text:
ASLP.2019.2959222,
author = {Ghasemzadeh, Hamzeh and Arjmandi, Meisam K.},
title = {Toward Optimum Quantification of Pathology-Induced Noises: An Investigation of Information Missed by Human Auditory System},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2959222},
doi = {10.1109/TASLP.2019.2959222},
abstract = {Clinical diagnosis of voice disorder and evaluation of therapy outcome heavily rely on accurate quantification of voice quality, which is closely tied to the physiology and function of the laryngeal mechanism. Considering the evaluation methodology of the voice, two main categories of auditory-perceptual assessment and acoustic analysis can be identified. This article presents a new approach for acoustic analysis of voice quality, which brings several advantages to the field. The proposed approach is non-parametric in the sense that it does not require the estimation of the fundamental frequency or spectral response of the vocal tract. This reduces the computational complexity of the measurement and reduces the possible errors due to inaccurate estimation of those parameters. Additionally, the method does not make any assumption about the phonetic context and hence has the potential to be applied to connected speech. The proposed method benefits from the multiresolution structure of the wavelet analysis for estimating the noisy component of a voice in the spectro-temporal domain. The informativeness of the estimated noise for voice quality distinction is examined based on different noise-quantification approaches. It is shown that deviation from the model of the human auditory system (HAS) leads to performance improvement. Through several analyses, it is argued that using models of HAS for quantification of the noise leads to significant loss of information relevant to voice quality. Findings from this article suggest that perception-based measures of voice quality are highly restricted in capturing important aspects of acoustic that could assist with voice quality distinctions. This characteristic is inherent to HAS and cannot be alleviated, highlighting a significant limitation of perception-based measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {519–528},
numpages = {10}
}

@article{10.1109/TASLP.2019.2955286,
author = {Morgenstern, Hai and Rafaely, Boaz},
title = {Perceptually-Transparent Online Estimation of Two-Channel Room Transfer Function for Sound Calibration},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955286},
doi = {10.1109/TASLP.2019.2955286},
abstract = {Sound calibration is employed in many commercial audio systems for improving sound quality. This process includes the estimation of the room transfer function (RTF) between each loudspeaker and a microphone located at the listeners’ position. Current methods for RTF estimation employ calibration signals, such as noise or tones, in a dedicated process applied to each loudspeaker separately. Such an estimation disrupts normal playback, is time consuming, and requires user intervention. A perceptually-transparent online RTF estimation method for a two-channel system, which employs calibration signals generated using the original audio signals and complementary filters, is proposed in this article. These calibration signals are uncorrelated across the two channels, which facilitates the online estimation of both channels using a single microphone. Estimation performance is investigated for an experimental system and displays low estimation errors. Finally, a subjective evaluation via a listening test shows that playback of calibration signals is perceptually-transparent under some of the conditions investigated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {332–342},
numpages = {11}
}

@article{10.1109/TASLP.2020.2966868,
author = {Zhang, Liwen and Shi, Ziqiang and Han, Jiqing},
title = {Pyramidal Temporal Pooling With Discriminative Mapping for Audio Classification},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2966868},
doi = {10.1109/TASLP.2020.2966868},
abstract = {Audio signals are temporally-structured data, and learning their discriminative representations containing temporal information is crucial for the audio classification. In this article, we propose an audio representation learning method with a hierarchical pyramid structure called pyramidal temporal pooling (PTP) which aims to capture the temporal information of an entire audio sample. By stacking a global temporal pooling layer on multiple local temporal pooling layers, the PTP can capture the high-level temporal dynamics of the input feature sequence in an unsupervised way. Furthermore, in the top global temporal pooling layer, we jointly optimize a learnable discriminative mapping (DM) and a softmax classifier. Such that, a joint learning method for the discriminative audio representations and the classifier called DM-PTP is also presented. By treating the temporal encoding as a low-level constraint of a bi-level optimization problem, the DM-PTP can produce the discriminative representation while maintaining the temporal information of the whole sequence. For an audio sample with an arbitrary time duration, both our PTP and DM-PTP can encode the input feature sequence with arbitrary length into a fixed-length representation. Without using any data augmentation and ensemble learning methods, both PTP and DM-PTP outperform the state-of-the-art CNNs on the audio event recognition (AER) dataset, and can achieve comparable performance on the DCASE 2018 acoustic scene classification (ASC) dataset compared with other best models in the challenge.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {770–784},
numpages = {15}
}

@article{10.1109/TASLP.2020.2978409,
author = {Hashemgeloogerdi, Sahar and Bocko, Mark F.},
title = {Adaptive Feedback Cancellation in Hearing Aids Based on Orthonormal Basis Functions With Prediction-Error Method Based Prewhitening},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2978409},
doi = {10.1109/TASLP.2020.2978409},
abstract = {Acoustic feedback is a persistent problem in hearing aids, which limits the achievable amplification and may severely degrade the sound quality by producing howling artifacts. A potential approach to feedback cancellation is to estimate the feedback path utilizing an adaptive filter. However, estimation of the feedback path suffers a large model error, known as the bias, due to the correlation between the loudspeaker and source signals. A prediction-error method (PEM) based prewhitening filter has been widely utilized to reduce the bias. This approach, however, requires a large number of adaptive parameters, thus increasing the computational complexity, reducing the convergence rate, and limiting the added stable gain. We introduce an adaptive feedback cancellation (AFC) algorithm derived based on the orthonormal basis functions (OBFs) for closed-loop identification of the feedback path by minimizing the prediction error. The OBFs are defined by a set of fixed poles and a small number of adaptive tap-output weights. We study two methods for obtaining the fixed poles, an inherently stable least-squares method and a log-scale frequency resolution method. The poles are then embedded as the <italic>a priori</italic> information into the algorithm. The proposed algorithm is extensively evaluated with speech and music source signals and with sudden changes in the feedback path. The experimental results show that the proposed method significantly increases the added stable gain, accelerates the convergence rate, and enhances the sound quality compared to state-of-the-art, while requiring far fewer adaptive parameters which leads to reduced computational complexity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1260–1269},
numpages = {10}
}

@article{10.1109/TASLP.2019.2957883,
author = {Cherkassky, Dani and Gannot, Sharon},
title = {Successive Relative Transfer Function Identification Using Blind Oblique Projection},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2957883},
doi = {10.1109/TASLP.2019.2957883},
abstract = {Distortionless speech extraction in a reverberant environment can be achieved by applying a beamforming algorithm, provided that the relative transfer functions (RTFs) of the sources and the covariance matrix of the noise are known. In this paper, the challenge of RTF identification in a multi-speaker scenario is addressed. We propose a successive RTF identification (SRI) technique, based on the sole assumption that sources do not become simultaneously active. That is, we address the challenge of estimating the RTF of a specific speech source while assuming that the RTFs of all other active sources in the environment were previously estimated in an earlier stage. The RTF of interest is identified by applying the blind oblique projection (BOP)-SRI technique. When a new speech source is identified, the BOP algorithm is applied. BOP results in a null steering toward the RTF of interest, by means of applying an oblique projection to the microphone measurements. We prove that by artificially increasing the rank of the range of the projection matrix, the RTF of interest can be identified. An experimental study is carried out to evaluate the performance of the BOP-SRI algorithm in various signal to noise ratio (SNR) and signal to interference ratio (SIR) conditions and to demonstrate its effectiveness in speech extraction tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {474–486},
numpages = {13}
}

@article{10.1109/TASLP.2019.2949928,
author = {Wen, Hao-Xiang and Yang, Sen-Quan and Hong, Yuan-Quan and Luo, Huan},
title = {A Partial Update Adaptive Algorithm for Sparse System Identification},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2949928},
doi = {10.1109/TASLP.2019.2949928},
abstract = {A sparse partial update (SPU) algorithm and its improved version improved SPU (ISPU) algorithm, are proposed in this paper for sparse system identification. The SPU first categorizes its filter coefficients into active and inactive coefficients. Then all the active coefficients are included in each adaptation, while only a small portion of the inactive coefficients is periodically chosen to be included in the adaptation. The SPU emphasizes convergence of the active coefficients by updating them at every adaptation, while the periodical adaptation of the inactive coefficients ensures its robustness and tracking capability. By eliminating most of the inactive coefficients from adaptation, the SPU significantly reduces the number of adapting coefficients in each adaptation. The decline in the number of adapting coefficients eventually leads to improvement in both computation and convergence speed. To avoid performance degradation in the case of identifying a dispersive system, an ISPU is further proposed by making modifications to the SPU. The simulation results demonstrate that the ISPU not only outperforms other sparse adaptive algorithms in identifying a sparse system but also performs at least as well as the NLMS algorithm in identifying a dispersive system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {240–255},
numpages = {16}
}

@article{10.1109/TASLP.2020.2984852,
author = {Varanasi, Vishnuvardhan and Gupta, Harshit and Hegde, Rajesh M.},
title = {A Deep Learning Framework for Robust DOA Estimation Using Spherical Harmonic Decomposition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2984852},
doi = {10.1109/TASLP.2020.2984852},
abstract = {Spherical harmonic decomposition facilitates decomposing the sound pressure at different microphones into independent functions of frequency, azimuth and elevation of the source and microphone locations. This decomposition facilitates the extraction of two sets of features containing different information about elevation and azimuth of the source for the direction of arrival (DOA) estimation. These features can be given as input to a learning approach for the estimation of azimuth and elevation separately. This approach aims at breaking down the problem of DOA estimation into azimuth and elevation estimation separately. An advantage of this is the reduction in computational complexity when compared with the joint DOA estimation. This facilitates a straightforward extension of this approach to denser DOA search grids. The contribution of this paper is threefold. First, we propose spherical harmonic magnitude and phase features and discuss the information present in these features regarding the azimuth and elevation of the source. Second, we propose the convolutional neural network architectures for DOA estimation. Third, we analyse the training, run-time computational complexities and propose to extend the DOA estimation approach to dense DOA search grid rather than restricting to a sparse DOA search grid. The performance of conventional DOA estimation approaches degrades in case of a noisy and reverberant environment. Several advancements to the existing DOA estimation approaches have been recently proposed. However, to the best of the authors’ knowledge, learning approaches to DOA estimation with dense DOA search grids with few frames in the context of spherical arrays have not been proposed. Performance evaluation is carried out using simulated as well as real datasets. The proposed approach is also evaluated on LOCATA dataset in the context of a moving source. The results are motivating enough to consider the application of the proposed method in practical scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1248–1259},
numpages = {12}
}

@article{10.1109/TASLP.2020.2980989,
author = {Huang, Gongping and Benesty, Jacob and Cohen, Israel and Chen, Jingdong},
title = {A Simple Theory and New Method of Differential Beamforming With Uniform Linear Microphone Arrays},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2980989},
doi = {10.1109/TASLP.2020.2980989},
abstract = {This article presents a theoretical study of differential beamforming with uniform linear arrays. By defining a forward spatial difference operator, any order of the spatial difference of the observed signals can be represented as a product of a difference operator matrix and the microphone array observations. Consequently, differential beamforming is implemented in two stages, where the first one obtains spatial difference of the observations and the second stage optimizes the beamformer. The major contributions of this article are as follows. First, we propose a new theory of differential beamforming with uniform linear arrays, which shows clearly the connection between the conventional differential beamforming and the null-constrained differential beamforming methods. This provides some new insight into the design of differential beamformers. Second, we deduce some new differential beamformers, where conventional beamforming may be seen as a particular case. Specifically, we derive the maximum white noise gain (MWNG), maximum directivity factor (MDF), parameterized MDF, and parameterized maximum front-to-back ratio differential beamformers. Third, we further extend the idea of how to design optimal differential beamformers by combining both the observed signals and their spatial differences.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1079–1093},
numpages = {15}
}

@article{10.1109/TASLP.2020.2968738,
author = {Kolb\ae{}k, Morten and Tan, Zheng-Hua and Jensen, S\o{}ren Holdt and Jensen, Jesper},
title = {On Loss Functions for Supervised Monaural Time-Domain Speech Enhancement},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2968738},
doi = {10.1109/TASLP.2020.2968738},
abstract = {Many deep learning-based speech enhancement algorithms are designed to minimize the mean-square error (MSE) in some transform domain between a predicted and a target speech signal. However, optimizing for MSE does not necessarily guarantee high speech quality or intelligibility, which is the ultimate goal of many speech enhancement algorithms. Additionally, only little is known about the impact of the loss function on the emerging class of time-domain deep learning-based speech enhancement systems. We study how popular loss functions influence the performance of time-domain deep learning-based speech enhancement systems. First, we demonstrate that perceptually inspired loss functions might be advantageous over classical loss functions like MSE. Furthermore, we show that the learning rate is a crucial design parameter even for adaptive gradient-based optimizers, which has been generally overlooked in the literature. Also, we found that waveform matching performance metrics must be used with caution as they in certain situations can fail completely. Finally, we show that a loss function based on scale-invariant signal-to-distortion ratio (SI-SDR) achieves good general performance across a range of popular speech enhancement evaluation metrics, which suggests that SI-SDR is a good candidate as a general-purpose loss function for speech enhancement systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {825–838},
numpages = {14}
}

@article{10.1109/TASLP.2019.2951995,
author = {M\o{}ller, Martin Bo and \O{}stergaard, Jan},
title = {A Moving Horizon Framework for Sound Zones},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2951995},
doi = {10.1109/TASLP.2019.2951995},
abstract = {Sound zones are generated to provide independent audio reproduction to multiple people in the same room using loudspeakers. In this article, sound zones are formulated in terms of a moving horizon framework. This framework allows the reproduction scenario to be time-varying and adapt to changes e.g. in the location of the zones or in the audio signal. The framework is tested using both simulated and measured room impulse responses from eight loudspeakers in a rectangular room. The performance is investigated using signals limited between 35–500 Hz, but the framework is not limited to a particular frequency range. The experimental results show that it is possible to gain on the order of 4 dB higher separation between the zones using the proposed framework, relative to a conventional time-invariant solution. This gain arises from knowledge about the audio content currently being reproduced in the zones, and it is obtained without deteriorating the reproduction accuracy or increasing the signal energy injected into the loudspeakers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {256–265},
numpages = {10}
}

@article{10.1109/TASLP.2019.2961556,
author = {Azad, Abul and Mili, Lamine},
title = {Robust Speech Filter and Voice Encoder Parameter Estimation Using the Phase–Phase Correlator},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2961556},
doi = {10.1109/TASLP.2019.2961556},
abstract = {In recent years, linear prediction voice encoders have become very efficient in terms of computing execution time and channel bandwidth usage while providing, in the absence of impulsive noise, natural sounding synthetic speech signals. This good performance has been achieved via the use of a maximum likelihood parameter estimation of an auto-regressive model of order ten that best fits the speech signal under the assumption that the signal and the noise are Gaussian stochastic processes. However, this method breaks down in the presence of impulse noise, which is common in practice, resulting in harsh or non-intelligible audio signals. In this paper, we propose a robust estimator of correlation, the Phase-Phase correlator that is able to cope with impulsive noise. Utilizing this correlator, we develop a Robust Mixed Excitation Linear Prediction encoder that provides improved audio quality for voiced, unvoiced, and transition speech segments. This is achieved by applying a statistical test to robust Mahalanobis distances for identifying the outliers in the corrupted speech signal, which are then replaced with filtered signals. Simulation results reveal that the proposed estimator of correlator outperforms in variance, bias, and breakdown point compared to three other robust approaches based on the arcsin law, the polarity coincidence correlator, and the median-of-ratio estimator without sacrificing the encoder bandwidth efficiency and the compression gain while remaining compatible with real-time applications. Furthermore, in the presence of impulsive noise, the proposed speech encoder speech subjective quality outperforms the state-of-the-art in terms of mean opinion score.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {592–604},
numpages = {13}
}

@article{10.1109/TASLP.2020.2964953,
author = {Kukanov, Ivan and Trong, Trung Ngo and Hautam\"{a}ki, Ville and Siniscalchi, Sabato Marco and Salerno, Valerio Mario and Lee, Kong Aik},
title = {Maximal Figure-of-Merit Framework to Detect Multi-Label Phonetic Features for Spoken Language Recognition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2964953},
doi = {10.1109/TASLP.2020.2964953},
abstract = {Bottleneck features (BNFs) generated with a deep neural network (DNN) have proven to boost spoken language recognition accuracy over basic spectral features significantly. However, BNFs are commonly extracted using language-dependent tied-context phone states as learning targets. Moreover, BNFs are less phonetically expressive than the output layer in a DNN, which is usually not used as a speech feature because of its very high dimensionality hindering further post-processing. In this article, we put forth a novel deep learning framework to overcome all of the above issues and evaluate it on the 2017 NIST Language Recognition Evaluation (LRE) challenge. We use manner and place of articulation as speech attributes, which lead to low-dimensional “universal” phonetic features that can be defined across all spoken languages. To model the asynchronous nature of the speech attributes while capturing their intrinsic relationships in a given speech segment, we introduce a new training scheme for deep architectures based on a Maximal Figure of Merit (MFoM) objective. MFoM introduces non-differentiable metrics into the backpropagation-based approach, which is elegantly solved in the proposed framework. The experimental evidence collected on the recent NIST LRE 2017 challenge demonstrates the effectiveness of our solution. In fact, the performance of speech language recognition (SLR) systems based on spectral features is improved for more than 5% absolute Cavg. Finally, the F1 metric can be brought from 77.6% up to 78.1% by combining the conventional baseline phonetic BNFs with the proposed articulatory attribute features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {682–695},
numpages = {14}
}

@article{10.1109/TASLP.2019.2957871,
author = {Beerends, John G. and Neumann, Niels M. P. and van den Broek, Egon L. and Llagostera Casanovas, Anna and Menendez, Jovana Torres and Schmidmer, Christian and Berger, Jens},
title = {Subjective and Objective Assessment of Full Bandwidth Speech Quality},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2957871},
doi = {10.1109/TASLP.2019.2957871},
abstract = {With the introduction of fullband speech coding the question arises what role frequency components above 14 kHz play in speech quality assessment. On the one hand, our results show that bandwidth limitation from 24 kHz down to 14 kHz is not audible to even the most critical subject. On the other hand, 14–24 kHz band limited, audible levels of noise clearly decrease the perceived quality, especially for young subjects with healthy ears. Furthermore, modern high-quality voice links, using the latest speech codecs, often apply advanced buffering schemes that introduce a new type of audible degradation: micropauses. We investigated the impact of i) bandwidth limitation, ii) coding schemes, iii) micropause, and iv) noise on the perceived quality. Subjective results and objective predictions based on ITU-T recommendation P.863 POLQA are compared. For accurate prediction of the impact of micropauses and noise degradations small model adaptations are suggested. In contrast codec degradations and bandwidth limitation are already predicted with very high accuracy by POLQA: <italic>r</italic> = 0.98, RMSE<sup>*</sup> = 0.05 Mean Opinion Score (MOS).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {440–449},
numpages = {10}
}

@article{10.1109/TASLP.2020.2982291,
author = {Urbanietz, Christoph and Enzner, Gerald},
title = {Direct Spatial-Fourier Regression of HRIRs from Multi-Elevation Continuous-Azimuth Recordings},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982291},
doi = {10.1109/TASLP.2020.2982291},
abstract = {Individual head-related impulse responses (HRIRs) have been recognized as a key to creating high-fidelity virtual auditory spaces. Thus, fast and comprehensive acquisition of individual HRIRs has been a subject of continued research. Traditional stop-and-go measurement at discrete angles is time consuming and additionally requires spatial interpolation that has been tackled by mapping discrete HRIR tables to a spatial Fourier format. Uniformly continuous-azimuth recording with a moving apparatus, on the other hand, reduces acquisition time, but is noise-limited due to a very short observation time per angle. In the interest of both fast acquisition and high accuracy, in this paper, we propose direct retrieval of a spatial Fourier format from continuous-azimuth recordings at multiple simultaneous discrete elevations. Specifically, we fit a generative continuous-azimuth model of the recorded signal, based on the spatial Fourier representation, to the continuous recordings of individuals by least-squares. In this approach, the model is meant to entirely capture the spatial variation of the HRIR in azimuth, while the duration of the recording then systematically controls the noise rejection. The proposed time-domain treatment is free of block artifacts, but is numerically demanding. We outline how to take the special structure of the involved covariance matrices into account. Experimental results with simulated data and real recordings demonstrate that the HRIR performance in terms of binaural cues and reproducibility benefits from the proposed algorithm. Our method is hence practical in terms of low measurement time and high performance, while benefiting from increased computational power of current computers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1129–1142},
numpages = {14}
}

@article{10.1109/TASLP.2020.2982285,
author = {Gfeller, Beat and Frank, Christian and Roblek, Dominik and Sharifi, Matt and Tagliasacchi, Marco and Velimirovi\'{c}, Mihajlo},
title = {SPICE: Self-Supervised Pitch Estimation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982285},
doi = {10.1109/TASLP.2020.2982285},
abstract = {We propose a model to estimate the fundamental frequency in monophonic audio, often referred to as pitch estimation. We acknowledge the fact that obtaining ground truth annotations at the required temporal and frequency resolution is a particularly daunting task. Therefore, we propose to adopt a self-supervised learning technique, which is able to estimate pitch without any form of supervision. The key observation is that pitch shift maps to a simple translation when the audio signal is analysed through the lens of the constant-Q transform (CQT). We design a self-supervised task by feeding two shifted slices of the CQT to the same convolutional encoder, and require that the difference in the outputs is proportional to the corresponding difference in pitch. In addition, we introduce a small model head on top of the encoder, which is able to determine the confidence of the pitch estimate, so as to distinguish between voiced and unvoiced audio. Our results show that the proposed method is able to estimate pitch at a level of accuracy comparable to fully supervised models, both on clean and noisy audio samples, although it does not require access to large labeled datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1118–1128},
numpages = {11}
}

@article{10.1109/TASLP.2019.2955276,
author = {Tan, Ke and Wang, DeLiang},
title = {Learning Complex Spectral Mapping With Gated Convolutional Recurrent Networks for Monaural Speech Enhancement},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955276},
doi = {10.1109/TASLP.2019.2955276},
abstract = {Phase is important for perceptual quality of speech. However, it seems intractable to directly estimate phase spectra through supervised learning due to their lack of spectrotemporal structure in it. Complex spectral mapping aims to estimate the real and imaginary spectrograms of clean speech from those of noisy speech, which simultaneously enhances magnitude and phase responses of speech. Inspired by multi-task learning, we propose a gated convolutional recurrent network (GCRN) for complex spectral mapping, which amounts to a causal system for monaural speech enhancement. Our experimental results suggest that the proposed GCRN substantially outperforms an existing convolutional neural network (CNN) for complex spectral mapping in terms of both objective speech intelligibility and quality. Moreover, the proposed approach yields significantly higher STOI and PESQ than magnitude spectral mapping and complex ratio masking. We also find that complex spectral mapping with the proposed GCRN provides an effective phase estimate.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {380–390},
numpages = {11}
}

@article{10.1109/TASLP.2019.2959257,
author = {Mogami, Shinichi and Takamune, Norihiro and Kitamura, Daichi and Saruwatari, Hiroshi and Takahashi, Yu and Kondo, Kazunobu and Ono, Nobutaka},
title = {Independent Low-Rank Matrix Analysis Based on Time-Variant Sub-Gaussian Source Model for Determined Blind Source Separation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2959257},
doi = {10.1109/TASLP.2019.2959257},
abstract = {Independent low-rank matrix analysis (ILRMA) is a fast and stable method of blind audio source separation. Conventional ILRMAs assume time-variant (super-)Gaussian source models, which can only represent signals that follow a super-Gaussian distribution. In this article, we focus on ILRMA based on a generalized Gaussian distribution (GGD-ILRMA) and propose a new type of GGD-ILRMA that adopts a time-variant sub-Gaussian distribution for the source model. We propose a new update scheme called generalized iterative projection for homogeneous source models (GIP-HSM) and obtain a convergence-guaranteed update rule for demixing spatial parameters by combining the GIP-HSM scheme and the majorization-minimization (MM) algorithm. Furthermore, a new extension of the MM algorithm is proposed for the convergence acceleration by applying the majorization-equalization algorithm to a multivariate case. In the experimental evaluation, we show the versatility of the proposed method, i.e., the proposed time-variant sub-Gaussian source model can be applied to various types of source signal.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {503–518},
numpages = {16}
}

@article{10.1109/TASLP.2020.2967567,
author = {Moro-Vel\'{a}quez, Laureano and Hern\'{a}ndez-Garc\'{\i}a, Estefan\'{\i}a and G\'{o}mez-Garc\'{\i}a, Jorge A. and Godino-Llorente, Juan I. and Dehak, Najim},
title = {Analysis of the Effects of Supraglottal Tract Surgical Procedures in Automatic Speaker Recognition Performance},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2967567},
doi = {10.1109/TASLP.2020.2967567},
abstract = {This article evaluates the impact in the performance of state-of-the-art automatic speaker recognition schemes of three surgical procedures modifying the supraglottal tract structures of speakers. To do so, a new corpus (Cuco) was recorded, containing the speech of 107 speakers before and after surgery. Speakers were divided into four groups depending on the type of surgery: tonsillectomy, functional endoscopy sinus surgery (FESS), septoplasty, and controls. The analyzed speaker recognition schemes were i-vectors, i-vectors with supervised Universal Background Model, i-vectors employing Time-delay Deep Neural Networks and x-vectors. In all cases, probabilistic linear discriminant analysis was employed in the back-end. Results show changes in the speech of patients who underwent tonsillectomy or FESS after surgery in contrast to controls or patients who had a septoplasty, where not significant variations are observed. These changes increase the Equal Error Rate (EER) of the analyzed speaker recognition schemes for the septoplasty and FESS groups when employing enrollment data recorded before the surgery. Moreover, surgery has a similar influence in the speech of female and male speakers with respect to the analyzed schemes. In consequence, results suggest that it is advisable to update the speaker's enrollment speech after three months following supraglottal tract surgery to ensure that the effects of the operation and post-operative recovery period do not influence the performance of the automatic speaker recognition systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {798–812},
numpages = {15}
}

@article{10.1109/TASLP.2020.2963954,
author = {Wang, Zhongqing and Sun, Qingying and Li, Shoushan and Zhu, Qiaoming and Zhou, Guodong},
title = {Neural Stance Detection With Hierarchical Linguistic Representations},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2963954},
doi = {10.1109/TASLP.2020.2963954},
abstract = {Stance detection aims to assign a stance label (i.e., <italic>favor</italic> or <italic>against</italic>) to a post towards a specific target. Recently, there is a growing interest in adopting neural models to detect stance of a document. However, most of these works focus on modeling the sequence of words to learn document representation, though other linguistic information, such as sentiment and arguments, are correlated with the stance of document, and may inspire us to explore the stance. In this article, we propose a <italic>hierarchical attention neural model</italic> to well study various linguistic information to better represent a document via hierarchical linguistic representations. In addition, we propose a hierarchical network with attention mechanism to weight the importance of various kinds of linguistic information, and learn the mutual attention between document and linguistic information. Detail evaluation on two benchmark datasets demonstrates the effectiveness of proposed hierarchical network with attention mechanism.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {635–645},
numpages = {11}
}

@article{10.1109/TASLP.2020.2984089,
author = {L\'{o}pez-Espejo, Iv\'{a}n and Tan, Zheng-Hua and Jensen, Jesper},
title = {Improved External Speaker-Robust Keyword Spotting for Hearing Assistive Devices},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2984089},
doi = {10.1109/TASLP.2020.2984089},
abstract = {For certain applications, keyword spotting (KWS) requires some degree of personalization. This is the case for KWS for hearing assistive devices, e.g., hearing aids, where only the device user should be allowed to trigger the KWS system. In this paper, we first develop a new realistic hearing aid experimental framework. Next, using this framework we show that the performance of a state-of-the-art multi-task deep learning architecture exploiting cepstral features for joint KWS and users’ own-voice/external speaker detection drops significantly. To overcome this problem, we use phase difference information through GCC-PHAT (Generalized Cross-Correlation with PHAse Transform)-based coefficients along with log-spectral magnitude features. In addition, we demonstrate that working in the perceptually-motivated constant-Q transform (CQT) domain instead of in the short-time Fourier transform (STFT) domain allows for the generation of compact and coherent features which provide superior KWS performance. Our experimental results show that our CQT-based proposal achieves a relative KWS accuracy improvement of around 18% compared to using cepstral features while dramatically decreasing the number of multiplications in the multi-task architecture, which is key in the context of low-resource devices like hearing assistive devices.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1233–1247},
numpages = {15}
}

@article{10.1109/TASLP.2020.2986886,
author = {Kano, Takatomo and Sakti, Sakriani and Nakamura, Satoshi},
title = {End-to-End Speech Translation With Transcoding by Multi-Task Learning for Distant Language Pairs},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2986886},
doi = {10.1109/TASLP.2020.2986886},
abstract = {Directly translating spoken utterances from a source language to a target language is challenging because it requires a fundamental transformation in both linguistic and para/non-linguistic features. Traditional speech-to-speech translation approaches concatenate automatic speech recognition (ASR), text-to-text machine translation (MT), and text-to-speech synthesizer (TTS) by text information. The current state-of-the-art models for ASR, MT, and TTS have mainly been built using deep neural networks, in particular, an attention-based encoder-decoder neural network with an attention mechanism. Recently, several works have constructed end-to-end direct speech-to-text translation by combining ASR and MT into a single model. However, the usefulness of these models has only been investigated on language pairs of similar syntax and word order (e.g., English-French or English-Spanish). For syntactically distant language pairs (e.g., English-Japanese), speech translation requires distant word reordering. Furthermore, parallel texts with corresponding speech utterances that are suitable for training end-to-end speech translation are generally unavailable. Collecting such corpora is usually time-consuming and expensive. This article proposes the first attempt to build an end-to-end direct speech-to-text translation system on syntactically distant language pairs that suffer from long-distance reordering. We train the model on English (subject-verb-object (SVO) word order) and Japanese (SOV word order) language pairs. To guide the attention-based encoder-decoder model on this difficult problem, we construct end-to-end speech translation with transcoding and utilize curriculum learning (CL) strategies that gradually train the network for end-to-end speech translation tasks by adapting the decoder or encoder parts. We use TTS for data augmentation to generate corresponding speech utterances from the existing parallel text data. Our experiment results show that the proposed approach provides significant improvements compared with conventional cascade models and the direct speech translation approach that uses a single model without transcoding and CL strategies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1342–1355},
numpages = {14}
}

@article{10.1109/TASLP.2020.2971828,
author = {Kim, Yeongseok and Park, Youngjin},
title = {Blockwise Weighted Least Square Active Noise Control for CPU-GPU Architecture},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2971828},
doi = {10.1109/TASLP.2020.2971828},
abstract = {Active noise control (ANC) is a technology which lowers the noise level by using the principle of destructive interference of sound wave. Even though recent developments in digital signal processing (DSP) made it possible to implement ANC algorithms in real-time, insufficient computational power is still one of the challenges to solve. In the previous research, as a way of overcoming the lack of computational power, CPU-GPU architecture was proposed so that ANC algorithms utilize the massive computing power of GPU without suffering from the block data transfer between CPU and GPU memories. However, for the feasibility test of the proposed CPU-GPU architecture in the previous research, a conventional block ANC algorithm was used, and ANC algorithm which can fully utilize the massive computing power of GPU has not been developed. In this article, ANC algorithm, which directly derives blockwise least square solution through GPU computation while generating control signals through CPU computation, is proposed. Based on the observation about speaker saturation and increase of noise level after applying the conventional blockwise least square solution, a new cost function for preventing such problems is also proposed. Therefore, blockwise weighted least square ANC (BWLS-ANC) algorithm, which derives blockwise least square solution minimizing the proposed cost function through GPU computation while generating control signals through CPU computation, is proposed throughout this research. Problems of conventional blockwise least square solution upon ANC applications are observed through simulations and experiments. The feasibility of the proposed BWLS-ANC algorithm is verified through experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {951–963},
numpages = {13}
}

@article{10.1109/TASLP.2019.2955246,
author = {Nguyen, Minh and Ngo, Gia H. and Chen, Nancy F.},
title = {Hierarchical Character Embeddings: Learning Phonological and Semantic Representations in Languages of Logographic Origin Using Recursive Neural Networks},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955246},
doi = {10.1109/TASLP.2019.2955246},
abstract = {Logographs (Chinese characters) have recursive structures (i.e. hierarchies of sub-units in logographs) that contain phonological and semantic information, as developmental psychology literature suggests that native speakers leverage on the structures to learn how to read. Exploiting these structures could potentially lead to better embeddings that can benefit many downstream tasks. We propose building hierarchical logograph (character) embeddings from logograph recursive structures using treeLSTM, a recursive neural network. Using recursive neural network imposes a prior on the mapping from logographs to embeddings since the network must read in the sub-units in logographs according to the order specified by the recursive structures. Based on human behavior in language learning and reading, we hypothesize that modeling logographs’ structures using recursive neural network should be beneficial. To verify this claim, we consider two tasks (1) predicting logographs’ Cantonese pronunciation from logographic structures and (2) language modeling. Empirical results show that the proposed hierarchical embeddings outperform baseline approaches. Diagnostic analysis suggests that hierarchical embeddings constructed using treeLSTM is less sensitive to distractors, thus is more robust, especially on complex logographs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {461–473},
numpages = {13}
}

@article{10.1109/TASLP.2020.2983589,
author = {Cobos, Maximo and Antonacci, Fabio and Comanducci, Luca and Sarti, Augusto},
title = {Frequency-Sliding Generalized Cross-Correlation: A Sub-Band Time Delay Estimation Approach},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2983589},
doi = {10.1109/TASLP.2020.2983589},
abstract = {The generalized cross-correlation(GCC) is regarded as the most popular approach for estimating the time difference of arrival (TDOA) between the signals received at two sensors. Time delay estimates are obtained by maximizing the GCC output, where the direct-path delay is usually observed as a prominent peak. Moreover, GCCs play also an important role in steered response power (SRP) localization algorithms, where the SRP functional can be written as an accumulation of the GCCs computed from multiple sensor pairs. Unfortunately, the accuracy of TDOA estimates is affected by multiple factors, including noise, reverberation and signal bandwidth. In this paper, a sub-band approach for time delay estimation aimed at improving the performance of the conventional GCC is presented. The proposed method is based on the extraction of multiple GCCs corresponding to different frequency bands of the cross-power spectrum phase in a sliding-window fashion. The major contributions of this paper include: 1) a sub-band GCC representation of the cross-power spectrum phase that, despite having a reduced temporal resolution, provides a more suitable representation for estimating the true TDOA; 2) such matrix representation is shown to be rank one in the ideal noiseless case, a property that is exploited in more adverse scenarios to obtain a more robust and accurate GCC; 3) we propose a set of low-rank approximation alternatives for processing the sub-band GCC matrix, leading to better TDOA estimates and source localization performance. An extensive set of experiments is presented to demonstrate the validity of the proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1270–1281},
numpages = {12}
}

@article{10.1109/TASLP.2020.2975423,
author = {Laufer-Goldshtein, Bracha and Talmon, Ronen and Gannot, Sharon},
title = {Global and Local Simplex Representations for Multichannel Source Separation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2975423},
doi = {10.1109/TASLP.2020.2975423},
abstract = {The problem of blind audio source separation (BASS) in noisy and reverberant conditions is addressed by a novel approach, termed Global and LOcal Simplex Separation (GLOSS), which integrates full- and narrow-band simplex representations. We show that the eigenvectors of the correlation matrix between time frames in a certain frequency band form a simplex that organizes the frames according to the speaker activities in the corresponding band. We propose to build two simplex representations: one global based on a broad frequency band and one local based on a narrow band. In turn, the two representations are combined to determine the dominant speaker in each time-frequency (TF) bin. Using the identified dominating speakers, a spectral mask is computed and is utilized for extracting each of the speakers using spatial beamforming followed by spectral postfiltering. The performance of the proposed algorithm is demonstrated using real-life recordings in various noisy and reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {914–928},
numpages = {15}
}

@article{10.1109/TASLP.2020.2966857,
author = {Ando, Atsushi and Masumura, Ryo and Kamiyama, Hosana and Kobashikawa, Satoshi and Aono, Yushi and Toda, Tomoki},
title = {Customer Satisfaction Estimation in Contact Center Calls Based on a Hierarchical Multi-Task Model},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2966857},
doi = {10.1109/TASLP.2020.2966857},
abstract = {This article presents a novel customer satisfaction (CS) estimation method that outputs both turn-level and call-level estimations simultaneously. Our key idea is to directly apply turn-level estimation results to call-level estimation and optimize them jointly; previous works treat both as being independent. Our proposal applies long short-term memory recurrent neural networks (LSTM-RNNs) to turn-level and call-level CS estimation to capture long-range sequential context in contact center calls. In addition, both networks are hierarchically stacked so as to use turn-level estimation results for call-level estimation directly. In order to learn the relationship between the two tasks, we also introduce joint optimization training to the stacked model. Several analyses of turn-level and call-level CS are provided on acted and real calls to support the proposed method. Experiments show that the proposed framework outperforms the conventional methods in both turn-level and call-level estimations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {715–728},
numpages = {14}
}

@article{10.1109/TASLP.2019.2956145,
author = {Wang, Xin and Takaki, Shinji and Yamagishi, Junichi},
title = {Neural Source-Filter Waveform Models for Statistical Parametric Speech Synthesis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2956145},
doi = {10.1109/TASLP.2019.2956145},
abstract = {Neural waveform models have demonstrated better performance than conventional vocoders for statistical parametric speech synthesis. One of the best models, called WaveNet, uses an autoregressive (AR) approach to model the distribution of waveform sampling points, but it has to generate a waveform in a time-consuming sequential manner. Some new models that use inverse-autoregressive flow (IAF) can generate a whole waveform in a one-shot manner but require either a larger amount of training time or a complicated model architecture plus a blend of training criteria. As an alternative to AR and IAF-based frameworks, we propose a neural source-filter (NSF) waveform modeling framework that is straightforward to train and fast to generate waveforms. This framework requires three components to generate waveforms: a source module that generates a sine-based signal as excitation, a non-AR dilated-convolution-based filter module that transforms the excitation into a waveform, and a conditional module that pre-processes the input acoustic features for the source and filter modules. This framework minimizes spectral-amplitude distances for model training, which can be efficiently implemented using short-time Fourier transform routines. As an initial NSF study, we designed three NSF models under the proposed framework and compared them with WaveNet using our deep learning toolkit. It was demonstrated that the NSF models generated waveforms at least 100 times faster than our WaveNet-vocoder, and the quality of the synthetic speech from the best NSF model was comparable to that from WaveNet on a large single-speaker Japanese speech corpus.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {402–415},
numpages = {14}
}

@article{10.1109/TASLP.2020.2975419,
author = {De Sena, Enzo and Cvetkovi\'{c}, Zoran and Hac\i{}habibo\u{g}lu, H\"{u}seyin and Moonen, Marc and van Waterschoot, Toon},
title = {Localization Uncertainty in Time-Amplitude Stereophonic Reproduction},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2975419},
doi = {10.1109/TASLP.2020.2975419},
abstract = {This article studies the effects of inter-channel time and level differences in stereophonic reproduction on perceived localization uncertainty, which is defined as how difficult it is for a listener to tell where a sound source is located. Towards this end, a computational model of localization uncertainty is proposed first. The model calculates inter-aural time and level difference cues, and compares them to those associated to free-field point-like sources. The comparison is carried out using a particular distance functional that replicates the increased uncertainty observed experimentally with inconsistent inter-aural time and level difference cues. The model is validated by formal listening tests, achieving a Pearson correlation of 0.99. The model is then used to predict localization uncertainty for stereophonic setups and a listener in central and off-central positions. Results show that amplitude methods achieve a slightly lower localization uncertainty for a listener positioned exactly in the center of the sweet spot. As soon as the listener moves away from that position, the situation reverses, with time-amplitude methods achieving a lower localization uncertainty.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1000–1015},
numpages = {16}
}

@article{10.1109/TASLP.2019.2950774,
author = {Hoffmann, Falk-Martin and Nelson, Philip Arthur and Fazi, Filippo Maria},
title = {DOA Estimation Performance With Circular Arrays in Sound Fields With Finite Rate of Innovation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2950774},
doi = {10.1109/TASLP.2019.2950774},
abstract = {A novel Direction-of-Arrival (DOA) estimation method based on a plane wave sound field model was recently proposed for circular microphone arrays <xref ref-type="bibr" rid="ref1">[1]</xref>. This article presents a detailed theoretical analysis of the method that relies on a Finite Rate of Innovation (FRI) assumption, investigating the impact of different theoretical acoustic source models. The method's estimation accuracy, and robustness against noisy measurement data and deviations from the model are investigated. The estimation performance is validated and assessed on the basis of results obtained from both simulations and experimental data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {171–184},
numpages = {14}
}

@article{10.1109/TASLP.2020.2980436,
author = {Sterpu, George and Saam, Christian and Harte, Naomi},
title = {How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2980436},
doi = {10.1109/TASLP.2020.2980436},
abstract = {Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, <italic>AV Align</italic>, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of <italic>AV Align</italic> and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and LRS2. We find that <italic>AV Align</italic> learns to align acoustic and visual representations of speech at the frame level on TCD-TIMIT in a generally monotonic pattern. We also determine the cause of initially seeing no improvement over audio-only speech recognition on the more challenging LRS2. We propose a regularisation method which involves predicting lip-related Action Units from visual representations. Our regularisation method leads to better exploitation of the visual modality, with performance improvements between 7% and 30% depending on the noise level. Furthermore, we show that the alternative <italic>Watch, Listen, Attend, and Spell</italic> network is affected by the same problem as <italic>AV Align</italic>, and that our proposed approach can effectively help it learn visual representations. Our findings validate the suitability of the regularisation method to AVSR and encourage researchers to rethink the multimodal convergence problem when having one dominant modality.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1052–1064},
numpages = {13}
}

@article{10.1109/TASLP.2020.2971419,
author = {Yu, Dongyan and Duan, Huiping and Fang, Jun and Zeng, Bing},
title = {Predominant Instrument Recognition Based on Deep Neural Network With Auxiliary Classification},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2971419},
doi = {10.1109/TASLP.2020.2971419},
abstract = {Instrument recognition plays very important roles in music information retrieval, sound source separation and automatic music transcription. However, due to different playing styles and audio qualities, this task cannot be accomplished easily. Simultaneous existence of multiple instruments in polyphonic music increases the challenge to a greater extent. This article mainly focus on the identification of the predominant instruments in polyphonic music. We propose to construct a network with an auxiliary classification designed based on the onset groups and instrument families. The principal classification and the auxiliary classification enable the network to learn the instrument categories and groups jointly in a pattern of multitask learning. The IRMAS dataset is adopted in the experiment to extract the mel-spectrogram and six other types of features. The micro and macro average of precisions, recalls and F1 measures are used to evaluate the classification results. The effect of multitask learning, batch normalization and center loss in the predominant instrument recognition are demonstrated by various experiments. By selecting the loss ratios through a development set, the micro and macro F1 measures of our proposed network can reach 0.685 and 0.597, which are 10.7% and 16.4% higher than those obtained by the baseline, the ConvNet presented in <xref ref-type="bibr" rid="ref1">[1]</xref>.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {852–861},
numpages = {10}
}

@article{10.1109/TASLP.2019.2955293,
author = {Diez, Mireia and Burget, Luk\'{a}\v{s} and Landini, Federico and \v{C}ernock\'{y}, Jan},
title = {Analysis of Speaker Diarization Based on Bayesian HMM With Eigenvoice Priors},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955293},
doi = {10.1109/TASLP.2019.2955293},
abstract = {In our previous work, we introduced our Bayesian Hidden Markov Model with eigenvoice priors, which has been recently recognized as the state-of-the-art model for Speaker Diarization. In this article we present a more complete analysis of the Diarization system. The inference of the model is fully described and derivations of all update formulas are provided for a complete understanding of the algorithm. An extensive analysis on the effect, sensitivity and interactions of all model parameters is provided, which might be used as a guide for their optimal setting. The newly introduced speaker regularization coefficient allows us to control the number of speakers inferred in an utterance. A naive speaker model merging strategy is also presented, which allows to drive the variational inference out of local optima. Experiments for the different diarization scenarios are presented on CALLHOME and DIHARD datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {355–368},
numpages = {14}
}

@article{10.1109/TASLP.2020.2979603,
author = {Nugraha, Aditya Arie and Sekiguchi, Kouhei and Yoshii, Kazuyoshi},
title = {A Flow-Based Deep Latent Variable Model for Speech Spectrogram Modeling and Enhancement},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2979603},
doi = {10.1109/TASLP.2020.2979603},
abstract = {This article describes a deep latent variable model of speech power spectrograms and its application to semi-supervised speech enhancement with a deep speech prior. By integrating two major deep generative models, a variational autoencoder (VAE) and a normalizing flow (NF), in a mutually-beneficial manner, we formulate a flexible latent variable model called the NF-VAE that can extract low-dimensional latent representations from high-dimensional observations, akin to the VAE, and does not need to explicitly represent the distribution of the observations, akin to the NF. In this article, we consider a variant of NF called the generative flow (GF a.k.a. Glow) and formulate a latent variable model called the GF-VAE. We experimentally show that the proposed GF-VAE is better than the standard VAE at capturing fine-structured harmonics of speech spectrograms, especially in the high-frequency range. A similar finding is also obtained when the GF-VAE and the VAE are used to generate speech spectrograms from latent variables randomly sampled from the standard Gaussian distribution. Lastly, when these models are used as speech priors for statistical multichannel speech enhancement, the GF-VAE outperforms the VAE and the GF.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1104–1117},
numpages = {14}
}

@article{10.1109/TASLP.2019.2960716,
author = {Ma, Fei and Zhang, Wen and Abhayapala, Thushara Dheemantha},
title = {Active Control of Outgoing Broadband Noise Fields in Rooms},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2960716},
doi = {10.1109/TASLP.2019.2960716},
abstract = {Active noise control system has been actively researched over the past half century, and implemented to reduce noises in ducts, headsets, and inside several automobile models. However, active control of noise fields, and specifically broadband noise fields, in rooms is still a barely explored topic due to the difficult of obtaining the reference signals, the challenge of online secondary path estimation, and the causal control constraint. In this paper, an active noise control system is developed that can cancel outgoing broadband noise fields in rooms. The proposed system decomposes the noise field on a sphere surrounding the noise sources into spherical harmonic modes, exploiting their directionality to generate the reference signals and remove the need for online secondary path estimation. A time-domain sound field separation algorithm and a time-wave domain adaptive algorithm allow the proposed system to meet the causal control constraint. Simulation results demonstrate that the proposed system can cancel broadband noise field globally in a room without suffering from the secondary source feedback problem.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {529–539},
numpages = {11}
}

@article{10.1109/TASLP.2020.2983593,
author = {Zheng, Yinhe and Chen, Guanyi and Huang, Minlie},
title = {Out-of-Domain Detection for Natural Language Understanding in Dialog Systems},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2983593},
doi = {10.1109/TASLP.2020.2983593},
abstract = {Natural Language Understanding (NLU) is a vital component of dialogue systems, and its ability to detect Out-of-Domain (OOD) inputs is critical in practical applications, since the acceptance of the OOD input that is unsupported by the current system may lead to catastrophic failure. However, most existing OOD detection methods rely heavily on manually labeled OOD samples and cannot take full advantage of unlabeled data. This limits the feasibility of these models in practical applications. In this paper, we propose a novel model to generate high-quality pseudo OOD samples that are akin to IN-Domain (IND) input utterances and thereby improves the performance of OOD detection. To this end, an autoencoder is trained to map an input utterance into a latent code. Moreover, the codes of IND and OOD samples are trained to be indistinguishable by utilizing a generative adversarial network. To provide more supervision signals, an auxiliary classifier is introduced to regularize the generated OOD samples to have indistinguishable intent labels. Experiments show that these pseudo OOD samples generated by our model can be used to effectively improve OOD detection in NLU. Besides, we also demonstrate that the effectiveness of these pseudo OOD data can be further improved by efficiently utilizing unlabeled data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1198–1209},
numpages = {12}
}

@article{10.1109/TASLP.2019.2950767,
author = {Canclini, Antonio and Antonacci, Fabio and Tubaro, Stefano and Sarti, Augusto},
title = {A Methodology for the Robust Estimation of the Radiation Pattern of Acoustic Sources},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2950767},
doi = {10.1109/TASLP.2019.2950767},
abstract = {We propose a novel methodology for estimating the radiation pattern of acoustic sources, which is general enough as to be suitable for a wide variety of sources without the need of anechoic conditions of operation. Multiple plenacoustic cameras (which can be thought of as arrays of acoustic cameras) scan the source while keeping reflections and interferers at bay through deconvolution and windowing of the measured response. In the case of a moving source (e.g. a musical instrument while it is being played), the plenacoustic cameras are also used for tracking the position of the source. As for its orientation, we propose practical solutions for tracking that as well, whenever such information is not known in advance. Two experiments are conducted in order to validate the proposed solution. The former focuses on a commercial loudspeaker cabinet, whose radiation pattern is known in advance and can be used as groundtruth. The latter concerns violins, which exhibit an extremely rich and hard to predict acoustic behavior, due to their inherent structural and constructional complexity. Our method allows us to capture the radiation pattern of the instrument while it is being played, thus returning data corresponding to the natural timbre of the instrument, including the unavoidable acoustic shadow of the violinist's head. Experimental results confirm a relevant improvement in accuracy and robustness afforded by the adoption of dynamic plenacoustic solutions with respect to state-of-the-art techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {211–224},
numpages = {14}
}

@article{10.1109/TASLP.2020.2977472,
author = {Khadem-hosseini, M. and Ghaemmaghami, S. and Abtahi, A. and Gazor, S. and Marvasti, F.},
title = {Error Correction in Pitch Detection Using a Deep Learning Based Classification},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2977472},
doi = {10.1109/TASLP.2020.2977472},
abstract = {While pitch detection has been the focal subject of numerous research efforts for several decades, it is still a challenging task in noisy conditions. In this article, we propose a method to improve the pitch detection accuracy of conventional pitch detection methods. The proposed pitch detection process starts with using the pitch value estimated by a conventional pitch detection method. Then, it extracts pitch candidates according to the most probable types of errors in the initial estimation of high-pitch and low-pitch frames classified by a Deep Convolutional Neural Network (DCNN). Next, a restrained selection procedure is run to find the true pitch value from the set of pitch candidates. In this procedure, we employ two features (harmonic summation and Euclidian deviation), the soft decision of the DCNN, the pitch smoothness feature in successive frames, and the effect of the initial estimation in a cost function. The pitch value which leads to the lowest cost value is chosen as the estimated pitch value. The simulations on CSTR and KEELE databases, in noisy environments with twelve types of noise, were performed. The results show the superiority of the proposed method over the state-of-the-art methods under different SNR conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {990–999},
numpages = {10}
}

@article{10.1109/TASLP.2019.2957887,
author = {Mathad, Vikram C. and Prasanna, S. R. Mahadeva},
title = {Vowel Onset Point Based Screening of Misarticulated Stops in Cleft Lip and Palate Speech},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2957887},
doi = {10.1109/TASLP.2019.2957887},
abstract = {The presence of velopharyngeal dysfunction, dental occlusion, and mislearned articulation in individuals with cleft lip and palate (CLP) results in the production of misarticulated stop consonants. The present work considers vowel onset points (VOPs) as the anchor points, around which the consonant-vowel (CV) transition regions are segmented to analyze the difference between normal and misarticulated stops. VOPs are located using an epoch-synchronously computed feature called maximum weighted inner product. Spectro-temporal dynamics of CV transitions anchored around VOP are analyzed using two-dimensional discrete cosine transform (2D-DCT) coefficients, where 2D-DCT coefficients are derived from single pole filter (SPF) based time-frequency representation. The SPF-based 2D-DCT coefficients are used to train a support vector machine for the classification of normal and misarticulated stops, where the class of misarticulated stops includes weak, nasalized, palatal, velar, pharyngeal, glottal, and devoicing errors produced by CLP speakers. The performance of the proposed VOP detection algorithm is evaluated on a database containing CV units of normal and misarticulated stops, and the results are compared with the state-of-the-art VOP detection methods. The classification results obtained for the proposed SPF-based 2D-DCT coefficients are compared with the short-time Fourier transform-based 2D-DCT coefficients and Mel-frequency cepstral coefficients. Further, the performance of the proposed system is compared with the hidden Markov model-based goodness of pronunciation approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {450–460},
numpages = {11}
}

@article{10.1109/TASLP.2019.2935843,
author = {Yu, Jun and Ling, Qiang and Luo, Changwei and Chen, Chang Wen},
title = {Synthesizing 3D Trump: Predicting and Visualizing the Relationship Between Text, Speech, and Articulatory Movements},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2935843},
doi = {10.1109/TASLP.2019.2935843},
abstract = {The movements of articulators, such as lips, tongue and teeth, play an important role in increasing the language expression capability by unmasking the information hid in text or speech. Hence, it is necessary to deeply mine and visualize the relationship between text, speech and articulatory movements for understanding language in multi-modality and multi-level. As a case study, given text and audio of President Donald John Trump, this paper synthesizes a high quality 3D animation of him speaking with accurate synchronicity between speech and articulators. First, visual co-articulation is modeled by predicting the mapping from text/speech to articulatory movements. Then, based on a reconstructed 3D head model, physiological characteristics and statistical learning are combined to visualize each phoneme. Finally, the visualization results of consecutive phonemes are fused by visual co-articulation model to generate synchronized articulatory animations. Experiments show that the system can not only produce photo-realistic results in front but also distinguish the visual differences among phonemes from unconstrained views.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2223–2233},
numpages = {11}
}

@article{10.1109/TASLP.2019.2942140,
author = {Prathosh, A. P. and Srivastava, Varun and Mishra, Mayank},
title = {Adversarial Approximate Inference for Speech to Electroglottograph Conversion},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2942140},
doi = {10.1109/TASLP.2019.2942140},
abstract = {Speech produced by human vocal apparatus conveys substantial non-semantic information including the gender of the speaker, voice quality, affective state, abnormalities in the vocal apparatus etc. Such information is attributed to the properties of the voice source signal, which is usually estimated from the speech signal. However, most of the source estimation techniques depend heavily on the goodness of the model assumptions and are prone to noise. A popular alternative is to indirectly obtain the source information through the Electroglottographic (EGG) signal that measures the electrical admittance around the vocal folds using dedicated hardware. In this paper, we address the problem of estimating the EGG signal directly from the speech signal, devoid of any hardware. Sampling from the intractable conditional distribution of the EGG signal given the speech signal is accomplished through optimization of an evidence lower bound. This is constructed via minimization of the KL-divergence between the true and the approximated posteriors of a latent variable learned using a deep neural auto-encoder that serves an informative prior. We demonstrate the efficacy of the method at generating the EGG signal by conducting several experiments on datasets comprising multiple speakers, voice qualities, noise settings and speech pathologies. The proposed method is evaluated on many benchmark metrics and is found to agree with the gold standard while proving better than the state-of-the-art algorithms on a few tasks such as epoch extraction.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2183–2196},
numpages = {14}
}

@article{10.1109/TASLP.2019.2945489,
author = {Kato, Akihiro and Kinnunen, Tomi H.},
title = {Statistical Regression Models for Noise Robust F0 Estimation Using Recurrent Deep Neural Networks},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2945489},
doi = {10.1109/TASLP.2019.2945489},
abstract = {The fundamental frequency (F0) in a speech signal, which corresponds to pitch, is one of the key features involved in a variety of speech processing tasks. Therefore, accurate F0 estimation has remained an important problem to be solved over decades. However, this problem is difficult, especially in low signal-to-noise ratio (SNR) conditions with unknown noise. In this work, we propose new approaches to noise-robust F0 estimation using recurrent neural networks (RNNs). Recent F0 estimation studies exploit deep neural networks (DNNs), including RNNs, to classify acoustic features into quantized frequency states. In contrast to these classification approaches, we put forward a <italic>regression</italic> method for F0 tracking, which is accomplished with RNNs. To this end, we propose two variants. Our first model predicts the (scalar) F0 value directly from a spectrum, while our second model predicts a target sinusoidal waveform (with the desired F0) from the raw speech waveform. Our experiments with the <italic>pitch tracking database from Graz University of Technology</italic> (PTDB-TUG), contaminated by additive noise (NOISEX-92), demonstrate the improvement of the proposed approaches in terms of the gross pitch error (GPE) and fine pitch error (FPE) rates by more than 35% at SNRs between −10 dB and +10 dB against a well-known, noise-robust F0 tracker, PEFAC. Furthermore, our methods outperform state-of-the-art neural network-based approaches by more than 15% in terms of both the FPE and GPE rates over the abovementioned SNR range.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2336–2349},
numpages = {14}
}

@article{10.1109/TASLP.2019.2945843,
author = {Sugiura, Ryosuke and Kamamoto, Yutaka and Moriya, Takehiro},
title = {Shape Control of Discrete Generalized Gaussian Distributions for Frequency-Domain Audio Coding},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2945843},
doi = {10.1109/TASLP.2019.2945843},
abstract = {Entropy coding, which is an essential part of audio compression, is always required to manage the tradeoffs between compression efficiency and computational complexity, and the strategy to achieve them highly depends on the distributions of inputs. In this paper, we present a method of controlling them for enhancing the compression efficiency of Golomb-Rice (GR) encoding, one of the simplest entropy coding methods optimal for Laplacian distributions. We will show that the proposed invertible and low-complexity mapping of integers enables the GR encoding to assign nearly the optimal code length for a wider range of distributions, generalized Gaussian distributions, maintaining low computational cost. A simulation by random numbers reveals that the proposed coder based on this scheme works about 6 times faster than the state-of-the-art arithmetic coder for Gaussian-distributed integers maintaining the increase in relative redundancy around <inline-formula><tex-math notation="LaTeX">$text{2.6}{%}$</tex-math></inline-formula>, which is much lower than that of a conventional GR coder. Additionally, an application to a practical speech and audio coding scheme is presented, and an objective evaluation for real speech and audio signals confirms the advantages of the proposed method in compression. The method is expected to widen the capability of low-complexity entropy coding, providing us with more flexible codec designs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2234–2248},
numpages = {15}
}

@article{10.1109/TASLP.2019.2946480,
author = {Zhang, Biao and Xiong, Deyi and Su, Jinsong and Luo, Jiebo},
title = {Future-Aware Knowledge Distillation for Neural Machine Translation},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2946480},
doi = {10.1109/TASLP.2019.2946480},
abstract = {Although future context is widely regarded useful for word prediction in machine translation, it is quite difficult in practice to incorporate it into neural machine translation. In this paper, we propose a future-aware knowledge distillation framework (FKD) to address this issue. In the FKD framework, we learn to distill future knowledge from a backward neural language model (teacher) to future-aware vectors (student) during the training phase. The future-aware vector for each word position is computed in a bridge network and optimized towards the corresponding hidden state in the backward neural language model via a knowledge distillation mechanism. We further propose an algorithm to jointly train the neural machine translation model, neural language model and knowledge distillation module end-to-end. The learned future-aware vectors are incorporated into the attention layer of the decoder to provide full-range context information during the decoding phase. Experiments on the NIST Chinese-English and WMT English-German translation tasks show that the proposed method significantly improves translation quality and word alignment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2278–2287},
numpages = {10}
}

@article{10.1109/TASLP.2019.2933047,
author = {Antonello, Niccolo and De Sena, Enzo and Moonen, Marc and Naylor, Patrick A. and van Waterschoot, Toon},
title = {Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2933047},
doi = {10.1109/TASLP.2019.2933047},
abstract = {Acoustic source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists of the approximation of the sound field measured by a set of microphones. The recorded sound pressure is matched with that of a particular acoustic model based on a collection of plane waves arriving from different directions at the microphone positions. In order to achieve meaningful results, spatial and spatio-spectral sparsity can be promoted in the weight signals controlling the plane waves. The large-scale optimization problem resulting from the inverse problem formulation is solved using a first order optimization algorithm combined with a weighted overlap-add procedure. It is shown that once the weight signals capable of effectively approximating the sound field are obtained, they can be readily used to localize a moving sound source in terms of direction of arrival DOA and to perform dereverberation in a highly reverberant environment. Results from simulation experiments and from real measurements show that the proposed algorithm is robust against both localized and diffuse noise exhibiting a noise reduction in the dereverberated signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1893–1905},
numpages = {13}
}

@article{10.1109/TASLP.2019.2942439,
author = {Arnela, Marc and Dabbaghchian, Saeed and Guasch, Oriol and Engwall, Olov},
title = {MRI-Based Vocal Tract Representations for the Three-Dimensional Finite Element Synthesis of Diphthongs},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2942439},
doi = {10.1109/TASLP.2019.2942439},
abstract = {The synthesis of diphthongs in three-dimensions 3D involves the simulation of acoustic waves propagating through a complex 3D vocal tract geometry that deforms over time. Accurate 3D vocal tract geometries can be extracted from Magnetic Resonance Imaging MRI, but due to long acquisition times, only static sounds can be currently studied with an adequate spatial resolution. In this work, 3D dynamic vocal tract representations are built to generate diphthongs, based on a set of cross-sections extracted from MRI-based vocal tract geometries of static vowel sounds. A diphthong can then be easily generated by interpolating the location, orientation and shape of these cross-sections, thus avoiding the interpolation of full 3D geometries. Two options are explored to extract the cross-sections. The first one is based on an adaptive grid AG, which extracts the cross-sections perpendicular to the vocal tract midline, whereas the second one resorts to a semi-polar grid SPG strategy, which fixes the cross-section orientations. The finite element method FEM has been used to solve the mixed wave equation and synthesize diphthongs [${alpha i}$] and [${alpha u}$] in the dynamic 3D vocal tracts. The outputs from a 1D acoustic model based on the Transfer Matrix Method have also been included for comparison. The results show that the SPG and AG provide very close solutions in 3D, whereas significant differences are observed when using them in 1D. The SPG dynamic vocal tract representation is recommended for 3D simulations because it helps to prevent the collision of adjacent cross-sections.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2173–2182},
numpages = {10}
}

@article{10.1109/TASLP.2019.2947232,
author = {Marafioti, Andr\'{e}s and Perraudin, Nathana\"{e}l and Holighaus, Nicki and Majdak, Piotr},
title = {A Context Encoder For Audio Inpainting},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2947232},
doi = {10.1109/TASLP.2019.2947232},
abstract = {In this article, we study the ability of deep neural networks (DNNs) to restore missing audio content based on its context, i.e., inpaint audio gaps. We focus on a condition which has not received much attention yet: gaps in the range of tens of milliseconds. We propose a DNN structure that is provided with the signal surrounding the gap in the form of time-frequency (TF) coefficients. Two DNNs with either complex-valued TF coefficient output or magnitude TF coefficient output were studied by separately training them on inpainting two types of audio signals (music and musical instruments) having 64-ms long gaps. The magnitude DNN outperformed the complex-valued DNN in terms of signal-to-noise ratios and objective difference grades. Although, for instruments, a reference inpainting obtained through linear predictive coding performed better in both metrics, it performed worse than the magnitude DNN for music. This demonstrates the potential of the magnitude DNN, in particular for inpainting signals that are more complex than single instrument sounds.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2362–2372},
numpages = {11}
}

@article{10.1109/TASLP.2019.2936755,
author = {Li, Wei and Chen, Nancy F. and Siniscalchi, Sabato Marco and Lee, Chin-Hui},
title = {Improving Mispronunciation Detection of Mandarin Tones for Non-Native Learners With Soft-Target Tone Labels and BLSTM-Based Deep Tone Models},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2936755},
doi = {10.1109/TASLP.2019.2936755},
abstract = {We investigate the effectiveness of soft-target tone labels and sequential context information for mispronunciation detection of Mandarin lexical tones pronounced by second language L2 learners whose first language L1 is of European origin. In conventional approaches, prosodic information e.g., F0 and tone posteriors extracted from trained tone models is used to calculate goodness of pronunciation GOP scores or train binary classifiers to verify pronunciation correctness. We propose three techniques to improve detection of mispronunciation of Mandarin tones for non-native learners. First, we extend our tone model from a deep neural network DNN to a bidirectional long short-term memory BLSTM network in order to more accurately model the high variability of non-native tone productions and the contextual information expressed in tone-level co-articulation. Second, we characterize ambiguous pronunciations where L2 learners’ tone realizations are between two canonical tone categories by relaxing hard target labels to soft targets with probabilistic transcriptions. Third, segmental tone features fed into verifiers are extracted by a BLSTM to exploit sequential context information to improve mispronunciation detection. Compared to DNN-GOP trained with hard targets, the proposed BLSTM-GOP framework trained with soft targets reduces the tones’ averaged equal error rate ERR from 7.58% to 5.83% and the averaged area under ROC curve AUC is increased from 97.85% to 98.31%. By utilizing BLSTM-based verifiers the EER further decreases to 5.16%, and the AUC is increased to 98.47%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2012–2024},
numpages = {13}
}

@article{10.1109/TASLP.2019.2934834,
author = {Ueno, Natsuki and Koyama, Shoichi and Saruwatari, Hiroshi},
title = {Three-Dimensional Sound Field Reproduction Based on Weighted Mode-Matching Method},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2934834},
doi = {10.1109/TASLP.2019.2934834},
abstract = {A sound field reproduction method based on the spherical wavefunction expansion of sound fields is proposed, which can be flexibly applied to various array geometries and directivities. First, we formulate sound field synthesis as a minimization problem of some norm on the difference between the desired and synthesized sound fields, and then the optimal driving signals are derived by using the spherical wavefunction expansion of the sound fields. This formulation is closely related to the mode-matching method; a major advantage of the proposed method is the optimal weight on the mode determined according to the norm to be minimized instead of the empirical truncation in the mode-matching method. We also provide some examples of norms and their corresponding weights in analytical forms. Both interior and exterior sound field reproduction are considered in the proposed method, and some applications, such as multizone reproduction and interior reproduction with exterior cancellation, are also discussed. Numerical simulation results indicated that higher reproduction accuracy can be achieved by the proposed method than by the current pressure-matching and mode-matching methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1852–1867},
numpages = {16}
}

@article{10.1109/TASLP.2019.2935891,
author = {Qi, Jun and Du, Jun and Siniscalchi, Sabato Marco and Lee, Chin-Hui},
title = {A Theory on Deep Neural Network Based Vector-to-Vector Regression With an Illustration of Its Expressive Power in Speech Enhancement},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2935891},
doi = {10.1109/TASLP.2019.2935891},
abstract = {This paper focuses on a theoretical analysis of deep neural network DNN based functional approximation. Leveraging upon two classical theorems on universal approximation, an artificial neural network ANN with a single hidden layer of neurons is used. With modified ReLU and Sigmoid activation functions, we first generalize the related concepts to vector-to-vector regression. Then, we show that the width of the hidden layer of ANN is numerically related to the approximation of the regression function. Furthermore, we increase the number of hidden layers and show that the depth of the ANN-based regression function can enhance its expressive power. We illustrate this representation with recently-emerged DNN based speech enhancement. We first compare the expressive power by varying ANN structures and then test its related regression performance under different noisy conditions in various noise types and signal-to-noise-ratio levels. Experimental results verify our theoretical prediction that an ANN of a broader hidden layer and a deeper architecture can jointly ensure a closer approximation of the vector-to-vector regression functions in terms of the Euclidean distance between the log power spectra of noisy and expected clean speech. Moreover, a DNN with a broader width at the top hidden layer can improve the regression performance relative to those with a narrower width at the top hidden layers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1932–1943},
numpages = {12}
}

@article{10.1109/TASLP.2019.2935807,
author = {Zheng, Yibin and Tao, Jianhua and Wen, Zhengqi and Yi, Jiangyan},
title = {Forward–Backward Decoding Sequence for Regularizing End-to-End TTS},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2935807},
doi = {10.1109/TASLP.2019.2935807},
abstract = {Neural end-to-end TTS such as Tacotron like network can generate very high-quality synthesized speech, and even close to human recording for similar domain text. However, it performs unsatisfactory when scaling it to some challenging test sets. One concern is that the encoder-decoder with attention-based network adopts autoregressive generative sequence model with the limitation of “exposure bias”: errors made early could be quickly amplified, harming subsequent sequence generation. To address this issue, we propose two novel methods, which aim at predicting future by improving the agreement between forward and backward decoding sequence. The first one denoted as MRBA is achieved by adding divergence regularization terms to model training objective to maximize the agreement between two directional models, namely L2R which generates targets from left-to-right and R2L which generates targets from right-to-left. While the second one denoted as BDR operates on decoder-level and exploits the future information during decoding. By introducing regularization term into the training objective of forward-backward decoders, the forward-decoder's hidden states are forced to be close to the backward-decoder's. Thus, the hidden representations of a unidirectional decoder are encouraged to embed some useful information about the future. Moreover, in order to make forward and backward decoding to improve each other in an interactive process, a joint training method is designed. Experimental results on both English and Mandarin dataset show that our proposed methods especially the second one BDR, lead to a significantly improvement on both robustness and overall naturalness, as achieving obvious preference advantages in a challenging test, and achieving state-of-the-art performance outperforming baseline “the revised version of Tacotron2” with a gap of 0.13 and 0.12 for English and Mandarin in MOS, respectively on a general test.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2067–2079},
numpages = {13}
}

@article{10.1109/TASLP.2019.2946086,
author = {Ali, Randall and Van Waterschoot, Toon and Moonen, Marc},
title = {Integration of <italic>a Priori</italic> and Estimated Constraints Into an MVDR Beamformer for Speech Enhancement},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2946086},
doi = {10.1109/TASLP.2019.2946086},
abstract = {Conventionally, the single constraint of the minimum variance distortionless response (MVDR) beamformer for speech enhancement has been defined using one of two approaches. Either it is based on a priori assumptions such as microphone characteristics, position, speech source location, and room acoustics, or on a relative transfer function (RTF) vector estimate using a data dependent method. Each approach has its respective merits and drawbacks and a decision usually has to be made between one of the approaches. In this paper, an alternative approach of using an integrated MVDR beamformer is investigated, where both the hard constraints from the two conventional approaches are softened to yield two tuning parameters. It will be shown that this integrated MVDR beamformer can be expressed as a convex combination of the conventional MVDR beamformers, a linearly constrained minimum variance (LCMV) beamformer, and an all-zero vector, with real, positive-valued coefficients. By analysing how the tuning parameters affect these coefficients, two tuning rules for a practical implementation of the integrated MVDR are subsequently proposed. An evaluation with simulated and recorded data demonstrates that the integrated MVDR beamformer can be beneficial as opposed to relying on either of the conventional MVDR beamformers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2288–2300},
numpages = {13}
}

@article{10.1109/TASLP.2019.2942157,
author = {Hu, Jingyi and Chen, Ning},
title = {Enhanced Feature Summarizing for Effective Cover Song Identification},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2942157},
doi = {10.1109/TASLP.2019.2942157},
abstract = {Self-similarity analysis-based feature summarizing technique SuCo was proposed recently to improve the time and memory efficiency of Cover Song Identification CSI. In this paper, both the feature summarizing and the cross-similarity calculating strategies of the SuCo model are modified as follows to enhance its identification accuracy. At the feature summarizing stage, first, the Hubness Reduction HR strategy is adopted to reduce the possible ‘Hubness’ phenomenon existing in the feature subsequence community, which may affect the retrieval effectiveness. Then, the Network Enhancement NE technique, which was originally proposed in biology to improve gene-function prediction accuracy, is introduced to reduce the noise in the self-similarity network caused by the limitation of feature extraction and similarity measuring, and the inherent musical and acoustic variations. At the cross-similarity calculating stage, first, the summarized representative feature subsequences of the reference are concatenated to obtain its combined representative feature. Then, considering that the nonlinear recurrence property is important for describing the melody perception-based similarity, Qmax is adopted to measure the similarity between the combined representative feature of the reference and the unsummarized feature sequence of the query. Extensive experiments carried out on four open CSI datasets with 5 types of features and 2 kinds of representative feature subsequence choosing methods verify that: i The proposed scheme outperforms the SuCo model in retrieval effectiveness. ii Each of the above modifications contributes to the performance enhancement of the proposed scheme. iii The proposed scheme achieves high generalization.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2113–2126},
numpages = {14}
}

@article{10.1109/TASLP.2019.2935809,
author = {Valimaki, Vesa and Ramo, Jussi},
title = {Neurally Controlled Graphic Equalizer},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2935809},
doi = {10.1109/TASLP.2019.2935809},
abstract = {This paper describes a neural network based method to simplify the design of a graphic equalizer without sacrificing the accuracy of approximation. The key idea is to train a neural network to predict the mapping from target gains to the optimized band filter gains at specified center frequencies. The prediction is implemented with a feedforward neural network having a hidden layer with 20 neurons in the case of the ten-octave graphic equalizer. The band filter coefficients can then be quickly and easily computed using closed-form formulas. This work turns, for the first time, the accurate graphic equalization design into a feedforward calculation without matrix inversion or iterations. The filter gain control using the neural network reduces the computing time by 99.6% in comparison to the least-squares design method it is imitating and contributes an approximation error of less than 0.1 dB. The resulting neurally controlled graphic equalizer will be highly useful in various audio and music processing applications, which require time-varying equalization.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2140–2149},
numpages = {10}
}

@article{10.1109/TASLP.2019.2937190,
author = {Chen, Kehai and Wang, Rui and Utiyama, Masao and Sumita, Eiichiro and Zhao, Tiejun},
title = {Neural Machine Translation With Sentence-Level Topic Context},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2937190},
doi = {10.1109/TASLP.2019.2937190},
abstract = {Traditional neural machine translation NMT methods use the word-level context to predict target language translation while neglecting the sentence-level context, which has been shown to be beneficial for translation prediction in statistical machine translation. This paper represents the sentence-level context as latent topic representations by using a convolution neural network, and designs a topic attention to integrate source sentence-level topic context information into both attention-based and Transformer-based NMT. In particular, our method can improve the performance of NMT by modeling source topics and translations jointly. Experiments on the large-scale LDC Chinese-to-English translation tasks and WMT’14 English-to-German translation tasks show that the proposed approach can achieve significant improvements compared with baseline systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1970–1984},
numpages = {15}
}

@article{10.1109/TASLP.2019.2935837,
author = {Dang, Xudong and Cheng, Qi and Zhu, Hongyan},
title = {Indoor Multiple Sound Source Localization via Multi-Dimensional Assignment Data Association},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2935837},
doi = {10.1109/TASLP.2019.2935837},
abstract = {In this paper, we address the multiple sound source localization problem by associating and fusing the direction of arrival DOA estimates from multiple microphone arrays. For multi-source scenarios especially in indoor environments, a critical issue is to tell the correspondence among DOA estimates across different arrays, which is known as the data association problem. We propose a multi-dimensional assignment-based data association approach to find the optimal associations of DOA estimates from the same source. First, in the sense of maximum likelihood, the data association problem is formulated by finding the most likely partition of the measurement set into the source-originated and false alarm-originated subsets. Next, by defining the association costs appropriately, the problem of finding the most likely measurement partition is transformed into a generalized multi-dimensional assignment problem which can be solved efficiently by a Lagrangian relaxation algorithm. After the optimal associations of DOA estimates across different arrays are obtained, the locations of sources can be estimated by fusing the same source-originated DOA estimates. In the presence of missed detections, false alarms and the unknown number of sources, the proposed method achieves high accuracy in data association and localization, and outperforms the competing method in reverberant and noisy environments. In addition, since our method does not require additional features and uses DOA estimates only, it is more computationally efficient than the competing method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1944–1956},
numpages = {13}
}

@article{10.1109/TASLP.2019.2934567,
author = {Borra, Federico and Bernardini, Alberto and Antonacci, Fabio and Sarti, Augusto},
title = {Uniform Linear Arrays of First-Order Steerable Differential Microphones},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2934567},
doi = {10.1109/TASLP.2019.2934567},
abstract = {We propose a spatial filtering method for linear arrays of First-Order Steerable Differential Microphones FOSDMs, which operates in two layers. In the former, signals acquired by individual microphones are locally filtered to produce the outputs of the FOSDMs. In the latter, the outputs of the FOSDMs are processed by another filter. We analyse different design methodologies and study the conditions under which the two filtering layers can be decoupled. The proposed two-layer spatial filter can be flexibly controlled with a single scalar parameter, which can be chosen, for example, to maximize the White Noise Gain like in a Delay-and-Sum beamformer; or to maximize the Directivity Factor like in a Super-Directive beamformer; without needing any matrix inversion. The effectiveness of the proposed beamforming method is compared with traditional spatial filtering techniques using different metrics.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1906–1918},
numpages = {13}
}

@article{10.1109/TASLP.2019.2941592,
author = {Pfeifenberger, Lukas and Zohrer, Matthias and Pernkopf, Franz},
title = {Eigenvector-Based Speech Mask Estimation for Multi-Channel Speech Enhancement},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2941592},
doi = {10.1109/TASLP.2019.2941592},
abstract = {We present the Eigennet architecture for estimating a gain mask from noisy, multi-channel microphone observations. While existing mask estimators use magnitude features, our system also exploits the spatial information embedded in the phase of the data. The mask is used to obtain the Minimum Variance Distortionless Response MVDR and Generalized Eigenvalue GEV beamformers. We also derive the Phase Aware Normalization PAN postfilter, which corrects both magnitude and phase distortions caused by the GEV. Further, we demonstrate the properties of our eigenvector features, and compare their performance with three state-of-the-art reference systems. We report their performance in terms of SNR improvement and Word Error Rate WER using Google and Kaldi Speech-to-Text API. Experiments are performed on the WSJ0 and CHiME4 corpora, where competitive performance in both WER and SNR is achieved.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2162–2172},
numpages = {11}
}

@article{10.1109/TASLP.2019.2935803,
author = {Chai, Li and Du, Jun and Liu, Qing-Feng and Lee, Chin-Hui},
title = {Using Generalized Gaussian Distributions to Improve Regression Error Modeling for Deep Learning-Based Speech Enhancement},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2935803},
doi = {10.1109/TASLP.2019.2935803},
abstract = {From a statistical perspective, the conventional minimum mean squared error MMSE criterion can be considered as the maximum likelihood ML solution under an assumed homoscedastic Gaussian error model. However, in this paper, a statistical analysis reveals the super-Gaussian and heteroscedastic properties of the prediction errors in nonlinear regression deep neural network DNN-based speech enhancement when estimating clean log-power spectral LPS components at DNN outputs with noisy LPS features in DNN input vectors. Accordingly, we propose treating all dimensions of the prediction error vector as statistically independent random variables and model them with generalized Gaussian distributions GGDs. Then, the objective function with the GGD error model is derived according to the ML criterion. Experiments on the TIMIT corpus corrupted by simulated additive noises show consistent improvements of our proposed DNN framework over the conventional DNN framework in terms of various objective quality measures under 14 unseen noise types evaluated and at various signal-to-noise ratio levels. Furthermore, the ML optimization objective with GGD outperforms the conventional MMSE criterion, achieving improved generalization and robustness.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1919–1931},
numpages = {13}
}

@article{10.1109/TASLP.2019.2937174,
author = {Wood, Sean U. N. and Stahl, Johannes K. W. and Mowlaee, Pejman},
title = {Binaural Codebook-Based Speech Enhancement With Atomic Speech Presence Probability},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2937174},
doi = {10.1109/TASLP.2019.2937174},
abstract = {In this work, we present a universal codebook-based speech enhancement framework that relies on a single codebook to encode both speech and noise components. The atomic speech presence probability ASPP is defined as the probability that a given codebook atom encodes speech at a given point in time. We develop ASPP estimators based on binaural cues including the interaural phase and level difference IPD and ILD, the interaural coherence magnitude ICM, as well as a combined version leveraging the full interaural transfer function ITF. We evaluate the performance of the resulting ASPP-based speech enhancement algorithms on binaural mixtures of reverberant speech and real-world noise. The proposed approach improves both objective speech quality and intelligibility over a wide range of input SNR, as measured with PESQ and binaural STOI metrics, outperforming two binaural speech enhancement benchmark methods. We show that the proposed ITF-based ASPP approach achieves a good balance of the trade-off between binaural noise reduction and binaural cue preservation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2150–2161},
numpages = {12}
}

@article{10.1109/TASLP.2019.2937413,
author = {Gomez-Alanis, Alejandro and Peinado, Antonio M. and Gonzalez, Jose A. and Gomez, Angel M.},
title = {A Gated Recurrent Convolutional Neural Network for Robust Spoofing Detection},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2937413},
doi = {10.1109/TASLP.2019.2937413},
abstract = {Automatic speaker verification ASV systems are exposed to spoofing attacks which may compromise their security. While anti-spoofing techniques have been mainly studied for clean scenarios, it has also been shown that they perform poorly in noisy environments. In this work, we aim at improving the performance of spoofing detection for ASV in clean and noisy scenarios. To achieve this, we first propose the use of Gated Recurrent Convolutional Neural Networks GRCNNs as a deep feature extractor to robustly represent speech signals as utterance-level embeddings, which are later used by a back-end recognizer for the final genuine/spoofed classification. Then, to enhance the robustness of the system in noisy conditions, we propose the use of signal-to-noise masks SNMs as new input features to inform the anti-spoofing system about the time-frequency regions of the input spectral features that are mostly affected by noise and, hence, should be neglected when computing the embeddings. To evaluate our proposals, experiments were carried out on the clean and noisy versions of the ASVspoof 2015 corpus for detecting logical access attacks, as well as on the ASVspoof 2017 database to detect replay attacks. Additional results are provided for the ASVspoof 2019 corpus, including both logical and physical scenarios. The experimental results show that our proposal clearly outperforms some well-known methods based on classical features and other similar deep feature based systems for both clean and noisy conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1985–1999},
numpages = {15}
}

@article{10.1109/TASLP.2019.2937192,
author = {Tu, Quansheng and Chen, Huawei},
title = {On Mainlobe Orientation of the First- and Second-Order Differential Microphone Arrays},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2937192},
doi = {10.1109/TASLP.2019.2937192},
abstract = {Due to the increased sensitivity to microphone mismatches, lower-order differential microphone arrays DMAs are usually employed in practice, especially the first and second-order DMAs. It is known that the mainlobe orientation of the typical first- and second-order DMAs is both along the fixed endfire direction. This may be no longer true, however, in the presence of microphone mismatchs. This paper studies the fundamental problem of how microphone mismatches affect mainlobe orientation of the first- and second-order DMAs. Some insights into the effects of microphone mismatches on the mainlobe orientation of the two types of DMAs are revealed. In addition, the property on mainlobe orientation of the second-order DMA under ideal condition, which is yet to be known, is also studied. Moreover, tolerance analysis of microphone mismatches to ensure correct mainlobe orientation of the first- and second-order DMAs are performed in order to offer a useful guidance for practical design. Numerical examples are shown to verify the theoretical findings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2025–2040},
numpages = {16}
}

@article{10.1109/TASLP.2019.2936385,
author = {Schneider, Martin and Habets, Emanuel A. P.},
title = {Iterative DFT-Domain Inverse Filter Optimization Using a Weighted Least-Squares Criterion},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2936385},
doi = {10.1109/TASLP.2019.2936385},
abstract = {For many inverse filtering problems, finite impulse response filters are designed according to least-squares criteria, where time-domain and frequency-domain weights are often applied to achieve optimal results for the considered application. While least-squares-optimal filter coefficients are given by an explicit formula, the computation cost to compute its solution is proportional up to the third power of the number of jointly optimized filter coefficients. A joint optimization of all filter coefficients is necessary whenever a time-domain or a frequency-domain weight is introduced. This imposes limits for filter lengths and numbers of channels in many real-world scenarios. In this contribution, an algorithm is presented that yields time-domain filter coefficients optimized to meet such a weighted least-squares criterion, while performing the most expensive computation steps efficiently in the discrete Fourier transform domain. As a consequence, the demands on computational power and memory are kept on a moderate level, even for large-scale problems. A rigorous mathematical derivation is provided that identifies all approximations used in the algorithm. Additionally, an effective regularization method is proposed that does not depend much on the regularization parameters. Furthermore, the proposed approach is experimentally evaluated considering a sound-zones scenario, which is one of many possible application areas. In that way, the applicability of the proposed approach is verified.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1957–1969},
numpages = {13}
}

@article{10.1109/TASLP.2019.2938863,
author = {Chorowski, Jan and Weiss, Ron J. and Bengio, Samy and van den Oord, Aaron},
title = {Unsupervised Speech Representation Learning Using WaveNet Autoencoders},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2938863},
doi = {10.1109/TASLP.2019.2938863},
abstract = {We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. Since the learned representation is tuned to contain only phonetic content, we resort to using a high capacity WaveNet decoder to infer information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder VAE, and a discrete Vector Quantized VAE VQ-VAE. We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2041–2053},
numpages = {13}
}

@article{10.1109/TASLP.2019.2941148,
author = {Liu, Yuzhou and Wang, DeLiang},
title = {Divide and Conquer: A Deep CASA Approach to Talker-Independent Monaural Speaker Separation},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2941148},
doi = {10.1109/TASLP.2019.2941148},
abstract = {We address talker-independent monaural speaker separation from the perspectives of deep learning and computational auditory scene analysis CASA. Specifically, we decompose the multi-speaker separation task into the stages of simultaneous grouping and sequential grouping. Simultaneous grouping is first performed in each time frame by separating the spectra of different speakers with a permutation-invariantly trained neural network. In the second stage, the frame-level separated spectra are sequentially grouped to different speakers by a clustering network. The proposed deep CASA approach optimizes frame-level separation and speaker tracking in turn, and produces excellent results for both objectives. Experimental results on the benchmark WSJ0-2mix database show that the new approach achieves the state-of-the-art results with a modest model size.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2092–2102},
numpages = {11}
}

@article{10.1109/TASLP.2019.2944348,
author = {Sekiguchi, Kouhei and Bando, Yoshiaki and Nugraha, Aditya Arie and Yoshii, Kazuyoshi and Kawahara, Tatsuya},
title = {Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2944348},
doi = {10.1109/TASLP.2019.2944348},
abstract = {This paper describes a semi-supervised multichannel speech enhancement method that uses clean speech data for prior training. Although multichannel nonnegative matrix factorization (MNMF) and its constrained variant called independent low-rank matrix analysis (ILRMA) have successfully been used for unsupervised speech enhancement, the low-rank assumption on the power spectral densities (PSDs) of all sources (speech and noise) does not hold in reality. To solve this problem, we replace a low-rank speech model with a deep generative speech model, i.e., formulate a probabilistic model of noisy speech by integrating a deep speech model, a low-rank noise model, and a full-rank or rank-1 model of spatial characteristics of speech and noise. The deep speech model is trained from clean speech data in an unsupervised auto-encoding variational Bayesian manner. Given multichannel noisy speech spectra, the full-rank or rank-1 spatial covariance matrices and PSDs of speech and noise are estimated in an unsupervised maximum-likelihood manner. Experimental results showed that the full-rank version of the proposed method was significantly better than MNMF, ILRMA, and the rank-1 version. We confirmed that the initialization-sensitivity and local-optimum problems of MNMF with many spatial parameters can be solved by incorporating the precise speech model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2197–2212},
numpages = {16}
}

@article{10.1109/TASLP.2019.2933727,
author = {Wu, Lijun and Tan, Xu and Qin, Tao and Lai, Jianhuang and Liu, Tie-Yan},
title = {Beyond Error Propagation: Language Branching Also Affects the Accuracy of Sequence Generation},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2933727},
doi = {10.1109/TASLP.2019.2933727},
abstract = {Sequence generation tasks, such as neural machine translation NMT and abstractive summarization, usually suffer from exposure bias as well as the error propagation problem due to the autoregressive training and generation. Many previous works have discussed the relationship between error propagation and the accuracy drop problem i.e., the right part of the generated sentence is often worse than its left part in left-to-right decoding models. In this paper, taking NMT as a typical sequence generation task, we measure the accuracy of the generated sentence with various metrics and conduct a series of analyses to deeply understand the accuracy drop problem. We obtain several interesting findings. First, The role of error propagation on accuracy drop is overstated in the literature, although it is indeed a cause to the accuracy drop problem. Second, Characteristics of a language play a more important role in causing the accuracy drop problem: the left part of the generated sentence in a right-branching language e.g., English is more likely to be more accurate than its right part, while the right part is more accurate for a left-branching language e.g., Japanese. Our discoveries are also confirmed on other generation tasks e.g., image captioning, abstractive summarization and language modeling with multiple left/right-branching languages, as well as in various model structures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1868–1879},
numpages = {12}
}

@article{10.1109/TASLP.2019.2946897,
author = {Yang, Jichen and Das, Rohan Kumar and Zhou, Nina},
title = {Extraction of Octave Spectra Information for Spoofing Attack Detection},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2946897},
doi = {10.1109/TASLP.2019.2946897},
abstract = {This article focuses on extracting information from the

license_type: permissive

branch_name: refs/heads/main

extension: bib