparse Topics},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2779862},
doi = {10.1109/TASLP.2017.2779862},
abstract = {This paper presents the Bayesian nonparametric BNP learning for hierarchical and sparse topics from natural language. Traditionally, the Indian buffet process provides the BNP prior on a binary matrix for an infinite latent feature model consisting of a flat layer of topics. The nested model paves an avenue to construct a tree model instead of a flat-layer model. This paper presents the nested Indian buffet process nIBP to achieve the sparsity and flexibility in topic model where the model complexity and topic hierarchy are learned from the groups of words. The mixed membership modeling is conducted by representing a document using the tree nodes or dishes that a document or a customer chooses according to the nIBP scenario. A tree stick-breaking process is implemented to select topic weights from a subtree for flexible topic modeling. Such an nIBP relaxes the constraint of adopting a single tree path in the nested Chinese restaurant process nCRP and, therefore, improves the variety of topic representation for heterogeneous documents. A Gibbs sampling procedure is developed to infer the nIBP topic model. Compared to the nested hierarchical Dirichlet process nhDP, the compactness of the estimated topics in a tree using nIBP is improved. Experimental results show that the proposed nIBP reduces the error rate of nCRP and nhDP by 18% and 8% on Reuters task for document classification, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {422–435},
numpages = {14}
}

@article{10.1109/TASLP.2017.2765819,
author = {May, Tobias},
title = {Robust Speech Dereverberation With a Neural Network-Based Post-Filter That Exploits Multi-Conditional Training of Binaural Cues},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2765819},
doi = {10.1109/TASLP.2017.2765819},
abstract = {This study presents an algorithm for binaural speech dereverberation based on the supervised learning of short-term binaural cues. The proposed system combined a delay-and-sum beamformer with a neural network-based post-filter that attenuated reverberant components in individual time-frequency units. A multi-conditional training procedure was used to simulate the uncertainties of short-term binaural cues in response to room reverberation by mixing the direct part of head related impulse responses HRIRs with diffuse noise. Despite being trained with only anechoic HRIRs, the proposed dereverberation algorithm was tested in a variety of reverberant environments and achieved considerable improvements relative to a coherence-based approach in terms of three objective metrics reflecting speech quality and speech intelligibility. Moreover, a systematic evaluation showed that the proposed system generalized very well to a wide range of acoustic conditions, including various measured binaural room impulse responses reflecting different reverberation times, azimuth positions spanning the entire frontal hemifield, various source-receiver distances as well as different artificial heads.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {406–414},
numpages = {9}
}

@article{10.1109/TASLP.2017.2778151,
author = {Rehr, Robert and Gerkmann, Timo},
title = {On the Importance of Super-Gaussian Speech Priors for Machine-Learning Based Speech Enhancement},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2778151},
doi = {10.1109/TASLP.2017.2778151},
abstract = {For enhancing noisy signals, machine-learning based single-channel speech enhancement schemes exploit prior knowledge about typical speech spectral structures. To ensure a good generalization and to meet requirements in terms of computational complexity and memory consumption, certain methods restrict themselves to learning speech spectral envelopes. We refer to these approaches as machine-learning spectral envelope MLSE-based approaches. In this paper, we show by means of theoretical and experimental analyses that for MLSE-based approaches, super-Gaussian priors allow for a reduction of noise between speech spectral harmonics which is not achievable using Gaussian estimators such as the Wiener filter. For the evaluation, we use a deep neural network based phoneme classifier and a low-rank nonnegative matrix factorization framework as examples of MLSE-based approaches. A listening experiment and instrumental measures confirm that while super-Gaussian priors yield only moderate improvements for classic enhancement schemes, for MLSE-based approaches super-Gaussian priors clearly make an important difference and significantly outperform Gaussian priors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {357–366},
numpages = {10}
}

@article{10.1109/TASLP.2017.2778150,
author = {Djaziri-Larbi, Sonia and Mahe, Gael and Mezghani, Imen and Turki, Monia and Jaidane, Meriem},
title = {Watermark-Driven Acoustic Echo Cancellation},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2778150},
doi = {10.1109/TASLP.2017.2778150},
abstract = {The performance of adaptive acoustic echo cancelers AEC is sensitive to the nonstationarity and correlation of speech signals. In this paper, we explore a new approach based on an adaptive AEC driven by data hidden in speech, to enhance the AEC robustness. We propose a two-stage AEC, where the first stage is a classical NLMS-based AEC driven by the far-end speech. In the signal, we embed—in an extended conception of data hiding—an imperceptible white and stationary signal, i.e., a watermark. The goal of the second stage AEC is to identify the misalignment of the first stage. It is driven by the watermark solely and takes advantage of its appropriate properties stationary and white to improve the robustness of the two-stage AEC to the nonstationarity and correlation of speech, and thus reduce the overall system misadjustment. We test two kinds of implementations: in the first implementation, referred to as adaptive watermark driven AEC A-WdAEC, the watermark is a white stationary Gaussian noise. Driven by this signal, the second stage converges faster than the classical AEC and provides better performance in steady state. In the second implementation, referred to as maximum length sequences WdAEC MLS-WdAEC, the watermark is built from MLS. Thus, the second stage performs a block identification of the first stage misalignment, given by the circular correlation watermark/preprocessed version of the first stage residual echo. The advantage of this implementation lies in its robustness against noise and undermodeling. Simulation results show the relevance of the “WdAEC” approach, compared to the classical “error-driven AEC.”},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {367–378},
numpages = {12}
}

@article{10.1109/TASLP.2017.2779405,
author = {Stahl, Johannes and Mowlaee, Pejman},
title = {A Pitch-Synchronous Simultaneous Detection-Estimation Framework for Speech Enhancement},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2779405},
doi = {10.1109/TASLP.2017.2779405},
abstract = {Speech enhancement methods formulated in the short-time Fourier transform STFT domain vary in the statistical assumptions made on the STFT coefficients, in the optimization criteria applied or in the models of the signal components. Recently, approaches relying on a stochastic-deterministic speech model have been proposed. The deterministic part of the signal corresponds to harmonically related sinusoids, often used to represent voiced speech. The stochastic part models signal components that are not captured by the deterministic components. In this paper, we consider this scenario under a new perspective yielding three main contributions. First, a pitch-synchronous signal representation is considered and shown to be advantageous for the estimation of the harmonic model parameters. Second, we model the harmonic amplitudes in voiced speech as random variables with frequency bin dependent Gamma distributions. Finally, distinct estimators for the different models of voiced speech, unvoiced speech, and speech absence are derived. To select from the arising estimates, we take into account the mutual impact of detection and estimation by proposing a binary decision framework that is derived from a Bayesian risk function. The resulting pitch-synchronous stochastic-deterministic estimator outperforms several benchmark methods in terms of speech intelligibility and perceived quality predicted by instrumental measures for various noise types and different signal-to-noise ratios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {436–450},
numpages = {15}
}

@article{10.1109/TASLP.2017.2772846,
author = {Chen, Kehai and Zhao, Tiejun and Yang, Muyun and Liu, Lemao and Tamura, Akihiro and Wang, Rui and Utiyama, Masao and Sumita, Eiichiro},
title = {A Neural Approach to Source Dependence Based Context Model for Statistical Machine Translation},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2772846},
doi = {10.1109/TASLP.2017.2772846},
abstract = {In statistical machine translation, translation prediction considers not only the aligned source word itself but also its source contextual information. Learning context representation is a promising method for improving translation results, particularly through neural networks. Most of the existing methods process context words sequentially and neglect source long-distance dependencies. In this paper, we propose a novel neural approach to source dependence-based context representation for translation prediction. The proposed model is capable of not only encoding source long-distance dependencies but also capturing functional similarities to better predict translations i.e., word form translations and ambiguous word translations. To verify our method, the proposed mode is incorporated into phrase-based and hierarchical phrase-based translation models, respectively. Experiments on large-scale Chinese-to-English and English-to-German translation tasks show that the proposed approach achieves significant improvement over the baseline systems and outperforms several existing context-enhanced methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {266–280},
numpages = {15}
}

@article{10.1109/TASLP.2017.2775742,
author = {Dropuljic, Branimir and Mijic, Igor and Petrinovic, Davor and Jovanovic, Tanja and Cosic, Kresimir and Dropuljic, Branimir and Mijic, Igor and Petrinovic, Davor and Jovanovic, Tanja and Cosic, Kresimir},
title = {Vocal Analysis of Acoustic Startle Responses},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2775742},
doi = {10.1109/TASLP.2017.2775742},
abstract = {This paper presents an extensive statistical analysis of the acoustic startle response of two vocal parameters: fundamental frequency F0 and root-mean-square energy E, as well as of the orbicularis oculi eyeblink surface electromyography sEMG. An experiment was conducted in which fourteen participants were exposed to acoustic startle stimuli of varying parameters, i.e., intensity level, duration, rise time, and spectral type, during periods of sustained phonation. Voice recordings of the phonations were taken alongside several physiological signals, of which only the sEMG was analyzed in this paper. Response features peak value, peak time, latency, rise time, fall time, and duration were extracted on F0, E and sEMG data, and statistical analysis was conducted using linear mixed effects models to show the response behavior with the varying stimuli. The results for vocal F0 and E data were congruent with sEMG data and earlier work in the field. The results demonstrated that vocal analysis can be used as a feasible alternative to the sEMG eyeblink analysis of acoustic startle responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {318–329},
numpages = {12}
}

@article{10.1109/TASLP.2017.2778423,
author = {Mesaros, Annamaria and Heittola, Toni and Benetos, Emmanouil and Foster, Peter and Lagrange, Mathieu and Virtanen, Tuomas and Plumbley, Mark D.},
title = {Detection and Classification of Acoustic Scenes and Events: Outcome of the DCASE 2016 Challenge},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2778423},
doi = {10.1109/TASLP.2017.2778423},
abstract = {Public evaluation campaigns and datasets promote active development in target research areas, allowing direct comparison of algorithms. The second edition of the challenge on detection and classification of acoustic scenes and events DCASE 2016 has offered such an opportunity for development of the state-of-the-art methods, and succeeded in drawing together a large number of participants from academic and industrial backgrounds. In this paper, we report on the tasks and outcomes of the DCASE 2016 challenge. The challenge comprised four tasks: acoustic scene classification, sound event detection in synthetic audio, sound event detection in real-life audio, and domestic audio tagging. We present each task in detail and analyze the submitted systems in terms of design and performance. We observe the emergence of deep learning as the most popular classification method, replacing the traditional approaches based on Gaussian mixture models and support vector machines. By contrast, feature representations have not changed substantially throughout the years, as mel frequency-based representations predominate in all tasks. The datasets created for and used in DCASE 2016 are publicly available and are a valuable resource for further research.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {379–393},
numpages = {15}
}

@article{10.1109/TASLP.2017.2772340,
author = {Bando, Yoshiaki and Itoyama, Katsutoshi and Konyo, Masashi and Tadokoro, Satoshi and Nakadai, Kazuhiro and Yoshii, Kazuyoshi and Kawahara, Tatsuya and Okuno, Hiroshi G.},
title = {Speech Enhancement Based on Bayesian Low-Rank and Sparse Decomposition of Multichannel Magnitude Spectrograms},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2772340},
doi = {10.1109/TASLP.2017.2772340},
abstract = {This paper presents a blind multichannel speech enhancement method that can deal with the time-varying layout of microphones and sound sources. Since nonnegative tensor factorization NTF separates a multichannel magnitude or power spectrogram into source spectrograms without phase information, it is robust against the time-varying mixing system. This method, however, requires prior information such as the spectral bases templates of each source spectrogram in advance. To solve this problem, we develop a Bayesian model called robust NTF Bayesian RNTF that decomposes a multichannel magnitude spectrogram into target speech and noise spectrograms based on their sparseness and low rankness. Bayesian RNTF is applied to the challenging task of speech enhancement for a microphone array distributed on a hose-shaped rescue robot. When the robot searches for victims under collapsed buildings, the layout of the microphones changes over time and some of them often fail to capture target speech. Our method robustly works under such situations, thanks to its characteristic of time-varying mixing system. Experiments using a 3-m hose-shaped rescue robot with eight microphones show that the proposed method outperforms conventional blind methods in enhancement performance by the signal-to-noise ratio of 1.03 dB.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {215–230},
numpages = {16}
}

@article{10.1109/TASLP.2017.2773198,
author = {Ruan, Yu-Ping and Chen, Qian and Ling, Zhen-Hua},
title = {A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2773198},
doi = {10.1109/TASLP.2017.2773198},
abstract = {In this paper, we propose a sequential neural encoder with latent structured description SNELSD for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory LSTM units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {231–242},
numpages = {12}
}

@article{10.1109/TASLP.2017.2778948,
author = {Chung, Cheng-Tao and Lee, Lin-Shan},
title = {Unsupervised Discovery of Structured Acoustic Tokens With Applications to Spoken Term Detection},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2778948},
doi = {10.1109/TASLP.2017.2778948},
abstract = {In this paper, we compare two paradigms for unsupervised discovery of structured acoustic tokens directly from speech corpora without any human annotation. The multigranular paradigm seeks to capture all available information in the corpora with multiple sets of tokens for different model granularities. The hierarchical paradigm attempts to jointly learn several levels of signal representations in a hierarchical structure. The two paradigms are unified within a theoretical framework in this paper. Query-by-example spoken term detection QbE-STD experiments on the query by example search on speech task dataset of MediaEval 2015 verifies the competitiveness of the acoustic tokens. The enhanced relevance score proposed in this work improves both paradigms for the task of QbE-STD. We also list results on the ABX evaluation task of the Zero Resource Challenge 2015 for comparison of the paradigms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {394–405},
numpages = {12}
}

@article{10.1109/TASLP.2017.2772831,
author = {Alexandridis, Anastasios and Mouchtaris, Athanasios},
title = {Multiple Sound Source Location Estimation in Wireless Acoustic Sensor Networks Using DOA Estimates: The Data-Association Problem},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2772831},
doi = {10.1109/TASLP.2017.2772831},
abstract = {In this paper, we consider the data-association problem for the localization of multiple sound sources in a wireless acoustic sensor network, where each node is a microphone array, using direction of arrival DOA estimates. The data-association problem arises because the central node that receives the multiple DOA estimates from the nodes cannot know to which source they belong. Hence, the DOAs from the different nodes that correspond to the same source must be found in order to perform accurate localization. We present a method to identify the correct association of DOAs to the sources and thus accurately estimate their locations. Our method results in high association and localization accuracy in realistic scenarios with missed detections, reverberation, noise, and moving sources and outperforms other recently proposed methods. It also incorporates a bitrate reduction scheme in order to keep the amount of information that needs to be transmitted in the network at low levels without affecting performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {342–356},
numpages = {15}
}

@article{10.1109/TASLP.2017.2774921,
author = {Gully, Amelia J. and Daffern, Helena and Murphy, Damian T. and Gully, Amelia J. and Daffern, Helena and Murphy, Damian T.},
title = {Diphthong Synthesis Using the Dynamic 3D Digital Waveguide Mesh},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2774921},
doi = {10.1109/TASLP.2017.2774921},
abstract = {Articulatory speech synthesis has the potential to offer more natural sounding synthetic speech than established concatenative or parametric synthesis methods. Time-domain acoustic models are particularly suited to the dynamic nature of the speech signal, and recent work has demonstrated the potential of dynamic vocal tract models that accurately reproduce the vocal tract geometry. This paper presents a dynamic 3D digital waveguide mesh DWM vocal tract model, capable of movement to produce diphthongs. The technique is compared to existing dynamic 2D and static 3D DWM models, for both monophthongs and diphthongs. The results indicate that the proposed model provides improved formant accuracy over existing DWM vocal tract models. Furthermore, the computational requirements of the proposed method are significantly lower than those of comparable dynamic simulation techniques. This paper represents another step toward a fully functional articulatory vocal tract model which will lead to more natural speech synthesis systems for use across society.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {243–255},
numpages = {13}
}

@article{10.1109/TASLP.2017.2761233,
author = {Aichinger, Philipp and Hagmuller, Martin and Schneider-Stickler, Berit and Schoentgen, Jean and Pernkopf, Franz},
title = {Tracking of Multiple Fundamental Frequencies in Diplophonic Voices},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2761233},
doi = {10.1109/TASLP.2017.2761233},
abstract = {Diplophonia is a type of pathological voice in which two fundamental frequencies $f_o$ are present simultaneously. Specialized audio analyzers that can handle up to two $f_o$s in diplophonic voices are in their infancy. We propose the tracking of up to two $f_o$ s in diplophonic voices by audio waveform modeling AWM, which involves obtaining candidates by repetitive execution of the Viterbi algorithm, followed by waveform Fourier synthesis, and heuristic candidate selection with majority voting. Our approach is evaluated with reference $f_o$-tracks obtained from laryngeal high-speed videos of 29 sustained phonations and compared to state-of-the-art tracking algorithms for multiple $f_o$s. An accurate and a fast variant of our algorithm are tested. The median error rate of the accurate variant is 6.52%, whereas the most accurate benchmark achieves 11.11%. The fast variant is more than twice as fast as the fastest relevant benchmark, and the median error rate is 9.52%. Furthermore, illustrative results of connected speech analysis are reported. Our approach may help to improve detection and analysis of diplophonia in clinical research and practice, as well as to advance synthesis of disordered voices.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {330–341},
numpages = {12}
}

@article{10.1109/TASLP.2017.2775860,
author = {Menzies, Dylan and Galvez, Marcos F. Simon and Fazi, Filippo Maria},
title = {A Low-Frequency Panning Method With Compensation for Head Rotation},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2775860},
doi = {10.1109/TASLP.2017.2775860},
abstract = {Amplitude panning produces interaural time difference ITD cues that help localize images in directions between loudspeakers. However, if the panning gains are static, then the ITD cues produced in this way vary inconsistently as the listener's head rotates, compared with a real source, and so the dynamic ITD cues are inaccurate. This effect destabilizes the perception of the image and overall scene, and is worse for loudspeakers that are more widely spaced relative to the listener. Based on a simple head model that is accurate in the low-frequency ITD regime, the ITD is calculated for a general field, including those produced by panning. A simple formula is derived relating head orientation, image direction, and a field description vector. Panning functions are then found that compensate for head orientation and are valid for any image direction. For the special case when the listener is facing the image, the functions are equivalent to vector base amplitude panning. The performance is first assessed objectively using measured binaural responses, rather than the simple head model. Subjective comparison is then made with pre-existing listening tests and new listening tests in which the listener's head is tracked to control the panning gains in real-time. These show that images can be stabilized as predicted, and, furthermore, that with the same panning functions, images can be produced in all directions using two loudspeakers placed in front.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {304–317},
numpages = {14}
}

@article{10.1109/TASLP.2017.2780989,
author = {Mirbagheri, Majid and Atlas, Les and Lee, Adrian K. C.},
title = {Regression Factor Analysis With an Application to Continuous HRIR Measurement},
year = {2018},
issue_date = {February 2018},
publisher = {IEEE Press},
volume = {26},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2780989},
doi = {10.1109/TASLP.2017.2780989},
abstract = {Linear time-varying LTV regression models play a central role in input–output analysis of many real-world dynamical systems. Most existing models for such systems consider either a switching dynamics with a discrete latent variable driving the system or a linear dynamics directly applied to the regression coefficients. These models usually fall short of capturing structural regularities or the total variability in the dynamics of the system. Addressing these issues in this paper, we propose a method to parametrize joint variations of regression coefficients in LTV systems with continuous latent variables based on factor analysis technique, i.e., regression factor analysis . By using a linear Gaussian dynamical model for the time evolution of factor weights, our model constrains the dynamics of the process. Our inference scheme takes advantage of the expectation–maximization algorithm to estimate the model parameters. We show how our proposed algorithm can be utilized in a key application for fast continuous measurement of personalized head-related impulse responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {415–421},
numpages = {7}
}

@article{10.1109/TASLP.2017.2759338,
author = {Deng, Jun and Xu, Xinzhou and Zhang, Zixing and Fruhholz, Sascha and Schuller, Bjorn and Jun Deng and Xinzhou Xu and Zixing Zhang and Fruhholz, Sascha and Schuller, Bjorn},
title = {Semisupervised Autoencoders for Speech Emotion Recognition},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2759338},
doi = {10.1109/TASLP.2017.2759338},
abstract = {Despite the widespread use of supervised learning methods for speech emotion recognition, they are severely restricted due to the lack of sufficient amount of labelled speech data for the training. Considering the wide availability of unlabelled speech data, therefore, this paper proposes semisupervised autoencoders to improve speech emotion recognition. The aim is to reap the benefit from the combination of the labelled data and unlabelled data. The proposed model extends a popular unsupervised autoencoder by carefully adjoining a supervised learning objective. We extensively evaluate the proposed model on the INTERSPEECH 2009 Emotion Challenge database and other four public databases in different scenarios. Experimental results demonstrate that the proposed model achieves state-of-the-art performance with a very small number of labelled data on the challenge task and other tasks, and significantly outperforms other alternative methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {31–43},
numpages = {13}
}

@article{10.1109/TASLP.2017.2761236,
author = {Abel, Johannes and Fingscheidt, Tim and Abel, Johannes and Fingscheidt, Tim},
title = {Artificial Speech Bandwidth Extension Using Deep Neural Networks for Wideband Spectral Envelope Estimation},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2761236},
doi = {10.1109/TASLP.2017.2761236},
abstract = {Estimating a wideband spectral envelope having only narrowband speech at hand is a challenging task. In this paper, we explore ways to do so in the context of an artificial speech bandwidth extension ABE framework. Starting from a typical hidden Markov model HMM/Gaussian mixture model baseline scheme, we investigate two types of features, topologies, and regularization approaches of deep neural networks DNNs to obtain estimates of wideband spectral envelopes with smallest cepstral distance to the original ones. In order to draw realistic conclusions, we employ a database for test, which is acoustically different to the training and validation speech material. Interestingly, it turns out that a DNN regression approach outperforms all other investigated methods, although the HMM has been dropped. Cepstral distance was reduced by 1.18 dB, wideband PESQ was improved by 0.23 MOS points, and a subjective comparison category rating listening test showed a significant preference of the best DNN ABE approach versus narrowband speech of 1.37 CMOS points.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {71–83},
numpages = {13}
}

@article{10.1109/TASLP.2017.2752364,
author = {Chakrabarty, Soumitro and Habets, Emanuel A. P. and Chakrabarty, Soumitro and Habets, Emanuel A. P.},
title = {A Bayesian Approach to Informed Spatial Filtering With Robustness Against DOA Estimation Errors},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2752364},
doi = {10.1109/TASLP.2017.2752364},
abstract = {A Bayesian approach to spatial filtering is presented, which is robust to uncertain or erroneous direction-of-arrival DOA information. The proposed framework aims to capture multiple sound sources at each time-frequency instant with an arbitrary direction-dependent gain, while attenuating diffuse sound and noise. For robustness, the DOA corresponding to each sound source is assumed to be a discrete random variable with a prior defined on a discrete set of candidate DOAs over the whole DOA space. With this assumption, the desired spatial filter is given as a weighted sum of spatial filters corresponding to a specific combination of probable DOA values, where the weights are given by the joint posterior probabilities of the combination of DOA values. Assuming the whole DOA space as the support for each random variable results in redundant computations and contributes to a high computational cost. To alleviate this problem, a narrowband DOA estimate-based posterior probability approximation method is proposed, which isolates regions in the DOA space with high probability of containing the actual source DOAs to compute time-adaptive supports for each random variable. Through experimental analysis, we demonstrate the robustness of the proposed framework against DOA estimation errors. Experimental evaluation with simulated and measured room impulse responses, in terms of objective performance measures, demonstrates the effectiveness of the framework to perform spatial filtering in noisy and reverberant acoustic environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {145–160},
numpages = {16}
}

@article{10.1109/TASLP.2017.2764545,
author = {Chen, Kuan-Yu and Liu, Shih-Hung and Chen, Berlin and Wang, Hsin-Min and Kuan-Yu Chen and Shih-Hung Liu and Berlin Chen and Hsin-Min Wang},
title = {An Information Distillation Framework for Extractive Summarization},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2764545},
doi = {10.1109/TASLP.2017.2764545},
abstract = {In the context of natural language processing, representation learning has emerged as a newly active research subject because of its excellent performance in many applications. Learning representations of words is a pioneering study in this school of research. However, paragraph or sentence and document embedding learning is more suitable/reasonable for some realistic tasks such as document summarization. Nevertheless, classic paragraph embedding methods infer the representation of a given paragraph by considering all of the words occurring in the paragraph. Consequently, those stop or function words that occur frequently may mislead the embedding learning process to produce a misty paragraph representation. Motivated by these observations, our major contributions in this paper are threefold. First, we propose a novel unsupervised paragraph embedding method, named the essence vector EV model, which aims at not only distilling the most representative information from a paragraph but also excluding the general background information to produce a more informative low-dimensional vector representation for the paragraph of interest. Second, in view of the increasing importance of spoken content processing, an extension of the EV model, named the denoising essence vector D-EV model, is proposed. The D-EV model not only inherits the advantages of the EV model but also can infer a more robust representation for a given spoken paragraph against imperfect speech recognition. Third, a new summarization framework, which can take both relevance and redundancy information into account simultaneously, is also introduced. We evaluate the proposed embedding methods i.e., EV and D-EV and the summarization framework on two benchmark summarization corpora. The experimental results

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repo_name: goyalkaraniit/acm-cr