text:
-4 score of 38.23 on WIKITABLETEXT outperforming template-based and language model based approaches. Furthermore, through experiments on 1 M table-query pairs from a search engine, our Table2Seq model considering the structured part of a table, i.e., table attributes and table cells, as additional information outperforms a sequence-to-sequence model considering only the sequential part of a table, i.e., table caption.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {311–320},
numpages = {10}
}

@article{10.1109/TASLP.2018.2880336,
author = {Zakeri, Vahid and Hodgson, Antony J.},
title = {Automatic Identification of Hard and Soft Bone Tissues by Analyzing Drilling Sounds},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2880336},
doi = {10.1109/TASLP.2018.2880336},
abstract = {Our purpose is to assess if bone drilling sounds could be used to automatically distinguish between hard- and soft-bone tissues. Such a capability could be of value in a variety of surgical procedures involving drilling through bone. We acquired sound signals from six bovine tibial bones that were being drilled with a surgical drill. We investigated various classifiers including logistic regression, support vector machine SVM, random forest RF, and hidden Markov model. We explored different time and frequency features, and considered two training/testing scenarios: leave-one bone-out LOBO and bone-specific BSP. Moreover, we conducted a survey of practicing surgeons to provide a baseline measure for assessing whether our proposed algorithm could improve the ability of surgeons to identify hard- and soft-bone tissues based on drilling sounds. The average accuracies for LOBO and BSP were 79.1% and 84.3%, respectively. In both scenarios, the feature that resulted in the highest accuracy of classification was wavelet packet transform coefficients. Moreover, RF and SVM produced the highest accuracies for LOBO and BSP, respectively. In the survey, the surgeons were unsure on 29.2% of questions, and had an average accuracy of 51.4% on their judged answers. In conclusion, the drilling in hard and soft bones could be automatically identified with good accuracy based on the drilling sounds. Several of the algorithms tested were generalizable across specimens and their accuracy significantly exceeded surgeons’ performance. The significance is that these algorithms can potentially be used to reduce challenges associated with bone-drilling procedures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {404–414},
numpages = {11}
}

@article{10.1109/TASLP.2018.2877894,
author = {Xiong, Feifei and Goetze, Stefan and Kollmeier, Birger and Meyer, Bernd T.},
title = {Joint Estimation of Reverberation Time and Early-To-Late Reverberation Ratio From Single-Channel Speech Signals},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2877894},
doi = {10.1109/TASLP.2018.2877894},
abstract = {The reverberation time RT and the early-to-late reverberation ratio ELR are two key parameters commonly used to characterize acoustic room environments. In contrast to conventional blind estimation methods that process the two parameters separately, we propose a model for joint estimation to predict the RT and the ELR simultaneously from single-channel speech signals from either full-band or sub-band frequency data, which is referred to as joint room parameter estimator jROPE. An artificial neural network is employed to learn the mapping from acoustic observations to the RT and the ELR classes. Auditory-inspired acoustic features obtained by temporal modulation filtering of the speech time-frequency representations are used as input for the neural network. Based on an in-depth analysis of the dependency between the RT and the ELR, a two-dimensional RT, ELR distribution with constrained boundaries is derived, which is then exploited to evaluate four different configurations for jROPE. Experimental results show that—in comparison to the single-task ROPE system which individually estimates the RT or the ELR—jROPE provides improved results for both tasks in various reverberant and diffuse noisy environments. Among the four proposed joint types, the one incorporating multi-task learning with shared input and hidden layers yields the best estimation accuracies on average. When encountering extreme reverberant conditions with RTs and ELRs lying beyond the derived RT, ELR distribution, the type considering RT and ELR as a joint parameter performs robustly, in particular. From state-of-the-art algorithms that were tested in the acoustic characterization of environments challenge, jROPE achieves comparable results among the best for all individual tasks RT and ELR estimation from full-band and sub-band signals.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {255–267},
numpages = {13}
}

@article{10.1109/TASLP.2018.2880317,
author = {Krishnan, Lakshmi and Betlehem, Terence and Teal, Paul D.},
title = {Fast Algorithms for Acoustic Impulse Response Shaping},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2880317},
doi = {10.1109/TASLP.2018.2880317},
abstract = {Impulse response shaping IRS is a prefiltering technique for modifying the characteristics of a linear channel to achieve desirable characteristics. Acoustic impulse response shaping is used to apply partial equalization to reduce the effects of reverberation on audio signals propagating inside a room and is thus used for listening room compensation. To be capable of responding rapidly to time varying room acoustics, a shaping algorithm must be computationally fast; this is difficult to achieve since the filters to be designed may have thousands of coefficients. This paper derives variants of the Dual Augmented Lagrangian Method, presents their application to the design of the shaping filters, and validates their efficacy through experimental studies. In addition, the formulations have been modified to design shaping filters that are robust to change in microphone positions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {392–403},
numpages = {12}
}

@article{10.1109/TASLP.2018.2878949,
author = {Hashimoto, Tetsuya and Saito, Daisuke and Minematsu, Nobuaki},
title = {Many-to-Many and Completely Parallel-Data-Free Voice Conversion Based on Eigenspace DNN},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2878949},
doi = {10.1109/TASLP.2018.2878949},
abstract = {Media conversion of image, text, speech, etc., generally requires a large amount of parallel data for training a conversion model. Recently, methods for training the model using no or a small amount of parallel data draw researchers’ attention. In many-to-many voice conversion, since it is often hard to collect parallel data from every pair of speakers, the conversion models requiring no parallel data are desired. Conventional many-to-many voice conversion models required a large amount of prestored parallel data to acquire prior knowledge of the entire speaker space. Then, a specific model from an arbitrary speaker to another can be realized by adapting a few model parameters. Although these conversion models certainly do not use parallel data in an adaptation step, they still use parallel data for prior training. In this study, we aim at realizing completely parallel-data-free and many-to-many voice conversion. The proposed method uses both Eigenvoice Gaussian mixture models EVGMM and Deep neural network DNN. EVGMM is a many-to-many conversion model that constructs the entire speaker space called eigenspace by analyzing mean vectors of Gaussian mixture models and it is used in our method to decompose training speakers’ features into their eigenspace components. By using the speaker features and the obtained components as pseudo parallel data, multiple DNNs are trained to realize conversion between them. With these DNNs, features of any target speaker can be represented by a weighted sum of the components. It should be noted that all the processes of our proposal do not require any parallel data. A key technique is to estimate covariance terms of EVGMM with no parallel data. Experiments indicate that individuality scores of the proposed method using no parallel data are comparable enough to those of a baseline system trained with parallel data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {332–341},
numpages = {10}
}

@article{10.1109/TASLP.2018.2881336,
author = {Bilbao, Stefan and Hamilton, Brian},
title = {Directional Sources in Wave-Based Acoustic Simulation},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2881336},
doi = {10.1109/TASLP.2018.2881336},
abstract = {Volumetric wave-based acoustic simulation relies on the complete solution to the three-dimensional wave equation over a spatial grid. Detailed modeling of sources, however, requires interpolation over the grid, which is complicated by the directional character of the source itself. In this paper, a new model of point sources of arbitrary directivity and location with respect to an underlying grid is presented. The model is framed in the spatio-temporal domain directly through the differentiation of Dirac distributions, leading to a spatial Fourier-based approximation strategy. Various approximants are presented, of both separable and nonseparable type, which allow for optimization over a specified wavenumber range. Such approximants are then employed in a finite difference time domain setting, yielding numerical results for sources of various types, which are then compared against exact solutions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {415–428},
numpages = {14}
}

@article{10.1109/TASLP.2018.2877465,
author = {Nakashika, Toru and Takaki, Shinji and Yamagishi, Junichi},
title = {Complex-Valued Restricted Boltzmann Machine for Speaker-Dependent Speech Parameterization From Complex Spectra},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2877465},
doi = {10.1109/TASLP.2018.2877465},
abstract = {This paper describes a novel energy-based probabilistic distribution that represents complex-valued data and explains how to apply it to direct feature extraction from complex-valued spectra. The proposed model, the complex-valued restricted Boltzmann machine CRBM, is designed to deal with complex-valued visible units as an extension of the well-known restricted Boltzmann machine RBM. Like the RBM, the CRBM learns the relationships between visible and hidden units without having connections between units in the same layer, which dramatically improves training efficiency by using Gibbs sampling or contrastive divergence. Another important characteristic is that the CRBM also has connections between real and imaginary parts of each of the complex-valued visible units that help represent the data distribution in the complex domain. In speech signal processing, classification and generation features are often based on amplitude spectra e.g., MFCC, cepstra, and mel-cepstra even if they are calculated from complex spectra, and they ignore phase information. In contrast, the proposed feature extractor using the CRBM directly encodes the complex spectra or another complex-valued representation of the complex spectra into binary-valued latent features hidden units. Since the visible-hidden connections are undirected, we can also recover decode the complex spectra from the latent features directly. Our speech representation experiments demonstrated that the CRBM outperformed other speech representation methods, such as methods using a conventional RBM, a mel-log spectrum approximate decoder, etc.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {244–254},
numpages = {11}
}

@article{10.1109/TASLP.2018.2877892,
author = {Stoter, Fabian-Robert and Chakrabarty, Soumitro and Edler, Bernd and Habets, Emanuel A. P.},
title = {CountNet: Estimating the Number of Concurrent Speakers Using Supervised Learning},
year = {2019},
issue_date = {February 2019},
publisher = {IEEE Press},
volume = {27},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2877892},
doi = {10.1109/TASLP.2018.2877892},
abstract = {Estimating the maximum number of concurrent speakers from single-channel mixtures is a challenging problem and an essential first step to address various audio-based tasks such as blind source separation, speaker diarization, and audio surveillance. We propose a unifying probabilistic paradigm, where deep neural network architectures are used to infer output posterior distributions. These probabilities are in turn processed to yield discrete point estimates. Designing such architectures often involves two important and complementary aspects that we investigate and discuss. First, we study how recent advances in deep architectures may be exploited for the task of speaker count estimation. In particular, we show that convolutional recurrent neural networks outperform recurrent networks used in a previous study when adequate input features are used. Even for short segments of speech mixtures, we can estimate up to five speakers, with a significantly lower error than other methods. Second, through comprehensive evaluation, we compare the best-performing method to several baselines, as well as the influence of gain variations, different data sets, and reverberation. The output of our proposed method is compared to human performance. Finally, we give insights into the strategy used by our proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {268–282},
numpages = {15}
}

@article{10.1109/TASLP.2018.2884853,
author = {Hakkani-Tur, Dilek},
title = {Inaugural Editorial Innovations in an Era of Ubiquitous Audio, Speech, and Language Processing},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2884853},
doi = {10.1109/TASLP.2018.2884853},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {5–6},
numpages = {2}
}

@article{10.1109/TASLP.2018.2875325,
title = {Erratum for “Nonlinear Audio Systems Identification Through Audio Input Gaussianization”},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2875325},
doi = {10.1109/TASLP.2018.2875325},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {240},
numpages = {1}
}

@article{10.1109/TASLP.2018.2873897,
author = {MV, Achuth Rao and Ghosh, Prasanta Kumar},
title = {Glottal Inverse Filtering Using Probabilistic Weighted Linear Prediction},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2873897},
doi = {10.1109/TASLP.2018.2873897},
abstract = {Glottal inverse filtering is a noninvasive method for getting the glottal flow estimate from the speech. In this paper, we propose a method for glottal inverse filtering based on probabilistic weighted linear prediction PWLP in which the speech is assumed to be the output of an all-pole filter with glottal flow as an excitation. First, we introduce a probabilistic interpretation of the WLP, and we propose a probabilistic temporal weighting as convolution of a binary vector and a fixed window. We construct the posterior distribution based on the PWLP likelihood and a Gaussian prior on the filter coefficients. The parameters are estimated using the Gibbs sampling. The experiments are performed using the Lijencrants–Fant LF model based synthetic data, a physical model based synthetic data of different vowels and real speech data. Results demonstrate that the proposed method outperforms the best of the existing state-of-the-art methods in terms of the normalized amplitude quotient by 0.035 and 0.12 for the LF model and physical model based synthetic data, respectively. The results based on real speech data show that the glottal flow estimated by the proposed method in the closed phase is flatter and has less formant ripple compared to existing state-of-the-art methods. We also show two key features of the proposed method: first, the proposed method does not need prior detection of glottal closure or opening instants. The temporal weights are learnt in a data-driven manner, which is often found to be high near the closed phase of the glottal cycle, second, the Gaussian prior helps in estimating the filter coefficients when the closed phase duration is small.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {114–124},
numpages = {11}
}

@article{10.1109/TASLP.2018.2869692,
author = {Duong, Thanh Thi Hien and Duong, Ngoc Q. K. and Nguyen, Phuong Cong and Nguyen, Cuong Quoc and Nguyen, Phuong Cong and Duong, Ngoc Q. K. and Nguyen, Cuong Quoc and Duong, Thanh Thi Hien},
title = {Gaussian Modeling-Based Multichannel Audio Source Separation Exploiting Generic Source Spectral Model},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2869692},
doi = {10.1109/TASLP.2018.2869692},
abstract = {As blind audio source separation has remained very challenging in real-world scenarios, some existing works, including ours, have investigated the use of a weakly informed approach where generic source spectral models GSSM can be learned a priori based on nonnegative matrix factorization NMF. Such approach was derived for single-channel audio mixtures and shown to be efficient in different settings. This paper proposes a multichannel source separation approach where the GSSM is combined with the source spatial covariance model within a unified Gaussian modeling framework. We present the generalized expectation-minimization EM algorithm for the para-meter estimation. Especially, for guiding the estimation of the intermediate source variances in each EM iteration, we investigate the use of two criteria: First, the estimated variances of each source are constrained by NMF, and finally, the total variances of all sources are constrained by NMF altogether. While the former can be seen as a source variance denoising step, the latter is viewed as an additional separation step applied to the source variance. We demonstrate the speech separation performance, together with its convergence and stability with respect to parameter setting, of the proposed approach using a benchmark dataset provided within the 2016 Signal Separation Evaluation Campaign.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {32–43},
numpages = {12}
}

@article{10.1109/TASLP.2018.2869686,
author = {Zhang, Guoqiang and Tao, Jiancheng and Qiu, Xiaojun and Burnett, Ian and Burnett, Ian and Qiu, Xiaojun and Zhang, Guoqiang and Tao, Jiancheng},
title = {Decentralized Two-Channel Active Noise Control for Single Frequency by Shaping Matrix Eigenvalues},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2869686},
doi = {10.1109/TASLP.2018.2869686},
abstract = {In an active noise control ANC system, computational complexity is one major concern when designing practical control algorithms. For an ANC system with multiple secondary sources and error microphones, one approach to reducing computational complexity is to apply a decentralized control scheme rather than centralized approaches. A decentralized scheme attempts to control a number of small-size ANC subsystems independently. In this paper, we consider the decentralized control of a two-channel ANC system tackling a noise disturbance in the frequency domain, where each channel consists of one secondary source and one error microphone. We propose a decentralized control method that is able to achieve the same noise reduction performance as the centralized controller with guaranteed convergence. The key step in designing the control method is to properly shape the eigenvalues of a matrix that models the two-channel secondary paths for each frequency index.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {44–52},
numpages = {9}
}

@article{10.1109/TASLP.2018.2875170,
author = {Yu, Jianfei and Jiang, Jing and Xia, Rui},
title = {Global Inference for Aspect and Opinion Terms Co-Extraction Based on Multi-Task Neural Networks},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2875170},
doi = {10.1109/TASLP.2018.2875170},
abstract = {Extracting aspect terms and opinion terms are two fundamental tasks in opinion mining. The recent success of deep learning has inspired various neural network architectures, which have been shown to achieve highly competitive performance in these two tasks. However, most existing methods fail to explicitly consider the syntactic relations among aspect terms and opinion terms, which may lead to the inconsistencies between the model predictions and the syntactic constraints. To this end, we first apply a multi-task learning framework to implicitly capture the relations between the two tasks, and then propose a global inference method by explicitly modelling several syntactic constraints among aspect term extraction and opinion term extraction to uncover their intra-task and inter-task relationship, which seeks an optimal solution over the neural predictions for both tasks. Extensive evaluations on three benchmark datasets demonstrate that our global inference approach is able to bring consistent improvements over several base models in different scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {168–177},
numpages = {10}
}

@article{10.1109/TASLP.2018.2871755,
author = {Moriya, Takafumi and Tanaka, Tomohiro and Shinozaki, Takahiro and Watanabe, Shinji and Duh, Kevin},
title = {Evolution-Strategy-Based Automation of System Development for High-Performance Speech Recognition},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2871755},
doi = {10.1109/TASLP.2018.2871755},
abstract = {The state-of-the-art large vocabulary speech recognition systems consist of several components including hidden Markov model and deep neural network. To realize the highest recognition performance, numerous meta-parameters specifying the designs and training setups of these components must be optimized. A prominent obstacle in system development is the laborious effort required by human experts in tuning these meta-parameters. To automate the process, we propose to tune the meta-parameters of a whole large vocabulary speech recognition system using the evolution strategy with a multi-objective Pareto optimization. As the result of the evolution, the system is optimized for both low word error rate and compact model size. Since the approach requires repeated training and evaluation of the recognition systems that require large computation, we make use of parallel computation on cloud computers. Experimental results show the effectiveness of the proposed approach by discovering appropriate configuration for large vocabulary speech recognition systems automatically.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {77–88},
numpages = {12}
}

@article{10.1109/TASLP.2018.2876177,
author = {Laufer, Yaron and Gannot, Sharon},
title = {A Bayesian Hierarchical Model for Speech Enhancement With Time-Varying Audio Channel},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2876177},
doi = {10.1109/TASLP.2018.2876177},
abstract = {We present a fully Bayesian hierarchical approach for multichannel speech enhancement with time-varying audio channel. Our probabilistic approach relies on a Gaussian prior for the speech signal and a Gamma hyperprior for the speech precision, combined with a multichannel linear-Gaussian state-space model for the acoustic channel. Furthermore, we assume a Wishart prior for the noise precision matrix. We derive a variational expectation-maximization VEM algorithm that uses a variant of a multichannel Wiener filter MCWF to infer the sound source and a Kalman smoother to infer the acoustic channel. It is further shown that the VEM speech estimator can be recasted as a multichannel minimum variance distortionless response MVDR beamformer followed by a single-channel variational postfilter. The proposed algorithm was evaluated using both simulated and real room environments with several noise types and reverberation levels. Both static and dynamic scenarios are considered. In terms of speech quality, it is shown that a significant improvement is obtained with respect to the noisy signal, and that the proposed method outperforms a baseline algorithm. In terms of channel alignment and tracking ability, a superior channel estimate is demonstrated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {225–239},
numpages = {15}
}

@article{10.1109/TASLP.2018.2876172,
author = {Amini, Jamal and Hendriks, Richard C. and Heusdens, Richard and Guo, Meng and Jensen, Jesper},
title = {Asymmetric Coding for Rate-Constrained Noise Reduction in Binaural Hearing Aids},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2876172},
doi = {10.1109/TASLP.2018.2876172},
abstract = {Binaural hearing aids HAs can potentially perform advanced noise reduction algorithms, leading to an improvement over monaural/bilateral HAs. Due to the limited transmission capacities between the HAs and given knowledge of the complete joint noisy signal statistics, the optimal rate-constrained beamforming strategy is known from the literature. However, as these joint statistics are unknown in practice, sub-optimal strategies have been presented. In this paper, we present a unified framework to study the performance of these existing optimal and sub-optimal rate-constrained beamforming methods for binaural HAs. Moreover, we propose to use an asymmetric sequential coding scheme to estimate the joint statistics between the microphones in the two HAs. We show that under certain assumptions, this leads to sub-optimal performance in one HA but allows to obtain the truly optimal performance in the second HA. Based on the mean square error distortion measure, we evaluate the performance improvement between monaural beamforming no communication and the proposed scheme, as well as the optimal and the existing sub-optimal strategies in terms of the information bit-rate. The results show that the proposed method outperforms existing practical approaches in most scenarios, especially at middle rates and high rates, without having the prior knowledge of the joint statistics.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {154–167},
numpages = {14}
}

@article{10.1109/TASLP.2018.2874708,
author = {Sun, Yang and Wang, Wenwu and Chambers, Jonathon and Naqvi, Syed Mohsen},
title = {Two-Stage Monaural Source Separation in Reverberant Room Environments Using Deep Neural Networks},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2874708},
doi = {10.1109/TASLP.2018.2874708},
abstract = {Deep neural networks DNNs have been used for dereverberation and separation in the monaural source separation problem. However, the performance of current state-of-the-art methods is limited, particularly when applied in highly reverberant room environments. In this paper, we propose a two-stage approach with two DNN-based methods to address this problem. In the first stage, the dereverberation of the speech mixture is achieved with the proposed dereverberation mask DM. In the second stage, the dereverberant speech mixture is separated with the ideal ratio mask IRM. To realize this two-stage approach, in the first DNN-based method, the DM is integrated with the IRM to generate the enhanced time-frequency T-F mask, namely the ideal enhanced mask IEM, as the training target for the single DNN. In the second DNN-based method, the DM and the IRM are predicted with two individual DNNs. The IEEE and the TIMIT corpora with real room impulse responses and noise from the NOISEX dataset are used to generate speech mixtures for evaluations. The proposed methods outperform the state-of-the-art specifically in highly reverberant room environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {125–139},
numpages = {15}
}

@article{10.1109/TASLP.2018.2875269,
author = {Ngo, Gia H. and Nguyen, Minh and Chen, Nancy F.},
title = {Phonology-Augmented Statistical Framework for Machine Transliteration Using Limited Linguistic Resources},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2875269},
doi = {10.1109/TASLP.2018.2875269},
abstract = {Transliteration converts words in a source language e.g., English into words in a target language e.g., Vietnamese. This conversion considers the phonological structure of the target language, as the transliterated output needs to be pronounceable in the target language. For example, a word in Vietnamese that begins with a consonant cluster is phonologically invalid and thus would be an incorrect output of a transliteration system. Most statistical transliteration approaches, albeit being widely adopted, do not explicitly model the target language's phonology, which often results in invalid outputs. The problem is compounded by the limited linguistic resources available when converting foreign words to transliterated words in the target language. In this paper, we present a phonology-augmented statistical framework suitable for transliteration, especially when only limited linguistic resources are available. We propose the concept of pseudo-syllables as structures representing how segments of a foreign word are organized according to the syllables of the target language's phonology. We performed transliteration experiments on Vietnamese and Cantonese. We show that the proposed framework outperforms the statistical baseline by up to 44.68% relative, when there are limited training examples 587 entries.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {199–211},
numpages = {13}
}

@article{10.1109/TASLP.2018.2877258,
author = {Koizumi, Yuma and Saito, Shoichiro and Uematsu, Hisashi and Kawachi, Yuta and Harada, Noboru},
title = {Unsupervised Detection of Anomalous Sound Based on Deep Learning and the Neyman–Pearson Lemma},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2877258},
doi = {10.1109/TASLP.2018.2877258},
abstract = {This paper proposes a novel optimization principle and its implementation for unsupervised anomaly detection in sound ADS using an autoencoder AE. The goal of the unsupervised-ADS is to detect unknown anomalous sounds without training data of anomalous sounds. The use of an AE as a normal model is a state-of-the-art technique for the unsupervised-ADS. To decrease the false positive rate FPR, the AE is trained to minimize the reconstruction error of normal sounds, and the anomaly score is calculated as the reconstruction error of the observed sound. Unfortunately, since this training procedure does not take into account the anomaly score for anomalous sounds, the true positive rate TPR does not necessarily increase. In this study, we define an objective function based on the Neyman–Pearson lemma by considering the ADS as a statistical hypothesis test. The proposed objective function trains the AE to maximize the TPR under an arbitrary low FPR condition. To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm. Through experiments using synthetic data, we found that the proposed method improved the performance measures of the ADS under low FPR conditions. In addition, we confirmed that the proposed method could detect anomalous sounds in real environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {212–224},
numpages = {13}
}

@article{10.1109/TASLP.2018.2876169,
author = {Wang, Zhong-Qiu and Zhang, Xueliang and Wang, DeLiang},
title = {Robust Speaker Localization Guided by Deep Learning-Based Time-Frequency Masking},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2876169},
doi = {10.1109/TASLP.2018.2876169},
abstract = {Deep learning-based time-frequency T-F masking has dramatically advanced monaural single-channel speech separation and enhancement. This study investigates its potential for direction of arrival DOA estimation in noisy and reverberant environments. We explore ways of combining T-F masking and conventional localization algorithms, such as generalized cross correlation with phase transform, as well as newly proposed algorithms based on steered-response SNR and steering vectors. The key idea is to utilize deep neural networks DNNs to identify speech dominant T-F units containing relatively clean phase for DOA estimation. Our DNN is trained using only monaural spectral information, and this makes the trained model directly applicable to arrays with various numbers of microphones arranged in diverse geometries. Although only monaural information is used for training, experimental results show strong robustness of the proposed approach in new environments with intense noise and room reverberation, outperforming traditional DOA estimation methods by large margins. Our study also suggests that the ideal ratio mask and its variants remain effective training targets for robust speaker localization.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {178–188},
numpages = {11}
}

@article{10.1109/TASLP.2018.2869684,
author = {Magron, Paul and Virtanen, Tuomas and Magron, Paul and Virtanen, Tuomas},
title = {Complex ISNMF: A Phase-Aware Model for Monaural Audio Source Separation},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2869684},
doi = {10.1109/TASLP.2018.2869684},
abstract = {This paper introduces a phase-aware probabilistic model for audio source separation. Classical source models in the short-time Fourier transform domain use circularly-symmetric Gaussian or Poisson random variables. This is equivalent to assuming that the phase of each source is uniformly distributed, which is not suitable for exploiting the underlying structure of the phase. Drawing on preliminary works, we introduce here a Bayesian anisotropic Gaussian source model in which the phase is no longer uniform. Such a model permits us to favor a phase value that originates from a signal model through a Markov chain prior structure. The variance of the latent variables are structured with nonnegative matrix factorization NMF. The resulting model is called complex Itakura–Saito NMF ISNMF since it generalizes the ISNMF model to the case of nonisotropic variables. It combines the advantages of ISNMF, which uses a distortion measure adapted to audio and yields a set of estimates which preserve the overall energy of the mixture, and of complex NMF, which enables one to account for some phase constraints. We derive a generalized expectation-maximization algorithm to estimate the model parameters. Experiments conducted on a musical source separation task in a semiinformed setting show that the proposed approach outperforms state-of-the-art phase-aware separation techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {20–31},
numpages = {12}
}

@article{10.1109/TASLP.2018.2870742,
author = {Zheng, Naijun and Zhang, Xiao-Lei},
title = {Phase-Aware Speech Enhancement Based on Deep Neural Networks},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2870742},
doi = {10.1109/TASLP.2018.2870742},
abstract = {Short-time frequency transform STFT is fundamental in speech processing. Because of the difficulty of processing highly unstructured STFT phase, most speech-processing algorithms only operate with STFT magnitude, leaving the STFT phase far from explored. However, with the recent development of deep neural network DNN based speech processing, e.g., speech enhancement and recognition, phase processing is becoming more important than ever before as a new growing point of DNN-based methods. In this paper, we propose a phase-aware speech enhancement algorithm based on DNN. Specifically, in the training stage, when incorporating phase as a target, our core idea is to transform an unstructured phase spectrogram to its derivative along the time axis, i.e., instantaneous frequency deviation IFD, which has a similar structure with its corresponding magnitude spectrogram. We further propose to optimize both IFD and magnitude jointly in a multiobjective learning framework. In the test stage, we propose a postprocessing method to recover the phase spectrogram from the estimated IFD. Experimental results demonstrate the effectiveness of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {63–76},
numpages = {14}
}

@article{10.1109/TASLP.2018.2875794,
author = {Ferrer, Luciana and Nandwana, Mahesh Kumar and McLaren, Mitchell and Castan, Diego and Lawson, Aaron},
title = {Toward Fail-Safe Speaker Recognition: Trial-Based Calibration With a Reject Option},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2875794},
doi = {10.1109/TASLP.2018.2875794},
abstract = {The output scores of most of the speaker recognition systems are not directly interpretable as stand-alone values. For this reason, a calibration step is usually performed on the scores to convert them into proper likelihood ratios, which have a clear probabilistic interpretation. The standard calibration approach transforms the system scores using a linear function trained using data selected to closely match the evaluation conditions. This selection, though, is not feasible when the evaluation conditions are unknown. In previous work, we proposed a calibration approach for this scenario called trial-based calibration TBC. TBC trains a separate calibration model for each test trial using data that is dynamically selected from a candidate training set to match the conditions of the trial. In this work, we extend the TBC method, proposing: 1 a new similarity metric for selecting training data that result in significant gains over the one proposed in the original work; 2 a new option that enables the system to reject a trial when not enough matched data are available for training the calibration model; and 3 the use of regularization to improve the robustness of the calibration models trained for each trial. We test the proposed algorithms on a development set composed of several conditions and on the Federal Bureau of Investigation multi-condition speaker recognition dataset, and we demonstrate that the proposed approach reduces calibration loss to values close to 0 for most of the conditions when matched calibration data are available for selection, and that it can reject most of the trials for which relevant calibration data are unavailable.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {140–153},
numpages = {14}
}

@article{10.1109/TASLP.2018.2868407,
author = {Bao, Feng and Abdulla, Waleed H. and Bao, Feng and Abdulla, Waleed H.},
title = {A New Ratio Mask Representation for CASA-Based Speech Enhancement},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2868407},
doi = {10.1109/TASLP.2018.2868407},
abstract = {In the computational auditory scene analysis method, the ideal ratio mask or alternatively the ideal binary mask is the key point to reconstruct the enhanced signal. The ratio mask in its Wiener filtering or its square root form is currently considered. However, this kind of ratio mask overlooked one important issue. It does not exploit the inter-channel correlation ICC in the noisy speech, noise, and clean speech spectra. Thus, in this paper, we first propose a novel ratio mask representation by utilizing the ICC. In this way, we adaptively reallocate the power ratio of the speech and noise during the construction of ratio mask, thus, more speech and noise components are retained and masked at the same time, respectively. Second, the channel-weight contour based on the equal loudness hearing attribute is adopted to revise this new ratio mask in each Gammatone filterbank channel. Finally, the revised ratio mask is effectively used to train a five-layer structured deep neural network. Experiments show that the proposed ratio mask performs better than the conventional ratio mask representation and other series of enhancement algorithms in terms of speech quality, intelligibility, and spectral distortion under different signal to noise ratio conditions using six types of noises.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {7–19},
numpages = {13}
}

@article{10.1109/TASLP.2018.2876171,
author = {Tan, Ke and Chen, Jitong and Wang, DeLiang},
title = {Gated Residual Networks With Dilated Convolutions for Monaural Speech Enhancement},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2876171},
doi = {10.1109/TASLP.2018.2876171},
abstract = {For supervised speech enhancement, contextual information is important for accurate mask estimation or spectral mapping. However, commonly used deep neural networks DNNs are limited in capturing temporal contexts. To leverage long-term contexts for tracking a target speaker, we treat speech enhancement as a sequence-to-sequence mapping, and present a novel convolutional neural network CNN architecture for monaural speech enhancement. The key idea is to systematically aggregate contexts through dilated convolutions, which significantly expand receptive fields. The CNN model additionally incorporates gating mechanisms and residual learning. Our experimental results suggest that the proposed model generalizes well to untrained noises and untrained speakers. It consistently outperforms a DNN, a unidirectional long short-term memory LSTM model, and a bidirectional LSTM model in terms of objective speech intelligibility and quality metrics. Moreover, the proposed model has far fewer parameters than DNN and LSTM models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {189–198},
numpages = {10}
}

@article{10.1109/TASLP.2018.2870725,
author = {Zhao, Yan and Wang, Zhong-Qiu and Wang, DeLiang},
title = {Two-Stage Deep Learning for Noisy-Reverberant Speech Enhancement},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2870725},
doi = {10.1109/TASLP.2018.2870725},
abstract = {In real-world situations, speech reaching our ears is commonly corrupted by both room reverberation and background noise. These distortions are detrimental to speech intelligibility and quality, and also pose a serious problem to many speech-related applications, including automatic speech and speaker recognition. In order to deal with the combined effects of noise and reverberation, we propose a two-stage strategy to enhance corrupted speech, where denoising and dereverberation are conducted sequentially using deep neural networks. In addition, we design a new objective function that incorporates clean phase during model training to better estimate spectral magnitudes, which would in turn yield better phase estimates when combined with iterative phase reconstruction. The two-stage model is then jointly trained to optimize the proposed objective function. Systematic evaluations and comparisons show that the proposed algorithm improves objective metrics of speech intelligibility and quality substantially, and significantly outperforms previous one-stage enhancement systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {53–62},
numpages = {10}
}

@article{10.1109/TASLP.2018.2872128,
author = {Kavalekalam, Mathew Shaji and Nielsen, Jesper Kjar and Boldt, Jesper Bunsow and Christensen, Mads Grasboll},
title = {Model-Based Speech Enhancement for Intelligibility Improvement in Binaural Hearing Aids},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2872128},
doi = {10.1109/TASLP.2018.2872128},
abstract = {Speech intelligibility is often severely degraded among hearing impaired individuals in situations such as the cocktail party scenario. The performance of the current hearing aid technology has been observed to be limited in these scenarios. In this paper, we propose a binaural speech enhancement framework that takes into consideration the speech production model. The enhancement framework proposed here is based on the Kalman filter that allows us to take the speech production dynamics into account during the enhancement process. The usage of a Kalman filter requires the estimation of clean speech and noise short term predictor STP parameters, and the clean speech pitch parameters. In this work, a binaural codebook-based method is proposed for estimating the STP parameters, and a directional pitch estimator based on the harmonic model and maximum likelihood principle is used to estimate the pitch parameters. The proposed method for estimating the STP and pitch parameters jointly uses the information from left and right ears, leading to a more robust estimation of the filter parameters. Objective measures such as PESQ and STOI have been used to evaluate the enhancement framework in different acoustic scenarios representative of the cocktail party scenario. We have also conducted subjective listening tests on a set of nine normal hearing subjects, to evaluate the performance in terms of intelligibility and quality improvement. The listening tests show that the proposed algorithm, even with access to only a single channel noisy observation, significantly improves the overall speech quality, and the speech intelligibility by up to $text{15}{%}$.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {99–113},
numpages = {15}
}

@article{10.1109/TASLP.2018.2872106,
author = {Kamper, Herman and Shakhnarovich, Gregory and Livescu, Karen},
title = {Semantic Speech Retrieval With a Visually Grounded Model of Untranscribed Speech},
year = {2019},
issue_date = {January 2019},
publisher = {IEEE Press},
volume = {27},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2872106},
doi = {10.1109/TASLP.2018.2872106},
abstract = {There is a growing interest in models that can learn from unlabelled speech paired with visual context. This setting is relevant for low-resource speech processing, robotics, and human language acquisition research. Here, we study how a visually grounded speech model, trained on images of scenes paired with spoken captions, captures aspects of semantics. We use an external image tagger to generate soft text labels from images, which serve as targets for a neural model that maps untranscribed speech to semantic keyword labels. We introduce a newly collected data set of human semantic relevance judgements and an associated task, semantic speech retrieval, where the goal is to search for spoken utterances that are semantically relevant to a given text query. Without seeing any text, the model trained on parallel speech and images achieves a precision of almost 60% on its top ten semantic retrievals. Compared to a supervised model trained on transcriptions, our model matches human judgements better by some measures, especially in retrieving non-verbatim semantic matches. We perform an extensive analysis of the model and its resulting representations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {89–98},
numpages = {10}
}

@article{10.1109/TASLP.2018.2879265,
author = {Li, H.},
title = {Farewell Editorial},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2879265},
doi = {10.1109/TASLP.2018.2879265},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2489},
numpages = {1}
}

@article{10.1109/TASLP.2018.2866707,
author = {Lin, Wei-wei and Mak, Man-Wai and Chien, Jen-Tzung},
title = {Multisource I-Vectors Domain Adaptation Using Maximum Mean Discrepancy Based Autoencoders},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2866707},
doi = {10.1109/TASLP.2018.2866707},
abstract = {Like many machine learning tasks, the performance of speaker verification SV systems degrades when training and test data come from very different distributions. What's more, both training and test data themselves could be composed of heterogeneous subsets. These multisource mismatches are detrimental to SV performance. This paper proposes incorporating maximum mean discrepancy MMD into the loss function of autoencoders to reduce these mismatches. MMD is a nonparametric method for measuring the distance between two probability distributions. With a properly chosen kernel, MMD can match up to infinite moments of data distributions. We generalize MMD to measure the discrepancies of multiple distributions. We call the generalized MMD domainwise MMD. Using domainwise MMD as an objective function, we propose two autoencoders, namely nuisance-attribute autoencoder NAE and domain-invariant autoencoder DAE, for multisource i-vector adaptation. NAE encodes the features that cause most of the multisource mismatch measured by domainwise MMD. DAE directly encodes the features that minimize the multisource mismatch. Using these MMD-based autoencoders as a preprocessing step for PLDA training, we achieve a relative improvement of 19.2% EER on the NIST 2016 SRE compared to PLDA without adaptation. We also found that MMD-based autoencoders are more robust to unseen domains. In the domain robustness experiments, MMD-based autoencoders show 6.8% and 5.2% improvements over IDVC on female and male Cantonese speakers, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2412–2422},
numpages = {11}
}

@article{10.1109/TASLP.2018.2867081,
author = {El Badawy, Dalia and Dokmanic, Ivan},
title = {Direction of Arrival With One Microphone, a Few LEGOs, and Non-Negative Matrix Factorization},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2867081},
doi = {10.1109/TASLP.2018.2867081},
abstract = {Conventional approaches to sound source localization require at least two microphones. It is known, however, that people with unilateral hearing loss can also localize sounds. Monaural localization is possible thanks to the scattering by the head, though it hinges on learning the spectra of the various sources. We take inspiration from this human ability to propose algorithms for accurate sound source localization using a single microphone embedded in an arbitrary scattering structure. The structure modifies the frequency response of the microphone in a direction-dependent way giving each direction a signature. While knowing those signatures is sufficient to localize sources of white noise, localizing speech is much more challenging: it is an ill-posed inverse problem, which we regularize by prior knowledge in the form of learned non-negative dictionaries. We demonstrate a monaural speech localization algorithm based on non-negative matrix factorization that does not depend on sophisticated, designed scatterers. In fact, we show experimental results with ad hoc scatterers made of LEGO bricks. Even with these rudimentary structures we can accurately localize arbitrary speakers; that is, we do not need to learn the dictionary for the particular speaker to be localized. Finally, we discuss multi-source localization and the related limitations of our approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2436–2446},
numpages = {11}
}

@article{10.1109/TASLP.2018.2865615,
author = {Martin-Morato, Irene and Cobos, Maximo and Ferri, Francesc J.},
title = {Adaptive Mid-Term Representations for Robust Audio Event Classification},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2865615},
doi = {10.1109/TASLP.2018.2865615},
abstract = {Low-level audio features are commonly used in many audio analysis tasks, such as audio scene classification or acoustic event detection. Due to the variable length of audio signals, it is a common approach to create fixed-length feature vectors consisting of a set of statistics that summarize the temporal variability of such short-term features. To avoid the loss of temporal information, the audio event can be divided into a set of mid-term segments or texture windows. However, such an approach requires to estimate accurately the onset and offset times of the audio events in order to obtain a robust mid-term statistical description of their temporal evolution. This paper proposes the use of an alternative event representation based on nonlinear time normalization prior to the extraction of mid-term statistics. The short-term features are transformed into a new fixed-length representation that considers uniform distance subsampling over a defined feature space in contrast to the classical short-term temporal framing. The results show that the use of distance-based texture windows provides an improved statistical description of the event robust to errors in the event segmentation stage under noisy conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2381–2392},
numpages = {12}
}

@article{10.1109/TASLP.2018.2852739,
author = {Lee, Hung-Yi and Chung, Pei-Hung and Wu, Yen-Chen and Lin, Tzu-Hsiang and Wen, Tsung-Hsien},
title = {Interactive Spoken Content Retrieval by Deep Reinforcement Learning},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2852739},
doi = {10.1109/TASLP.2018.2852739},
abstract = {For text content retrieval, the user can easily scan through and select from a list of retrieved items. This is impossible for spoken content retrieval, because the retrieved items are not easily displayed on-screen. In addition, due to the high degree of uncertainty for speech recognition, retrieval results can be very noisy. One way to counter such difficulties is through user-machine interaction. The machine can take different actions to interact with the user to obtain better retrieval results before showing them to the user. For example, the machine can request extra information from the user, return a list of topics for the user to select from, and so on. In this paper, we propose using deep-Q-network DQN to determine the machine actions for interactive spoken content retrieval. DQN bypasses the need to estimate hand-crafted states, and directly determines the best action based on the present retrieval results even without any human knowledge. It is shown to achieve significantly better performance as compared with the previous hand-crafted states. We further find that double DQN and dueling DQN improve the naive version.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2447–2459},
numpages = {13}
}

@article{10.1109/TASLP.2018.2862641,
author = {Enzner, Gerald and Thune, Philipp},
title = {Bayesian MMSE Filtering of Noisy Speech by SNR Marginalization With Global PSD Priors},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2862641},
doi = {10.1109/TASLP.2018.2862641},
abstract = {MMSE filtering of speech with additive noise and latent speech power-spectral density PSD is addressed. This problem is strong in single-channel speech enhancement and restricts the utility of stationary Wiener filters or other statistical estimators based on PSDs. The issue typically manifests itself in residual noise after filtering, despite the availability of the noise PSD. Our paper therefore incorporates the latent speech PSD state via marginalization into the MMSE estimation framework of complex speech spectral amplitudes. The hence involved joint posterior distribution of the complex speech amplitude and speech PSD, conditioned on just the noisy observations, is then resolved in the Bayesian sense into a speech and a speech-PSD posterior. The latter is expressed via the local data likelihood and a hyper-prior of the local speech PSD or a-priori SNR—i.e., a global distribution across the entire speech signal. Marginalization, in this way, turns into expectation over a latent Wiener filter, such that explicit estimation of local a-priori SNR is eliminated. The local input data in the form of the a-posteriori SNR and the global SNR value as a descriptor of the overall speech-in-noise condition turns out sufficient to control our resulting MMSE spectral gain function, and, potentially, can be provided much easier than the latent and time-varying a-priori SNR. An improved balance of residual noise and speech quality in the enhancement of noisy speech is demonstrated by objective experimental evaluation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2289–2304},
numpages = {16}
}

@article{10.1109/TASLP.2018.2864535,
author = {Surendran, Sudeep and Kumar, T. Kishore},
title = {Oblique Projection and Cepstral Subtraction in Signal Subspace Speech Enhancement for Colored Noise Reduction},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2864535},
doi = {10.1109/TASLP.2018.2864535},
abstract = {In this paper, a subspace speech enhancement method handling the case of colored noise using oblique projection in the cepstral domain is proposed. Perceptual features and variance normalization are used to reduce the residual noise and improve the intelligibility of the output speech. Initially, the additive noise present in the noisy speech is removed by removing the orthogonal noise subspace from the noisy speech subspace to obtain the speech subspace. Then, the oblique projection of the noise subspace on the speech subspace along the additive noise subspace is used to determine the colored noise that remains. Colored noise removal is performed by power spectral subtraction in the cepstral domain. The spectral domain constrained estimator that incorporates the combined masking property of the human auditory system is employed to estimate the clean speech signal using the variance of the colored noise. To avoid the occurrence of any abrupt spikes in the output, variance normalization is performed by adaptively changing the control parameter of the estimator's gain matrix. The spectrograms, the objective measures and the subjective intelligibility test show the superior performance of the proposed method over the other existing speech enhancement methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2328–2340},
numpages = {13}
}

@article{10.1109/TASLP.2018.2868416,
author = {Bao, Yu and Chen, Huawei},
title = {A Chance-Constrained Programming Approach to the Design of Robust Broadband Beamformers With Microphone Mismatches},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2868416},
doi = {10.1109/TASLP.2018.2868416},
abstract = {This paper addresses the design of robust broadband beamformers by taking into account the prior knowledge of uncertain means and variances of microphone mismatches. A chance-constrained programming CCP based design approach is proposed, which is a generic extension of the existing worst case mean performance optimization WMPO based counterpart by using a chance-constrained stopband level constraint instead of a hard one. The original CCP-based design problem, however, is NP-hard to solve. To deal with the problem, a relaxed convex optimization formulation is developed by exploiting the distribution property of array response distortion due to microphone mismatches. The relation between the proposed CCP- and WMPO-based design approaches is analyzed theoretically. It shows that there always exists a chance constraint probability under which the CCP-based design is comparable to the WMPO-based one, which provides an insight into the overconservatism problem in the WMPO-based design from the perspective of the CCP-based framework. Moreover, a criterion on the setting of the chance constraint probability is also derived for the proposed design approach in order to remedy the overconservatism problem. Simulation and real experimental results are presented to show the superior performance of the proposed design approach compared with its state-of-the-art counterparts.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2475–2488},
numpages = {14}
}

@article{10.1109/TASLP.2018.2865091,
author = {Firtha, Gergely and Fiala, Peter and Schultz, Frank and Spors, Sascha and Fiala, Peter and Schultz, Frank and Spors, Sascha and Firtha, Gergely},
title = {On the General Relation of Wave Field Synthesis and Spectral Division Method for Linear Arrays},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2865091},
doi = {10.1109/TASLP.2018.2865091},
abstract = {Sound field synthesis aims at the reproduction of an arbitrary target sound field over an extended listening area applying a densely spaced loudspeaker ensemble. Two basic analytic methodologies—the explicit and the implicit—exist in order to derive the required loudspeaker driving functions. The explicit solution aims at the direct solution of the involved integral equation describing the general sound field synthesis problem, resulting in driving functions in the form of a spectral integral. The implicit solution extracts the driving function from an appropriate boundary integral representation of the target sound field. So far the relationship between two approaches was investigated for target field specific synthesis scenarios. For linear arrays this paper introduces a high-frequency approximation for the explicit solution resulting in a novel, purely spatial domain formulation of the direct approach. The presented driving functions allow the synthesis of an arbitrary virtual sound field, optimizing the reproduction on an arbitrary reference line. It is furthermore shown that for an arbitrary virtual sound field, the implicit solution constitutes a high-frequency approximation of the explicit method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2393–2403},
numpages = {11}
}

@article{10.1109/TASLP.2018.2864577,
author = {Zhang, Wen and Hofmann, Christian and Buerger, Michael and Abhayapala, Thushara Dheemantha and Kellermann, Walter},
title = {Spatial Noise-Field Control With Online Secondary Path Modeling: A Wave-Domain Approach},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2864577},
doi = {10.1109/TASLP.2018.2864577},
abstract = {Due to strong interchannel interference in multichannel active noise control ANC, there are fundamental problems associated with the filter adaptation and online secondary path modeling remains a major challenge. This paper proposes a wave-domain adaptation algorithm for multichannel ANC with online secondary path modelling to cancel tonal noise over an extended region of two-dimensional plane in a reverberant room. The design is based on exploiting the diagonal-dominance property of the secondary path in the wave domain. The proposed wave-domain secondary path model is applicable to both concentric and nonconcentric circular loudspeakers and microphone array placement, and is also robust against array positioning errors. Normalized least mean squares-type algorithms are adopted for adaptive feedback control. Computational complexity is analyzed and compared with the conventional time-domain and frequency-domain multichannel ANCs. Through simulation-based verification in comparison with existing methods, the proposed algorithm demonstrates more efficient adaptation with low-level auxiliary noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2355–2370},
numpages = {16}
}

@article{10.1109/TASLP.2018.2865609,
author = {Birkholz, Peter and Stone, Simon and Wolf, Klaus and Plettemeier, Dirk and Wolf, Klaus and Plettemeier, Dirk and Stone, Simon and Birkholz, Peter},
title = {Non-Invasive Silent Phoneme Recognition Using Microwave Signals},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2865609},
doi = {10.1109/TASLP.2018.2865609},
abstract = {Besides the recognition of audible speech, there is currently an increasing interest in the recognition of silent speech, which has a range of novel applications. A major obstacle for a wide spread of silent-speech technology is the lack of measurement methods for speech movements that are convenient, non-invasive, portable, and robust at the same time. Therefore, as an alternative to established methods, we examined to what extent different phonemes can be discriminated from the electromagnetic transmission and reflection properties of the vocal tract. To this end, we attached two Vivaldi antennas on the cheek and below the chin of two subjects. While the subjects produced 25 phonemes in multiple phonetic contexts each, we measured the electromagnetic transmission spectra from one antenna to the other, and the reflection spectra for each antenna radar, in a frequency band from 2–12 GHz. Two classification methods k-nearest neighbors and linear discriminant analysis were trained to predict the phoneme identity from the spectral data. With linear discriminant analysis, cross-validated phoneme recognition rates of 93% and 85% were achieved for the two subjects. Although these results are speaker- and session-dependent, they suggest that electromagnetic transmission and reflection measurements of the vocal tract have great potential for future silent-speech interfaces.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2404–2411},
numpages = {8}
}

@article{10.1109/TASLP.2018.2860287,
author = {Wang, Xing and Tu, Zhaopeng and Zhang, Min},
title = {Incorporating Statistical Machine Translation Word Knowledge Into Neural Machine Translation},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2860287},
doi = {10.1109/TASLP.2018.2860287},
abstract = {Neural machine translation NMT has gained more and more attention in recent years, mainly due to its simplicity yet state-of-the-art performance. However, previous research has shown that NMT suffers from several limitations: source coverage guidance, translation of rare words, and the limited vocabulary, while statistical machine translation SMT has complementary properties that correspond well to these limitations. It is straightforward to improve the translation performance by combining the advantages of two kinds of models. This paper proposes a general framework for incorporating the SMT word knowledge into NMT to alleviate above word-level limitations. In our framework, the NMT decoder makes more accurate word prediction by referring to the SMT word recommendations in both training and testing phases. Specifically, the SMT model offers informative word recommendations based on the NMT decoding information. Then, we use the SMT word predictions as prior knowledge to adjust the NMT word generation probability, which unitizes a neural network based classifier to digest the discrete word knowledge. In this paper, we use two model variants to implement the framework, one with a gating mechanism and the other with a direct competition mechanism. Experimental results on Chinese-to-English and English-to-German translation tasks show that the proposed framework can take advantage of the SMT word knowledge and consistently achieve significant improvements over NMT and SMT baseline systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2255–2266},
numpages = {12}
}

@article{10.1109/TASLP.2018.2862826,
author = {Huang, Gongping and Chen, Jingdong and Benesty, Jacob},
title = {Insights Into Frequency-Invariant Beamforming With Concentric Circular Microphone Arrays},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2862826},
doi = {10.1109/TASLP.2018.2862826},
abstract = {This paper studies the problem of frequency-invariant beamforming with concentric circular microphone arrays CCMAs and presents an approach to the design of frequency-invariant and symmetric beampatterns. We first apply the Jacobi-Anger expansion to each ring of the CCMA to approximate the beampattern. The beamformer is then designed by using all the expansions from different rings. In comparison with the existing work in the literature where a Jacobi-Anger expansion of the same order is applied to different rings, here in this contribution the order of the Jacobi-Anger expansion at a ring is related to its number of sensors and, as a result, the expansion order at different rings may be different. The developed approach is rather general. It is not only able to mitigate the deep nulls problem in the directivity factor and the white noise gain, that is common to circular microphone arrays CMAs, and improve the steering flexibility, but is also flexible to use in practice where a smaller ring can have less microphones than a larger one. We discuss the conditions for the design of $N$th-order symmetric beampatterns and examples of frequency-invariant beampatterns with commonly used array geometries such as CMAs, CMAs with a sensor at the center, and CCMAs. We show the advantage of adding one microphone at the center of either a CMA or a CCMA, i.e., circumventing the deep nulls problem caused by the 0th-order Bessel function.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2305–2318},
numpages = {14}
}

@article{10.1109/TASLP.2018.2867099,
author = {Abdelwahab, Mohammed and Busso, Carlos},
title = {Domain Adversarial for Acoustic Emotion Recognition},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2867099},
doi = {10.1109/TASLP.2018.2867099},
abstract = {The performance of speech emotion recognition is affected by the differences in data distributions between train source domain and test target domain sets used to build and evaluate the models. This is a common problem, as multiple studies have shown that the performance of emotional classifiers drops when they are exposed to data that do not match the distribution used to build the emotion classifiers. The difference in data distributions becomes very clear when the training and testing data come from different domains, causing a large performance gap between development and testing performance. Due to the high cost of annotating new data and the abundance of unlabeled data, it is crucial to extract as much useful information as possible from the available unlabeled data. This study looks into the use of adversarial multitask training to extract a common representation between train and test domains. The primary task is to predict emotional-attribute-based descriptors for arousal, valence, or dominance. The secondary task is to learn a common representation, where the train and test domains cannot be distinguished. By using a gradient reversal layer, the gradients coming from the domain classifier are used to bring the source and target domain representations closer. We show that exploiting unlabeled data consistently leads to better emotion recognition performance across all emotional dimensions. We visualize the effect of adversarial training on the feature representation across the proposed deep learning architecture. The analysis shows that the data representations for the train and test domains converge as the data are passed to deeper layers of the network. We also evaluate the difference in performance when we use a shallow neural network versus a deep neural network and the effect of the number of shared layers used by the task and domain classifiers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2423–2435},
numpages = {13}
}

@article{10.1109/TASLP.2018.2862353,
author = {Meynard, Adrien and Torresani, Bruno},
title = {Spectral Analysis for Nonstationary Audio},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2862353},
doi = {10.1109/TASLP.2018.2862353},
abstract = {A new approach for the analysis of nonstationary signals is proposed, with a focus on audio applications. Following earlier contributions, nonstationarity is modeled via stationarity-breaking operators acting on Gaussian stationary random signals. The focus is on time warping and amplitude modulation, and an approximate maximum-likelihood approach based on suitable approximations in the wavelet transform domain is developed. This paper provides theoretical analysis of the approximations, and introduces joint estimation of frequency, amplitude, and spectrum, a corresponding estimation algorithm. The latter is tested and validated on synthetic as well as real audio signal.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2371–2380},
numpages = {10}
}

@article{10.1109/TASLP.2018.2860682,
author = {Zhao, Yunxin and Kuruvilla-Dugdale, Mili and Song, Minguang},
title = {Structured Sparse Spectral Transforms and Structural Measures for Voice Conversion},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2860682},
doi = {10.1109/TASLP.2018.2860682},
abstract = {We investigate a structured sparse spectral transform method for voice conversion VC to perform frequency warping and spectral shaping simultaneously on high-dimensional D STRAIGHT spectra. Learning a large transform matrix for high-D data often results in an overfit matrix with low sparsity, which leads to muffled speech in VC. We address this problem by using the frequency-warping characteristic of a source–target speaker pair to define a region of support ROS in a transform matrix, and further optimize it by nonnegative matrix factorization NMF to obtain structured sparse transform. We also investigate structural measures of spectral and temporal covariance and variance at different scales for assessing VC speech quality. Our experiments on ARCTIC dataset of 12 speaker pairs show that embedding the ROS in spectral transforms offers flexibility in tradeoffs between spectral distortion and structure preservation, and the structural measures provide quantitatively reasonable results on converted speech. Our subjective listening tests show that the proposed VC method achieves a mean opinion score of “very good” relative to natural speech, and in comparison with three other VC methods, it is the most preferred one in naturalness and in voice similarity to target speakers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2267–2276},
numpages = {10}
}

@article{10.1109/TASLP.2018.2864648,
author = {Li, Qiang and Wong, Derek F. and Chao, Lidia S. and Zhu, Muhua and Xiao, Tong and Zhu, Jingbo and Zhang, Min},
title = {Linguistic Knowledge-Aware Neural Machine Translation},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2864648},
doi = {10.1109/TASLP.2018.2864648},
abstract = {Recently, researchers have shown an increasing interest in incorporating linguistic knowledge into neural machine translation NMT. To this end, previous works choose either to alter the architecture of NMT encoder to incorporate syntactic information into the translation model, or to generalize the embedding layer of the encoder to encode additional linguistic features. The former approach mainly focuses on injecting the syntactic structure of the source sentence into the encoding process, leading to a complicated model that lacks the flexibility to incorporate other types of knowledge. The latter extends word embeddings by considering additional linguistic knowledge as features to enrich the word representation. It thus does not explicitly balance the contribution from word embeddings and the contribution from additional linguistic knowledge. To address these limitations, this paper proposes a knowledge-aware NMT approach that models additional linguistic features in parallel to the word feature. The core idea is that we propose modeling a series of linguistic features at the word level knowledge block using a recurrent neural network RNN. And in sentence level, those word-corresponding feature blocks are further encoded using a RNN encoder. In decoding, we propose a knowledge gate and an attention gate to dynamically control the proportions of information contributing to the generation of target words from different sources. Extensive experiments show that our approach is capable of better accounting for importance of additional linguistic, and we observe significant improvements from 1.0 to 2.3 BLEU points on Chinese$leftrightarrow$ English and English$rightarrow$German translation tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2341–2354},
numpages = {14}
}

@article{10.1109/TASLP.2018.2842432,
author = {Shen, Shi-qi and Chen, Yun and Yang, Cheng and Liu, Zhi-yuan and Sun, Mao-song},
title = {Zero-Shot Cross-Lingual Neural Headline Generation},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2842432},
doi = {10.1109/TASLP.2018.2842432},
abstract = {Neural headline generation NHG has been proven to be effective in generating a fully abstractive headline recently. Existing NHG systems are only capable of producing headline of the same language as the original document. Cross lingual headline generation is an important task since it provides an efficient way to understand the key point of a document in a different language. Due to the lack of those parallel corpora of direct source language articles and target language headlines, we propose to deal with the cross-lingual neural headline generation CNHG under the zero-shot scenario. A trivial solution is to translate and summarize the source document in a pipeline way. However, a pipeline solution will lead to error propagation in the translation and summarization phases. This challenge motivates us to build a direct source-to-target CNHG model based on existing parallel corpora of translation and monolingual headline generation. Specifically, we let a parameterized CNHG model student model mimic the output of a pretrained translation or headline generation model teacher model. To the best of our knowledge, this is the first effort to address CNHG problem. Besides, we construct English–Chinese headline generation evaluation datasets by manual translation. Experimental results on English-to-Chinese cross-lingual headline generation demonstrate that our proposed method significantly outperforms the baseline models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2319–2327},
numpages = {9}
}

@article{10.1109/TASLP.2018.2860786,
author = {Salehi, Haniyeh and Suelzle, David and Folkeard, Paula and Parsa, Vijay},
title = {Learning-Based Reference-Free Speech Quality Measures for Hearing Aid Applications},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2860786},
doi = {10.1109/TASLP.2018.2860786},
abstract = {Objective measures of speech quality are highly desirable in benchmarking and monitoring the performance of hearing aids HAs. Existing HA speech quality indices such as the hearing aid speech quality index HASQI are intrusive in that they require a properly time-aligned and frequency-shaped reference signal to predict the quality of HA output. Two new reference-free HA speech quality indices are proposed in this paper, based on a model that amalgamates perceptual linear prediction PLP, hearing loss HL modeling, and machine learning concepts. For the first index, HL-modified PLP coefficients and their statistics were used as the feature set, which was subsequently mapped to the predicted quality scores using support vector regression SVR. For the second index, HL-impacted gammatone auditory filterbank energies and their second-order statistics constituted the feature set, which was again mapped using SVR. Two databases involving HA recordings were collected and utilized for the evaluation of the robustness and generalizability of the two indices. Experimental results showed that the index based on the gammatone filterbank energies not only correlated well with HA quality ratings by hearing impaired listeners, but also exhibited robust performance across different test conditions and was comparable to the full-reference HASQI performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2277–2288},
numpages = {12}
}

@article{10.1109/TASLP.2018.2867947,
author = {Elshamy, Samy and Madhu, Nilesh and Tirry, Wouter and Fingscheidt, Tim},
title = {DNN-Supported Speech Enhancement With Cepstral Estimation of Both Excitation and Envelope},
year = {2018},
issue_date = {December 2018},
publisher = {IEEE Press},
volume = {26},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2867947},
doi = {10.1109/TASLP.2018.2867947},
abstract = {In this paper, we propose and compare various techniques for the estimation of clean spectral envelopes in noisy conditions. The source-filter model of human speech production is employed in combination with a hidden Markov model and/or a deep neural network approach to estimate clean envelope-representing coefficients in the cepstral domain. The cepstral estimators for speech spectral envelope-based noise reduction are both evaluated alone and also in combination with the recently introduced cepstral excitation manipulation CEM technique for a priori SNR estimation in a noise reduction framework. Relative to the classical MMSE short time spectral amplitude estimator, we obtain more than 2 dB higher noise attenuation, and relative to our recent CEM technique still 0.5 dB more, in both cases maintaining the quality of the speech component and obtaining considerable SNR improvement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2460–2474},
numpages = {15}
}

@article{10.1109/TASLP.2018.2851157,
author = {Zhang, Jie and Heusdens, Richard and Hendriks, Richard Christian},
title = {Rate-Distributed Spatial Filtering Based Noise Reduction in Wireless Acoustic Sensor Networks},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851157},
doi = {10.1109/TASLP.2018.2851157},
abstract = {In wireless acoustic sensor networks WASNs, sensors typically have a limited energy budget as they are often battery-driven. Energy efficiency is, therefore, essential for the design of algorithms in WASNs. One way to reduce energy costs is to select only the sensors that are most informative, a problem known as sensor selection . In this way, only sensors that significantly contribute to the task at hand will be involved. In this paper, we consider a more general approach, which is based on rate-distributed spatial filtering. Depending on the distance over which a transmission takes place, the bit rate directly influences the energy consumption. We try to minimize the battery usage due to transmission, while constraining the noise reduction performance. This results in an efficient rate allocation strategy, which depends on the underlying signal statistics, as well as the distance from sensors to a fusion center FC. Through the utilization of a linearly constrained minimum variance beamformer, the problem is derived as a semidefinite program. Furthermore, we show that rate allocation is more general than sensor selection, and sensor selection can be seen as a special case of the presented rate-allocation solution, e.g., the best microphone subset can be determined by thresholding the rates. Finally, numerical simulations for estimating several target sources in a WASN demonstrate that the proposed method outperforms the sensor-selection-based approaches in terms of energy usage, and we find that the sensors close to the FC and point sources are allocated with higher rates.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2015–2026},
numpages = {12}
}

@article{10.1109/TASLP.2018.2852500,
author = {Heck, Michael and Sakti, Sakriani and Nakamura, Satoshi},
title = {Dirichlet Process Mixture of Mixtures Model for Unsupervised Subword Modeling},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2852500},
doi = {10.1109/TASLP.2018.2852500},
abstract = {We develop a parallelizable Markov chain Monte Carlo sampler for a Dirichlet process mixture of mixtures model. Our sampler jointly infers a codebook and clusters. The codebook is a global collection of components. Clusters are mixtures, defined over the codebook. We combine a nonergodic Gibbs sampler with two layers of split and merge samplers on codebook and mixture level to form a valid ergodic chain. We design an additional switch sampler for components that supports convergence in our experimental results. In the use case of unsupervised subword modeling, we show that our method infers complex classes from real speech feature vectors that consistently show higher quality on several evaluation metrics. At the same time, we infer fewer classes that represent subword units more consistently and show longer durations, compared to a standard Dirichlet process mixture model sampler.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2027–2042},
numpages = {16}
}

@article{10.1109/TASLP.2018.2856625,
author = {Xu, Jingjing and He, Hangfeng and Sun, Xu and Ren, Xuancheng and Li, Sujian},
title = {Cross-Domain and Semisupervised Named Entity Recognition in Chinese Social Media: A Unified Model},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2856625},
doi = {10.1109/TASLP.2018.2856625},
abstract = {Named entity recognition NER in Chinese social media is an important, but challenging task because Chinese social media language is informal and noisy. Most previous methods on NER focus on in-domain supervised learning, which is limited by scarce annotated data in social media. In this paper, we present that sufficient corpora in formal domains and massive unannotated text can be combined to improve the NER performance in social media. We propose a unified model which can learn from out-of-domain corpora and in-domain unannotated text. The unified model is composed of two parts. One is for cross-domain learning and the other is for semisupervised learning. Cross-domain learning can learn out-of-domain information based on domain similarity. Semisupervised learning can learn in-domain unannotated information by self-training. Experimental results show that our unified model yields a 9.57% improvement over strong baselines and achieves the state-of-the-art performance.1},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2142–2152},
numpages = {11}
}

@article{10.1109/TASLP.2018.2851664,
author = {Weisz, Gellert and Budzianowski, Pawel and Su, Pei-Hao and Gasic, Milica},
title = {Sample Efficient Deep Reinforcement Learning for Dialogue Systems With Large Action Spaces},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851664},
doi = {10.1109/TASLP.2018.2851664},
abstract = {In spoken dialogue systems, we aim to deploy artificial intelligence to build automated dialogue agents that can converse with humans. A part of this effort is the policy optimization task, which attempts to find a policy describing how to respond to humans, in the form of a function taking the current state of the dialogue and returning the response of the system. In this paper, we investigate deep reinforcement learning approaches to solve this problem. Particular attention is given to actor-critic methods, off-policy reinforcement learning with experience replay, and various methods aimed at reducing the bias and variance of estimators. When combined, these methods result in the previously proposed ACER algorithm that gave competitive results in gaming environments. These environments, however, are fully observable and have a relatively small action set so, in this paper, we examine the application of ACER to dialogue policy optimization. We show that this method beats the current state of the art in deep learning approaches for spoken dialogue systems. This not only leads to a more sample efficient algorithm that can train faster, but also allows us to apply the algorithm in more difficult environments than before. We thus experiment with learning in a very large action space, which has two orders of magnitude more actions than previously considered. We find that ACER trains significantly faster than the current state of the art.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2083–2097},
numpages = {15}
}

@article{10.1109/TASLP.2018.2855968,
author = {Wu, Shuangzhi and Zhang, Dongdong and Zhang, Zhirui and Yang, Nan and Li, Mu and Zhou, Ming},
title = {Dependency-to-Dependency Neural Machine Translation},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2855968},
doi = {10.1109/TASLP.2018.2855968},
abstract = {Recent research has proven that syntactic knowledge is effective to improve the performance of neural machine translation NMT. Most previous work focuses on leveraging either source or target syntax in the recurrent neural network RNN based encoder–decoder model. In this paper, we simultaneously use both source and target dependency tree to improve the NMT model. First, we propose a simple but effective syntax-aware encoder to incorporate source dependency tree into NMT. The new encoder enriches each source state with dependence relations from the tree. Then, we propose a novel sequence-to-dependence framework. In this framework, the target translation and its corresponding dependence tree are jointly constructed and modeled. During decoding, the tree structure is used as context to facilitate word generations. Finally, we extend the sequence-to-dependence framework with the syntax-aware encoder to build a dependence-NMT model and apply the dependence-based framework to the Transformer. Experimental results on several translation tasks show that both source and target dependence structures can improve the translation quality and their effects can be accumulated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2132–2141},
numpages = {10}
}

@article{10.1109/TASLP.2018.2854871,
author = {Lin, Shoufeng},
title = {Reverberation-Robust Localization of Speakers Using Distinct Speech Onsets and Multichannel Cross Correlations},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2854871},
doi = {10.1109/TASLP.2018.2854871},
abstract = {Many speaker localization methods can be found in the literature. However, speaker localization under strong reverberation still remains a major challenge in the real-world applications. This paper proposes two algorithms for localizing speakers using microphone array recordings of reverberated sounds. To separate concurrent speakers, the first algorithm decomposes microphone signals spectrotemporally into subbands via an auditory filterbank. To suppress reverberation, we propose a novel speech onset detection approach derived from the speech signal and impulse response models, and further propose to formulate the multichannel cross-correlation coefficient of encoded speech onsets in each subband. The subband results are combined to estimate the directions-of-arrival of speakers. The second algorithm extends the generalized cross-correlation phase transform method by using redundant information of multiple microphones to address the reverberation problem. The proposed methods have been evaluated under adverse conditions using not only simulated signals reverberation time $T_{60}$ of up to $1$s but also recordings in a real reverberant room $T_{60} approx 0.65$s. Comparing with some state-of-the-art localization methods, experimental results confirm that the proposed methods can reliably locate static and moving speakers, in presence of reverberation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2098–2111},
numpages = {14}
}

@article{10.1109/TASLP.2018.2848701,
author = {Hadian, Hossein and Sameti, Hossein and Povey, Daniel and Khudanpur, Sanjeev},
title = {Flat-Start Single-Stage Discriminatively Trained HMM-Based Models for ASR},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2848701},
doi = {10.1109/TASLP.2018.2848701},
abstract = {In recent years, end-to-end approaches to automatic speech recognition have received considerable attention as they are much faster in terms of preparing resources. However, conventional multistage approaches, which rely on a pipeline of training hidden Markov models HMM-GMM models and tree-building steps still give the state-of-the-art results on most databases. In this study, we investigate flat-start one-stage training of neural networks using lattice-free maximum mutual information LF-MMI objective function with HMM for large vocabulary continuous speech recognition. We thoroughly look into different issues that arise in such a setup and propose a standalone system, which achieves word error rates WER comparable with that of the state-of-the-art multi-stage systems while being much faster to prepare. We propose to use full biphones to enable flat-start context-dependent CD modeling and show through experiments that our CD modeling approach can be almost as effective as regular tree-based CD modeling. We show that our flat-start LF-MMI setup together with this tree-free CD modeling technique achieves 10 to 25 % relative WER reduction compared to other end-to-end methods on well-known databases. The improvements are larger for smaller databases.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1949–1961},
numpages = {13}
}

@article{10.1109/TASLP.2018.2851151,
author = {Nie, Shuai and Liang, Shan and Liu, Wenju and Zhang, Xueliang and Tao, Jianhua},
title = {Deep Learning Based Speech Separation via NMF-Style Reconstructions},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851151},
doi = {10.1109/TASLP.2018.2851151},
abstract = {Deep learning based speech separation usually uses a supervised algorithm to learn a mapping function from noisy features to separation targets. These separation targets, either ideal masks or magnitude spectrograms, have prominent spectro-temporal structures. Nonnegative matrix factorization NMF is a well-known representation learning technique that is capable of capturing the basic spectral structures. Therefore, the combination of deep learning and NMF as an organic whole is a smart strategy. However, previous methods typically use deep neural networks DNN and NMF for speech separation in a separate manner. In this paper, we propose a jointly combinatorial scheme to concentrate the strengths of both DNN and NMF for speech separation. NMF is used to learn the basis spectra that then are integrated into a DNN to directly reconstruct the magnitude spectrograms of speech and noise. Instead of predicting activation coefficients inferred by NMF, which is used as an intermediate target by the previous methods, DNN directly optimizes an actual separation objective in our system, so that the accumulated errors could be alleviated. Moreover, we explore a discriminative training objective with sparsity constraints to suppress noise and preserve more speech components further. Systematic experiments show that the proposed models are competitive with the previous methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2043–2055},
numpages = {13}
}

@article{10.1109/TASLP.2018.2858559,
author = {McFee, Brian and Salamon, Justin and Bello, Juan Pablo},
title = {Adaptive Pooling Operators for Weakly Labeled Sound Event Detection},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2858559},
doi = {10.1109/TASLP.2018.2858559},
abstract = {Sound event detection SED methods are tasked with labeling segments of audio recordings by the presence of active sound sources. SED is typically posed as a supervised machine learning problem, requiring strong annotations for the presence or absence of each sound source at every time instant within the recording. However, strong annotations of this type are both labor- and cost-intensive for human annotators to produce, which limits the practical scalability of SED methods. In this paper, we treat SED as a multiple instance learning MIL problem, where training labels are static over a short excerpt, indicating the presence or absence of sound sources but not their temporal locality. The models, however, must still produce temporally dynamic predictions, which must be aggregated pooled when comparing against static labels during training. To facilitate this aggregation, we develop a family of adaptive pooling operators—referred to as autopool—which smoothly interpolate between common pooling operators, such as min-, max-, or average-pooling, and automatically adapt to the characteristics of the sound sources in question. We evaluate the proposed pooling operators on three datasets, and demonstrate that in each case, the proposed methods outperform nonadaptive pooling operators for static prediction, and nearly match the performance of models trained with strong, dynamic annotations. The proposed method is evaluated in conjunction with convolutional neural networks, but can be readily applied to any differentiable model for time-series label prediction. While this paper focuses on SED applications, the proposed methods are general, and could be applied widely to MIL problems in any domain.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2180–2193},
numpages = {14}
}

@article{10.1109/TASLP.2018.2851144,
author = {Katzberg, Fabrice and Mazur, Radoslaw and Maass, Marco and Koch, Philipp and Mertins, Alfred},
title = {A Compressed Sensing Framework for Dynamic Sound-Field Measurements},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851144},
doi = {10.1109/TASLP.2018.2851144},
abstract = {The conventional sampling of sound fields by use of stationary microphones is impractical for large bandwidths. Satisfying the Nyquist–Shannon sampling theorem in three-dimensional space requires a huge number of sampling positions. Dynamic sound-field measurements with moving microphones together with a compressed-sensing recovery allow for weakening the spatial sampling problem. For bandlimited signals, the dynamic samples taken along the microphone trajectory may be related to the room impulse responses on a virtual grid in space via spatial interpolation. The tracking of the microphone positions and the knowledge of the excitation sequence allow for setting up a linear system of equations that can be solved for the room impulse responses on the modeled virtual grid. Nevertheless, there is still the necessity for recovering a huge number of sound-field variables, in order to ensure aliasing-free reconstruction. Thus, for practical applications, random or suboptimally chosen trajectories may be expected to lead to underdetermined sampling problems for a given volume of interest. In this paper, we present a compressed sensing framework that enables us to uniquely solve the dynamic sampling problem despite having underdetermined variables. The spatio-temporal sampling problem is integrated into compressed sensing models that allow for stable and robust sub-Nyquist sampling given incoherent measurements. For a modeled equidistant grid and sparse Fourier representations, the influence of the microphone trajectories on the compressed sensing problem is investigated and a simple expression is derived for evaluating trajectories with regard to compressed-sensing based recovery.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1962–1975},
numpages = {14}
}

@article{10.1109/TASLP.2018.2851147,
author = {Sundar, Harshavardhan and Sreenivas, Thippur V. and Seelamantula, Chandra Sekhar},
title = {TDOA-Based Multiple Acoustic Source Localization Without Association Ambiguity},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851147},
doi = {10.1109/TASLP.2018.2851147},
abstract = {Multiple source localization using time-differences of arrival TDOAs is challenging because of the ambiguity involved in associating the TDOAs computed across microphone pairs to the sources. We show that the association ambiguity of the TDOAs can be effectively resolved using the concept of an inverse delay interval region IDIR, which we introduce in this paper. By examining the association between a spatial domain and the TDOAs, we define IDIR as an interhyperboloidal spatial region corresponding to an interval of delays for a given pair of microphones. The proposed scheme for localizing multiple sources involves two stages. In the first stage, the given enclosure is partitioned into nonoverlapping elemental regions and the ones that contain a source are detected using a measure based on the generalized cross-correlation with phase transform and the IDIRs. In the second stage, the sources are finely localized within each of the detected elemental regions by identifying the IDIRs containing a single source and a novel region-constrained localization approach. We evaluate the performance of the proposed approach on real recordings from the AV16.3 corpus and in a simulated reverberation setting with a reverberation time RT60 of up to 500 ms, and show that the DOA estimation error with two active speakers is within 2° and the spatial localization error is less than 30 cm for each speaker.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1976–1990},
numpages = {15}
}

@article{10.1109/TASLP.2018.2855960,
author = {Ma, Ning and Gonzalez, Jose A. and Brown, Guy J.},
title = {Robust Binaural Localization of a Target Sound Source by Combining Spectral Source Models and Deep Neural Networks},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2855960},
doi = {10.1109/TASLP.2018.2855960},
abstract = {Despite there being a clear evidence for top–down e.g., attentional effects in biological spatial hearing, relatively few machine hearing systems exploit the top–down model-based knowledge in sound localization. This paper addresses this issue by proposing a novel framework for the binaural sound localization that combines the model-based information about the spectral characteristics of sound sources and deep neural networks DNNs. A target source model and a background source model are first estimated during a training phase using spectral features extracted from sound signals in isolation. When the identity of the background source is not available, a universal background model can be used. During testing, the source models are used jointly to explain the mixed observations and improve the localization process by selectively weighting source azimuth posteriors output by a DNN-based localization system. To address the possible mismatch between the training and testing, a model adaptation process is further employed the on-the-fly during testing, which adapts the background model parameters directly from the noisy observations in an iterative manner. The proposed system, therefore, combines the model-based and data-driven information flow within a single computational framework. The evaluation task involved localization of a target speech source in the presence of an interfering source and room reverberation. Our experiments show that by exploiting the model-based information in this way, the sound localization performance can be improved substantially under various noisy and reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2122–2131},
numpages = {10}
}

@article{10.1109/TASLP.2018.2856374,
author = {Van Kuyk, Steven and Kleijn, W. Bastiaan and Hendriks, Richard Christian},
title = {An Evaluation of Intrusive Instrumental Intelligibility Metrics},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2856374},
doi = {10.1109/TASLP.2018.2856374},
abstract = {Instrumental intelligibility metrics are commonly used as an alternative to listening tests. This paper evaluates 12 monaural intrusive intelligibility metrics: SII, HEGP, CSII, HASPI, NCM, QSTI, STOI, ESTOI, MIKNN, SIMI, SIIB, and $text{sEPSM}^text{corr}$. In addition, this paper investigates the ability of intelligibility metrics to generalize to new types of distortions and analyzes why the top performing metrics have high performance. The intelligibility data were obtained from 11 listening tests described in the literature. The stimuli included Dutch, Danish, and English speech that was distorted by additive noise, reverberation, competing talkers, preprocessing enhancement, and postprocessing enhancement. SIIB and HASPI had the highest performance achieving a correlation with listening test scores on average of $bf rho =0.92$ and $bf rho =0.89$, respectively. The high performance of SIIB may, in part, be the result of SIIBs developers having access to all the intelligibility data considered in the evaluation. The results show that intelligibility metrics tend to perform poorly on datasets that were not used during their development. By modifying the original implementations of SIIB and STOI, the advantage of reducing statistical dependencies between input features is demonstrated. Additionally, this paper presents a new version of SIIB called $text{SIIB}^text{Gauss}$, which has similar performance to SIIB and HASPI, but takes less time to compute by two orders of magnitude.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2153–2166},
numpages = {14}
}

@article{10.1109/TASLP.2018.2848698,
author = {Dubey, Harishchandra and Sangwan, Abhijeet and Hansen, John H. L.},
title = {Leveraging Frequency-Dependent Kernel and DIP-Based Clustering for Robust Speech Activity Detection in Naturalistic Audio Streams},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2848698},
doi = {10.1109/TASLP.2018.2848698},
abstract = {Speech activity detection SAD is front-end in most speech systems, e.g., speaker verification, speech recognition etc. Supervised SAD typically leverages machine learning models trained on annotated data. For applications like zero-resource speech processing and NIST-OpenSAT-2017 public safety communications task, it might not be feasible to collect SAD annotations. SAD is challenging for naturalistic audio streams containing multiple noise-sources simultaneously. We propose a novel frequency-dependent kernel FDK based SAD features. FDK provides enhanced spectral decomposition from which several statistical descriptors are derived. FDK statistical descriptors are combined by principal component analysis into one-dimensional FDK-SAD features. We further proposed two decision backends: First, variable model-size Gaussian mixture model VMGMM; and second, Hartigan dip-based robust feature clustering. While VMGMM is a model-based approach, the DipSAD is nonparametric. We used both backends for comparative evaluations in two phases: first, standalone SAD performance; and second, the effect of SAD on text-dependent speaker verification using RedDots data. The NIST-OpenSAD-2015 and NIST-OpenSAT-2017 corpora are used for standalone SAD evaluations. We establish two Center for Robust Speech Systems CRSS corpora namely CRSS-PLTL-II and CRSS long-duration naturalistic noise corpus. The CRSS corpora facilitate standalone SAD evaluations on naturalistic audio streams. We performed comparative studies of the proposed approaches with multiple baselines including SohnSAD, rSAD, semisupervised Gaussian mixture model, and Gammatone spectrogram features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2056–2071},
numpages = {16}
}

@article{10.1109/TASLP.2018.2852502,
author = {Ouyang, Xi and Gu, Kang and Zhou, Pan},
title = {Spatial Pyramid Pooling Mechanism in 3D Convolutional Network for Sentence-Level Classification},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2852502},
doi = {10.1109/TASLP.2018.2852502},
abstract = {In this paper, we investigate the usage of the convolutional neural network CNN to propose a novel end-to-end language processing structure to model textual data for this task. In particular, we propose a 3D CNN structure for the task, which is featured by spatial pyramid pooling SPP. To our knowledge, it is the first time that 3D convolution and SPP structure are applied together in language processing issues. Compared with methods of 2D CNNs, the proposed method can effectively and efficiently capture the complicated internal relations in sentences. Furthermore, in previous work, the issue of sentence length variety is usually addressed by padding zero to make all sentences vectors to a fixed length, which causes too much redundant and useless noise. Inspired by the SPP structure for object detection in image processing, this issue can be well handled with the SPP, which divides the sentences into several length sections for respective pooling processing. Experiments are conducted for the task of sentence classification as well as relation classification. Experiments on Stanford Treebank, TREC, subj, and Yelp datasets demonstrate that our proposed method can outperform other state-of-the-art models, with respect to classification accuracy. Auxiliary attempts to leverage our method to SemEval-2010 Task 8 dataset further substantiate the model's capability of extracting features efficiently.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2167–2179},
numpages = {13}
}

@article{10.1109/TASLP.2018.2851155,
author = {Dinkel, Heinrich and Qian, Yanmin and Yu, Kai},
title = {Investigating Raw Wave Deep Neural Networks for End-to-End Speaker Spoofing Detection},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851155},
doi = {10.1109/TASLP.2018.2851155},
abstract = {Recent advances in automatic speaker verification ASV lead to an increased interest in securing these systems for real-world applications. Malicious spoofing attempts against ASV systems can lead to serious security breaches. A spoofing attack within the context of ASV is a condition in which a potentially harmful person successfully masks as another, to the ASV system already known person by falsifying or manipulating data. While most previous work focuses on enhanced, spoof-aware features, end-to-end models can be a potential alternative. In this paper, we investigate the training of a raw wave front-ends for deep convolutional, long short-term memory LSTM and vanilla neural networks, which are analyzed for their suitability toward spoofing detection, regarding the influence of frame size, number of output neurons, and sequence length. A joint convolutional LSTM neural network CLDNN is proposed, which outperforms previous attempts on the BTAS2016 dataset 0.82% $rightarrow$ 0.19% HTER, placing itself as the current state-of-the-art model for the dataset. We show that end-to-end approaches are appropriate for the important replay detection task and show that the proposed model is capable of distinguishing device-invariant spoofing attempts. Regarding the ASVspoof2015 dataset, the end-to-end solution achieves an equal error rate EER of 0.00% for the S1-S9 conditions. We show that the end-to-end approach based on a raw waveform input can outperform common cepstral features, without the use of context-dependent frame extensions. In addition, a cross-database domain mismatch scenario is also evaluated, which shows that the proposed CLDNN model trained on the BTAS2016 dataset achieves an EER of 25.7% on the ASVspoof2015 dataset.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2002–2014},
numpages = {13}
}

@article{10.1109/TASLP.2018.2858932,
author = {Coteli, Mert Burkay and Olgun, Orhun and Hacihabiboglu, Huseyin},
title = {Multiple Sound Source Localization With Steered Response Power Density and Hierarchical Grid Refinement},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2858932},
doi = {10.1109/TASLP.2018.2858932},
abstract = {Estimation of the direction-of-arrival DOA of sound sources is an important step in sound field analysis. Rigid spherical microphone arrays allow the calculation of a compact spherical harmonic representation of the sound field. The standard method for analyzing sound fields recorded using such arrays is steered response power SRP maps wherein the source DOA can be estimated as the steering direction that maximizes the output power of a maximally directive beam. This approach is computationally costly since it requires steering the beam in all possible directions. This paper presents an extension to SRP called steered response power density SRPD and an associated, signal-adaptive search method called hierarchical grid refinement for reducing the number of steering directions needed for DOA estimation. The proposed method can localize near-coherent as well as incoherent sources while jointly providing the number of prominent sources in the scene. It is shown to be robust to reverberation and additive white noise. An evaluation of the proposed method using simulations and real recordings under highly reverberant conditions as well as a comparison with the state-of-the-art methods are presented.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2215–2229},
numpages = {15}
}

@article{10.1109/TASLP.2018.2851145,
author = {Sahraeian, Reza and Van Compernolle, Dirk},
title = {Cross-Entropy Training of DNN Ensemble Acoustic Models for Low-Resource ASR},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2851145},
doi = {10.1109/TASLP.2018.2851145},
abstract = {Deep neural networks DNNs have shown a great promise in exploiting out-of-language data, particularly for under-resourced languages. The common trend is to merge data from various source languages to train a multilingual DNN and then reuse the hidden layers as language-independent feature extractors for a low-resource target language. While there is a consensus that using as much data from various languages results in a better and more general multilingual DNN, employing only source languages similar to the target language has proven effective. In this study, we propose a novel framework for multilingual DNN training, which employs all the available training data and exploits complementary information from individual source languages at the same time. Toward this goal, we borrow the idea of an ensemble with one generalist and many specialists. The generalist is derived from a multilingual DNN acoustic model trained on all available multilingual data; the specialists are the DNNs derived from the source languages individually. Then, the constituents in the ensemble are combined using weighted averaging schemes, where the combination weights are trained to minimize the cross-entropy objective function. In this framework, we seek for complementary information among the constituents while it is possible to get at least the performance equal to the baseline. Moreover, unlike previous well-known system combination schemes, only one model is required during decoding. We successfully examined two combination methodologies and demonstrated their usefulness in different scenarios using the multilingual GlobalPhone dataset. It is observed that, specifically, speech recognition systems developed in low-resource settings profit from the proposed strategy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1991–2001},
numpages = {11}
}

@article{10.1109/TASLP.2018.2854861,
author = {Abidin, Shamsiah and Togneri, Roberto and Sohel, Ferdous},
title = {Spectrotemporal Analysis Using Local Binary Pattern Variants for Acoustic Scene Classification},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2854861},
doi = {10.1109/TASLP.2018.2854861},
abstract = {In this paper, we present an approach for acoustic scene classification, which aggregates spectral and temporal features. We do this by proposing the first use of the variable-Q transform VQT to generate the time–frequency representation for acoustic scene classification. The VQT provides finer control over the resolution compared to the constant-Q transform CQT or short time fourier transform and can be tuned to better capture acoustic scene information. We then adopt a variant of the local binary pattern LBP, the adjacent evaluation completed LBP AECLBP, which is better suited to extracting features from acoustic time–frequency images. Our results yield a 5.2% improvement on the DCASE 2016 dataset compared to the application of standard CQT with LBP. Fusing our proposed AECLBP with HOG features, we achieve a classification accuracy of 85.5%, which outperforms one of the top performing systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2112–2121},
numpages = {10}
}

@article{10.1109/TASLP.2018.2858538,
author = {Barbancho, Isabel and Tzanetakis, George and Barbancho, Ana M. and Tardon, Lorenzo J.},
title = {Discrimination Between Ascending/Descending Pitch Arpeggios},
year = {2018},
issue_date = {November 2018},
publisher = {IEEE Press},
volume = {26},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2018.2858538},
doi = {10.1109/TASLP.2018.2858538},
abstract = {Automatic music transcription can be defined as the analysis of the acoustic signal to extract a symbolic representation of music. Existing transcription systems typically consider just the notes played at a given moment; however, other aspects such as expressiveness and playing technique can also be considered. This work is focused on how chords are played. Specifically, we consider a special type of chords, those played in arpeggio style, or simply arpeggios, in which the notes are played fast, sequentially from the lowest to the highest pitched note or vice versa and with a large overlap of the notes’ sound. The main goal of this paper is to determine the pitch direction in which the arpeggiated chord was played. Two different classification methods are considered: a Fisher linear discriminant and an SVM linear classification scheme. Different features are presented for this task: one is based on the Mel-frequency cepstral coefficients MFCCs and two others, specifically designed for this task, rely on different analyses of the spectrogram. Evaluations have been done with a wide number of musical instruments. The results show that the pitch direction can be reliably detected using the

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repo_name: goyalkaraniit/acm-cr