text:
@article{10.1109/TASLP.2019.2955290,
author = {Gu, Jia-Chen and Ling, Zhen-Hua and Liu, Quan},
title = {Utterance-to-Utterance Interactive Matching Network for Multi-Turn Response Selection in Retrieval-Based Chatbots},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955290},
doi = {10.1109/TASLP.2019.2955290},
abstract = {This article proposes an utterance-to-utterance interactive matching network (U2U-IMN) for multi-turn response selection in retrieval-based chatbots. Different from previous methods following context-to-response matching or utterance-to-response matching frameworks, this model treats both contexts and responses as sequences of utterances when calculating the matching degrees between them. For a context-response pair, the U2U-IMN model first encodes each utterance separately using recurrent and self-attention layers. Then, a global and bidirectional interaction between the context and the response is conducted using the attention mechanism to collect the matching information between them. The distances between context and response utterances are employed as a prior component when calculating the attention weights. Finally, sentence-level aggregation and context-response-level aggregation are executed in turn to obtain the feature vector for matching degree prediction. Experiments on four public datasets showed that our proposed method outperformed baseline methods on all metrics, achieving a new state-of-the-art performance and demonstrating compatibility across domains for multi-turn response selection.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {369–379},
numpages = {11}
}

@article{10.1109/TASLP.2020.2973795,
author = {Huang, Gongping and Benesty, Jacob and Cohen, Israel and Chen, Jingdong},
title = {Differential Beamforming on Graphs},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2973795},
doi = {10.1109/TASLP.2020.2973795},
abstract = {In this article, we study differential beamforming from a graph perspective. The microphone array used for differential beamforming is viewed as a graph, where its sensors correspond to the nodes, the number of microphones corresponds to the order of the graph, and linear spatial difference equations among microphones are related to graph edges. Specifically, for the first-order differential beamforming with an array of <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> microphones, each pair of adjacent microphones are directly connected, resulting in <inline-formula><tex-math notation="LaTeX">$M-1$</tex-math></inline-formula> spatial difference equations. On a graph, each of these equations corresponds to a 2-clique. For the second-order differential beamforming, each three adjacent microphones are directly connected, resulting in <inline-formula><tex-math notation="LaTeX">$M-2$</tex-math></inline-formula> second-order spatial difference equations, and each of these equations corresponds to a 3-clique. In an analogous manner, the differential microphone array for any order of differential beamforming can be viewed as a graph. From this perspective, we then derive a class of differential beamformers, including the maximum white noise gain beamformer, the maximum directivity factor one, and optimal compromising beamformers. Simulations are presented to demonstrate the performance of the derived differential beamformers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {901–913},
numpages = {13}
}

@article{10.1109/TASLP.2019.2962684,
author = {Maeno, Yu and Mitsufuji, Yuki and Samarasinghe, Prasanga N. and Murata, Naoki and Abhayapala, Thushara D.},
title = {Spherical-Harmonic-Domain Feedforward Active Noise Control Using Sparse Decomposition of Reference Signals from Distributed Sensor Arrays},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2962684},
doi = {10.1109/TASLP.2019.2962684},
abstract = {Active acoustic noise attenuation over a sizable space is a challenging problem in signal processing. The noise attenuation performance of feedforward active noise control (ANC) relies on the preciseness of a reference signal of a primary noise field. To capture the precise reference signal for controlling a sizable space, a large number of reference microphones are required, which reduces system viability. In this study, we exploit an efficient representation of the reference signal in spherical harmonic (SH) domain by utilizing the inherent sparseness of the noise field. The main contributions of this work are as follows. (1) A general reference microphone geometry can be used. The implementation difficulty in the array structure, which is recognized as the common issue of SH-domain signal processing, e.g., use of a fully surrounding spherical array, is reduced by using the fields translation based on the addition theorem. (2) The accuracy of low-frequency signal decomposition is improved. The low accuracy of low-frequency signal decomposition in compressive sensing (CS), which is commonly reported in the literature, is improved by applying signal representation in SH domain. (3) System robustness is increased. The robustness of the system is increased by considering a noise source spatial distribution of both the interior and exterior sound fields, which is not possible in the case of a general signal representation in SH domain. Experimental results indicate that the noise attenuation performance of our proposed method exceeds that of existing solutions. The flexibility of the array structure is also increased, which leads to a more feasible practical system setup.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {656–670},
numpages = {15}
}

@article{10.1109/TASLP.2019.2948765,
author = {Desiraju, Naveen Kumar and Doclo, Simon and Buck, Markus and Wolff, Tobias},
title = {Online Estimation of Reverberation Parameters For Late Residual Echo Suppression},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2948765},
doi = {10.1109/TASLP.2019.2948765},
abstract = {In hands-free telephony and other distant-talk applications, often a short AEC filter is used to achieve fast convergence at low computational cost. As a result, a significant amount of late residual echo (LRE) may remain, especially in highly reverberant environments. This LRE can be suppressed using a postfilter in the subband domain, which requires an estimate of the power spectral density (PSD) of the LRE. To estimate the LRE PSD, an exponentially decaying model with frequency-dependent reverberation scaling and decay parameters has frequently been assumed. State-of-the-art methods estimate both reverberation parameters independently of each other, either in offline or in online mode. In this article, we propose two signal-based methods (i.e. output error and equation error) to jointly estimate both reverberation parameters in online mode. The estimated parameters are then used to generate an estimate for the LRE PSD, which is fed into a postfilter for the purpose of late residual echo suppression. We derive several gradient-descent-based algorithms to simultaneously update both reverberation parameters, minimizing either the mean squared error or the mean squared log error cost function. The proposed methods are compared with state-of-the-art methods in terms of the accuracy of the estimated reverberation parameters and the corresponding LRE PSD estimate. Extensive simulation results using both artificial as well as measured room impulse responses show that the proposed output error method with mean squared log error minimization outperforms state-of-the-art methods in all considered scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {77–91},
numpages = {15}
}

@article{10.1109/TASLP.2019.2949219,
author = {Huang, Gongping and Chen, Jingdong and Benesty, Jacob},
title = {Design of Planar Differential Microphone Arrays With Fractional Orders},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2949219},
doi = {10.1109/TASLP.2019.2949219},
abstract = {Differential microphone arrays (DMAs) often encounter white noise amplification, especially at low frequencies. If the array geometry and the number of microphones are fixed, one can improve the white noise amplification problem by reducing the DMA order. With the existing differential beamforming methods, the DMA order can only be a positive integer number. Consequently, with a specified beampattern (or a kind of beampattern), reducing this order may easily lead to over compensation of the white noise gain (WNG) and too much reduction of the directivity factor (DF), which is not optimal. To deal with this problem, we present in this article a general approach to the design of DMAs with fractional orders. The major contributions of this article include but are not limited to: 1) we first define a directivity pattern that can achieve a continuous compromise between the pattern corresponding to the maximum DMA order and the omnidirectional pattern; 2) by approximating the beamformer's beampattern with the Jacobi-Anger expansion, we present a method to find the proper differential beamforming filter so that its beampattern matches closely the target directivity pattern of fractional orders; and 3) we show how to determine analytically the proper fractional order of the DMA with a given target beampattern when either the value of the DF or WNG is specified, which is useful in practice to achieve the desired beampattern and spatial gain while maintaining the robustness of the DMA system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {116–130},
numpages = {15}
}

@article{10.1109/TASLP.2019.2949925,
author = {Dai, Tao and Zhu, Li and Wang, Yaxiong and Carley, Kathleen M.},
title = {Attentive Stacked Denoising Autoencoder With Bi-LSTM for Personalized Context-Aware Citation Recommendation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2949925},
doi = {10.1109/TASLP.2019.2949925},
abstract = {The rapid growth of scientific publications brings the problem of finding appropriate citations for authors. Context-aware citation recommendation is an essential technology to overcome this obstacle when given a fragment of manuscript. In this article, we propose a novel neural network model for context-aware citation recommendation by combining stacked denoising autoencoders (SDAE) and Bi-LSTM. To obtain effective embedding for cited paper, we extend SDAE into attentive SDAE (ASDAE) by utilizing the attentive information from citation context, which essentially enhance the learning ability of original SDAE. For citation context, we devise an attentive Bi-LSTM to obtain effective embedding. Specifically, the attentive Bi-LSTM is able to extract suitable citation context and recommend citations simultaneously when given a long text, which is a issue that few papers addressed before. We also integrate personalized author information to improve the performance of recommendation. Our model is essentially a seemly integration of different types of neural network with latent variables. We derive the generative process of our model, and develop a learning algorithm based on maximum a posteriori (MAP) estimation. Experimental results on the RefSeer, ANN and DBLP datasets show that our model outperforms baseline methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {553–568},
numpages = {16}
}

@article{10.1109/TASLP.2019.2953000,
author = {Birnie, Lachlan I. and Abhayapala, Thushara D. and Samarasinghe, Prasanga N.},
title = {Reflection Assisted Sound Source Localization Through a Harmonic Domain MUSIC Framework},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2953000},
doi = {10.1109/TASLP.2019.2953000},
abstract = {This work presents a method that persuades acoustic reflections to be a favorable property for sound source localization. Whilst most real world spatial audio applications utilize prior knowledge of sound source position, estimating such positions in reverberant environments is still considered to be a difficult problem due to acoustic reflections. This article presents a novel MUSIC framework for multiple sound source localization (range, elevation, azimuth) in reverberant rooms by incorporating a recently proposed region-to-region room transfer model. The method is built upon the received signals of a higher order microphone and a spherical harmonic representation of the room transfer function. We demonstrate the method's general applicability and multiple source localization performance through a simulation study across an assortment of reverberant conditions. Additionally, we investigate robustness against various system modeling errors to gauge implementation viability. Finally, we prove the method in a practical experiment inside a real-world room with measured region-to-region transfer function parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {279–293},
numpages = {15}
}

@article{10.1109/TASLP.2019.2953350,
author = {Ding, Wenhao and He, Liang},
title = {Adaptive Multi-Scale Detection of Acoustic Events},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2953350},
doi = {10.1109/TASLP.2019.2953350},
abstract = {The goal of acoustic (or sound) events detection (AED or SED) is to predict the temporal position of target events in given audio segments. This task plays a significant role in safety monitoring, acoustic early warning and other scenarios. However, the deficiency of data and diversity of acoustic event sources make the AED task a tough issue, especially for prevalent data-driven methods. In this article, we start from analyzing acoustic events according to their time-frequency domain properties, showing that different acoustic events have different time-frequency scale characteristics. Inspired by the analysis, we propose an adaptive multi-scale detection (AdaMD) method. By taking advantage of hourglass neural network and gated recurrent unit (GRU) module, our AdaMD produces multiple predictions at different temporal and frequency resolutions. An adaptive training algorithm is subsequently adopted to combine multi-scale predictions to enhance the overall capability. Experimental results on Detection and Classification of Acoustic Scenes and Events 2017 (DCASE 2017) Task 2, DCASE 2016 Task 3 and DCASE 2017 Task 3 demonstrate that the AdaMD outperforms published state-of-the-art competitors in terms of the metrics of event error rate (ER) and F1-score. The verification experiment on our collected factory mechanical dataset also proves the noise-resistant capability of the AdaMD, providing the possibility for it to be deployed in the complex environment.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {294–306},
numpages = {13}
}

@article{10.1109/TASLP.2019.2947741,
author = {Eskimez, Sefik Emre and Maddox, Ross K. and Xu, Chenliang and Duan, Zhiyao},
title = {Noise-Resilient Training Method for Face Landmark Generation From Speech},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2947741},
doi = {10.1109/TASLP.2019.2947741},
abstract = {Visual cues such as lip movements, when available, play an important role in speech communication. They are especially helpful for the hearing impaired population or in noisy environments. When not available, having a system to automatically generate talking faces in sync with input speech would enhance speech communication and enable many novel applications. In this article, we present a new system that can generate 3D talking face landmarks from speech in an online fashion. We employ a neural network that accepts the raw waveform as an input. The network contains convolutional layers with 1D kernels and outputs the active shape model (ASM) coefficients of face landmarks. To promote smoother transitions between video frames, we present a variant of the model that has the same architecture but also accepts the previous frame's ASM coefficients as an additional input. To cope with background noise, we propose a new training method to incorporate speech enhancement ideas at the feature level. Objective evaluations on landmark prediction show that the proposed system yields statistically significantly smaller errors than two state-of-the-art baseline methods on both a single-speaker dataset and a multi-speaker dataset. Experiments on noisy speech input with five types of non-stationary unseen noise show statistically significant improvements of the system performance thanks to the noise-resilient training method. Finally, subjective evaluations show that the generated talking faces have a significantly more convincing match with the input audio, achieving a similarly convincing level of realism as the ground-truth landmarks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {27–38},
numpages = {12}
}

@article{10.1109/TASLP.2020.2986896,
author = {Taherian, Hassan and Wang, Zhong-Qiu and Chang, Jorge and Wang, DeLiang},
title = {Robust Speaker Recognition Based on Single-Channel and Multi-Channel Speech Enhancement},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2986896},
doi = {10.1109/TASLP.2020.2986896},
abstract = {Deep neural network (DNN) embeddings for speaker recognition have recently attracted much attention. Compared to i-vectors, they are more robust to noise and room reverberation as DNNs leverage large-scale training. This article addresses the question of whether speech enhancement approaches are still useful when DNN embeddings are used for speaker recognition. We investigate single- and multi-channel speech enhancement for text-independent speaker verification based on x-vectors in conditions where strong diffuse noise and reverberation are both present. Single-channel (monaural) speech enhancement is based on complex spectral mapping and is applied to individual microphones. We use masking-based minimum variance distortion-less response (MVDR) beamformer and its rank-1 approximation for multi-channel speech enhancement. We propose a novel method of deriving time-frequency masks from the estimated complex spectrogram. In addition, we investigate gammatone frequency cepstral coefficients (GFCCs) as robust speaker features. Systematic evaluations and comparisons on the NIST SRE 2010 retransmitted corpus show that both monaural and multi-channel speech enhancement significantly outperform x-vector's performance, and our covariance matrix estimate is effective for the MVDR beamformer.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1293–1302},
numpages = {10}
}

@article{10.1109/TASLP.2020.2983580,
author = {Padi, Bharat and Mohan, Anand and Ganapathy, Sriram},
title = {Towards Relevance and Sequence Modeling in Language Recognition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2983580},
doi = {10.1109/TASLP.2020.2983580},
abstract = {The task of automatic language identification (LID) involving multiple dialects of the same language family in the presence of noise is a challenging problem. In these scenarios, the identity of the language/dialect may be reliably present only in parts of the temporal sequence of the speech signal. The conventional approaches to LID (and for speaker recognition) ignore the sequence information by extracting long-term statistical summary of the recording assuming an independence of the feature frames. In this paper, we propose a neural network framework utilizing short-sequence information in language recognition. In particular, a new model is proposed for incorporating relevance in language recognition, where parts of speech data are weighted more based on their relevance for the language recognition task. This relevance weighting is achieved using the bidirectional long short-term memory (BLSTM) network with attention modeling. We explore two approaches, the first approach uses segment level i-vector/x-vector representations that are aggregated in the neural model and the second approach where the acoustic features are directly modeled in an end-to-end neural model. Experiments are performed using the language recognition task in NIST LRE 2017 Challenge using clean, noisy and multi-speaker speech data as well as in the RATS language recognition corpus. In these experiments on noisy LRE tasks as well as the RATS dataset, the proposed approach yields significant improvements over the conventional i-vector/x-vector based language recognition approaches as well as with other previous models incorporating sequence information.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1223–1232},
numpages = {10}
}

@article{10.1109/TASLP.2019.2957889,
author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Ngoc Q. K. and P\'{e}rez, Patrick and Richard, Ga\"{e}l},
title = {Weakly Supervised Representation Learning for Audio-Visual Scene Analysis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2957889},
doi = {10.1109/TASLP.2019.2957889},
abstract = {Audio-visual (AV) representation learning is an important task from the perspective of designing machines with the ability to understand complex events. To this end, we propose a novel multimodal framework that instantiates multiple instance learning. Specifically, we develop methods that identify events and localize corresponding AV cues in unconstrained videos. Importantly, this is done using weak labels where only video-level event labels are known without any information about their location in time. We show that the learnt representations are useful for performing several tasks such as event/object classification, audio event detection, audio source separation and visual object localization. An important feature of our method is its capacity to learn from unsynchronized audio-visual events. We also demonstrate our framework's ability to separate out the audio source of interest through a novel use of nonnegative matrix factorization. State-of-the-art classification results, with a F1-score of 65.0, are achieved on DCASE 2017 smart cars challenge data with promising generalization to diverse object types such as musical instruments. Visualizations of localized visual regions and audio segments substantiate our system's efficacy, especially when dealing with noisy situations where modality-specific cues appear asynchronously.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {416–428},
numpages = {13}
}

@article{10.1109/TASLP.2019.2955289,
author = {Ding, Shaojin and Zhao, Guanlong and Liberatore, Christopher and Gutierrez-Osuna, Ricardo},
title = {Learning Structured Sparse Representations for Voice Conversion},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955289},
doi = {10.1109/TASLP.2019.2955289},
abstract = {Sparse-coding techniques for voice conversion assume that an utterance can be decomposed into a sparse code that only carries linguistic contents, and a dictionary of atoms that captures the speakers’ characteristics. However, conventional dictionary-construction and sparse-coding algorithms rarely meet this assumption. The result is that the sparse code is no longer speaker-independent, which leads to lower voice-conversion performance. In this paper, we propose a Cluster-Structured Sparse Representation (CSSR) that improves the speaker independence of the representations. CSSR consists of two complementary components: a Cluster-Structured Dictionary Learning module that groups atoms in the dictionary into clusters, and a Cluster-Selective Objective Function that encourages each speech frame to be represented by atoms from a small number of clusters. We conducted four experiments on the CMU ARCTIC corpus to evaluate the proposed method. In a first ablation study, results show that each of the two CSSR components enhances speaker independence, and that combining both components leads to further improvements. In a second experiment, we find that CSSR uses increasingly larger dictionaries more efficiently than phoneme-based representations by allowing finer-grained decompositions of speech sounds. In a third experiment, results from objective and subjective measurements show that CSSR outperforms prior voice-conversion methods, improving the acoustic quality of the synthesized speech while retaining the target speaker's voice identity. Finally, we show that the CSSR captures latent (i.e., phonetic) information in the speech signal.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {343–354},
numpages = {12}
}

@article{10.1109/TASLP.2019.2955253,
author = {Sharma, Bidisha and Wang, Ye},
title = {Automatic Evaluation of Song Intelligibility Using Singing Adapted STOI and Vocal-Specific Features},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955253},
doi = {10.1109/TASLP.2019.2955253},
abstract = {An objective machine-driven measure of song intelligibility would be of great utility for various music information retrieval tasks. Song intelligibility mostly depends on two factors, the amount of interference caused by background accompaniment, and the quality of singing vocal. We leverage these two factors to determine the intelligibility of a song. For the first factor, we adapt a well known method for intelligibility prediction of noisy speech, short term objective intelligibility (STOI), to singing. The singing-adapted STOI considers the polyphonic song as a time-frequency weighted noisy version of the extracted singing vocal. We use U-net based audio source separation method to extract singing vocal from a polyphonic song. The singing vocal shares the same underlying physiological mechanism for production as that of speech, with some differences in the pronunciation and prosody of the phonemes. Therefore, for the second factor, we have introduced vocal-specific features to measure the intelligibility of the singing vocal, which are excitation source, spectral, and prosodic singing characteristics. We perform detailed analysis on each of these features to establish their efficacy for quantifying song intelligibility. We train a regression model to derive the intelligibility scores using a combination of the vocal-specific features and singing adapted STOI, obtaining a significant improvement in performance. The correlation between the intelligibility score obtained using proposed framework and human-rated intelligibility score is 0.81, which shows the efficacy of the proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {319–331},
numpages = {13}
}

@article{10.1109/TASLP.2020.2967539,
author = {Zhang, Mengfan and Ge, Zhongshu and Liu, Tiejun and Wu, Xihong and Qu, Tianshu},
title = {Modeling of Individual HRTFs Based on Spatial Principal Component Analysis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2967539},
doi = {10.1109/TASLP.2020.2967539},
abstract = {Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This article presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {785–797},
numpages = {13}
}

@article{10.1109/TASLP.2019.2958408,
author = {Trowitzsch, Ivo and Schymura, Christopher and Kolossa, Dorothea and Obermayer, Klaus},
title = {Joining Sound Event Detection and Localization Through Spatial Segregation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2958408},
doi = {10.1109/TASLP.2019.2958408},
abstract = {Identification and localization of sounds are both integral parts of computational auditory scene analysis. Although each can be solved separately, the goal of forming coherent auditory objects and achieving a comprehensive spatial scene understanding suggests pursuing a joint solution of the two problems. This article presents an approach that robustly binds localization with the detection of sound events in a binaural robotic system. Both tasks are joined through the use of spatial stream segregation which produces probabilistic time-frequency masks for individual sources attributable to separate locations, enabling segregated sound event detection operating on these streams. We use simulations of a comprehensive suite of test scenes with multiple co-occurring sound sources, and propose performance measures for systematic investigation of the impact of scene complexity on this segregated detection of sound types. Analyzing the effect of spatial scene arrangement, we show how a robot could facilitate high performance through optimal head rotation. Furthermore, we investigate the performance of segregated detection given possible localization error as well as error in the estimation of number of active sources. Our analysis demonstrates that the proposed approach is an effective method to obtain joint sound event location and type information under a wide range of conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {487–502},
numpages = {16}
}

@article{10.1109/TASLP.2019.2949709,
author = {Emura, Satoru},
title = {Wave-Domain Residual Echo Reduction Using Subspace Tracking},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2949709},
doi = {10.1109/TASLP.2019.2949709},
abstract = {This article proposes a wave-domain residual echo reduction method for two-way immersive sound communication, which is based on wave field synthesis and uses uniform linear arrays of loudspeakers and microphones. This method is intended to improve the echo reduction performance in a mildly reverberant room with moderate increase in computational complexity. For improving such performance, this method takes residual echo due to reflected waves into account and uses a multi-tap echo-path model. To mitigate the increase in the method's computational complexity, it uses a size-reduced transfer function (TF) matrix instead of the full TF matrix. Specifically, this method extracts the dominant components of the loudspeaker signals adaptively by subspace tracking and estimates a size-reduced TF matrix that relates these dominant components to the residual echo. Simulation showed that the proposed method plus a wave-domain acoustic echo canceller (AEC) improved echo return loss enhancement by 10 dB compared to the conventional wave-domain echo reduction method plus the wave-domain AEC in a room with a reverberation time 180 ms. Its total computational complexity is twice that of the conventional echo reduction method plus a wave-domain AEC.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {144–156},
numpages = {13}
}

@article{10.1109/TASLP.2020.2964427,
author = {Zhou, Qingyu and Yang, Nan and Wei, Furu and Huang, Shaohan and Zhou, Ming and Zhao, Tiejun},
title = {A Joint Sentence Scoring and Selection Framework for Neural Extractive Document Summarization},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2964427},
doi = {10.1109/TASLP.2020.2964427},
abstract = {Extractive document summarization methods aim to extract important sentences to form a summary. Previous works perform this task by first scoring all sentences in the document then selecting most informative ones; while we propose to jointly learn the two steps with a novel end-to-end neural network framework. Specifically, the sentences in the input document are represented as real-valued vectors through a neural document encoder. Then the method builds the output summary by extracting important sentences one by one. Different from previous works, the proposed joint sentence scoring and selection framework directly predicts the relative sentence importance score according to both sentence content and previously selected sentences. We evaluate the proposed framework with two realizations: a hierarchical recurrent neural network based model; and a pre-training based model that uses BERT as the document encoder. Experiments on two datasets show that the proposed joint framework outperforms the state-of-the-art extractive summarization models which treat sentence scoring and selection as two subtasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {671–681},
numpages = {11}
}

@article{10.1109/TASLP.2020.2982287,
author = {Buchris, Yaakov and Cohen, Israel and Benesty, Jacob and Amar, Alon},
title = {Joint Sparse Concentric Array Design for Frequency and Rotationally Invariant Beampattern},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982287},
doi = {10.1109/TASLP.2020.2982287},
abstract = {Frequency-invariant concentric arrays are fundamental components in some real-world applications, like teleconferencing, voice service devices, underwater acoustics, and others, where the azimuthal arrival direction of the desired signal is varying. The fact that the demand for limited hardware and computational resources in such applications is essential, motivates the use of a sparse design which can optimize both the number of the required sensors and the complex weights of the beamformer. Herein, we propose a new greedy based joint-sparse design of frequency and rotationally invariant concentric arrays which preserves the properties of the designed directivity pattern for different azimuthal directions of steering. Simulation results show that the greedy sparse design, compared to uniform and random designs, gives superior performance in terms of array gain, and frequency and rotationally invariant beampattern, with a reasonable computational and hardware resources.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1143–1158},
numpages = {16}
}

@article{10.1109/TASLP.2020.2977776,
author = {Tjandra, Andros and Sakti, Sakriani and Nakamura, Satoshi},
title = {Machine Speech Chain},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2977776},
doi = {10.1109/TASLP.2020.2977776},
abstract = {Despite the close relationship between speech perception and production, research in automatic speech recognition (ASR) and text-to-speech synthesis (TTS) has progressed more or less independently without exerting much mutual influence. In human communication, on the other hand, a closed-loop speech chain mechanism with auditory feedback from the speaker's mouth to her ear is crucial. In this paper, we take a step further and develop a closed-loop machine speech chain model based on deep learning. The sequence-to-sequence model in closed-loop architecture allows us to train our model on the concatenation of both labeled and unlabeled data. While ASR transcribes the unlabeled speech features, TTS attempts to reconstruct the original speech waveform based on the text from ASR. In the opposite direction, ASR also attempts to reconstruct the original text transcription given the synthesized speech. To the best of our knowledge, this is the first deep learning framework that integrates human speech perception and production behaviors. Our experimental results show that the proposed approach significantly improved performance over that from separate systems that were only trained with labeled data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {976–989},
numpages = {14}
}

@article{10.1109/TASLP.2019.2950602,
author = {Su, Rongfeng and Liu, Xunying and Wang, Lan and Yang, Jingzhou},
title = {Cross-Domain Deep Visual Feature Generation for Mandarin Audio–Visual Speech Recognition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2950602},
doi = {10.1109/TASLP.2019.2950602},
abstract = {There has been a long term interest in using visual information to improve automatic speech recognition (ASR) system performance. Both audio and visual information are required in conventional audio visual speech recognition (AVSR) systems. This limits their wider applications when visual modality is not present. To this end, one possible solution is to use acoustic-to-visual (A2V) inversion techniques to generate visual features. Previous research in this direction used synthetic acoustic-articulatory parallel data in inversion model training. The acoustic mismatch between the audio-visual (AV) parallel data and target data was not considered. In addition, the target language to apply these technologies has been focused on English. In this article, a real 3D Audio-Visual Mandarin Continuous Speech (3DAV-MCS) corpus was used to train deep neural network based A2V inversion models. Cross-domain adaptation of the inversion models allows suitable visual features to be generated from acoustic data of mismatched domains. The proposed cross-domain deep visual feature generation techniques were evaluated on two state-of-the-art Mandarin speech recognition tasks: DAPRA GALE broadcast transcription and BOLT conversational telephone speech recognition. The AVSR systems constructed using the cross-domain generated visual features consistently outperformed the baseline convolutional neural network (CNN) ASR systems by up to 3.3% absolute (9.1% relative) character error rate (CER) reductions after both speaker adaptive training and sequence discriminative training were performed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {185–197},
numpages = {13}
}

@article{10.1109/TASLP.2019.2948794,
author = {Laufer, Yaron and Gannot, Sharon},
title = {Scoring-Based ML Estimation and CRBs for Reverberation, Speech, and Noise PSDs in a Spatially Homogeneous Noise Field},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2948794},
doi = {10.1109/TASLP.2019.2948794},
abstract = {Hands-free speech systems are subject to performance degradation due to reverberation and noise. Common methods for enhancing reverberant and noisy speech require the knowledge of the speech, reverberation and noise power spectral densities (PSDs). Most literature on this topic assumes that the noise power spectral density (PSD) matrix is known. However, in many practical acoustic scenarios, the noise PSD is unknown and should be estimated along with the speech and the reverberation PSDs. In this article, the noise is modeled as a spatially homogeneous sound field, with an unknown time-varying PSD multiplied by a known time-invariant spatial coherence matrix. We derive two maximum likelihood estimators (MLEs) for the various PSDs, including the noise: The first is a non-blocking-based estimator, that jointly estimates the PSDs of the speech, reverberation and noise components. The second MLE is a blocking-based estimator, that blocks the speech signal and estimates the reverberation and noise PSDs. Since a closed-form solution does not exist, both estimators iteratively maximize the likelihood using the Fisher scoring method. In order to compare both methods, the corresponding Cramér-Rao Bounds (CRBs) are derived. For both the reverberation and the noise PSDs, it is shown that the non-blocking-based CRB is lower than the blocking-based CRB. Performance evaluation using both simulated and real reverberant and noisy signals, shows that the proposed estimators outperform competing estimators, and greatly reduce the effect of reverberation and noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {61–76},
numpages = {16}
}

@article{10.1109/TASLP.2019.2948730,
author = {Zohourian, Mehdi and Martin, Rainer},
title = {Binaural Direct-to-Reverberant Energy Ratio and Speaker Distance Estimation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2948730},
doi = {10.1109/TASLP.2019.2948730},
abstract = {This article addresses the problem of distance estimation using binaural hearing aid microphones in reverberant rooms. Among several distance indicators, the direct-to-reverberant energy ratio (DRR) has been shown to be more effective than other features. Therefore, we present two novel approaches to estimate the DRR of binaural signals. The first method is based on the interaural magnitude-squared coherence whereas the second approach uses stochastic maximum likelihood beamforming to estimate the power of the direct and reverberant components. The proposed DRR estimation algorithms are integrated into a distance estimation technique. When based solely on DRR, the distance estimation algorithm requires calibration where naturally the critical distance is a good calibration point. We thus propose two approaches for the calibration of the distance estimation algorithm: Informed calibration using the critical distance of the reverberant room and blind calibration using the listener's own voice. Results across various acoustical environments show the benefit of the proposed algorithms for the estimation of sound source distances up to 3 m with an estimation error of about 35 cm using informed calibration and about 1 m using the fully blind calibration strategy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {92–104},
numpages = {13}
}

@article{10.1109/TASLP.2020.2966869,
author = {Dietzen, Thomas and Doclo, Simon and Moonen, Marc and van Waterschoot, Toon},
title = {Integrated Sidelobe Cancellation and Linear Prediction Kalman Filter for Joint Multi-Microphone Speech Dereverberation, Interfering Speech Cancellation, and Noise Reduction},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2966869},
doi = {10.1109/TASLP.2020.2966869},
abstract = {In multi-microphone speech enhancement, reverberation as well as additive noise and/or interfering speech are commonly suppressed by deconvolution and spatial filtering, e.g., using multi-channel linear prediction (MCLP) on the one hand and beamforming, e.g., a generalized sidelobe canceler (GSC), on the other hand. In this article, we consider several reverberant speech components, whereof some are to be dereverberated and others to be canceled, as well as a diffuse (e.g., babble) noise component to be suppressed. In order to perform both deconvolution and spatial filtering, we integrate MCLP and the GSC into a novel architecture referred to as integrated sidelobe cancellation and linear prediction (ISCLP), where the sidelobe-cancellation (SC) filter and the linear prediction (LP) filter operate in parallel, but on different microphone signal frames. Within ISCLP, we estimate both filters jointly by means of a single Kalman filter. We further propose a spectral Wiener gain post-processor, which is shown to relate to the Kalman filter's posterior state estimate. The presented ISCLP Kalman filter is benchmarked against two state-of-the-art approaches, namely first a pair of alternating Kalman filters respectively performing dereverberation and noise reduction, and second an MCLP+GSC Kalman filter cascade. While the ISCLP Kalman filter is roughly <inline-formula><tex-math notation="LaTeX">$M^2$</tex-math></inline-formula> times less expensive than both reference algorithms, where <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula> denotes the number of microphones, it is shown to perform at least similarly as compared to the former, and to outperform the latter. A MATLAB implementation is available.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {740–754},
numpages = {15}
}

@article{10.1109/TASLP.2020.2982030,
author = {Zhu, Yingying and Zhao, Haiquan and Zeng, Xiangping and Chen, Badong},
title = {Robust Generalized Maximum Correntropy Criterion Algorithms for Active Noise Control},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982030},
doi = {10.1109/TASLP.2020.2982030},
abstract = {As a robust nonlinear similarity measure, the maximum correntropy criterion (MCC) has been successfully applied to active noise control (ANC) for impulsive noise. The default kernel function of the filtered-x maximum correntropy criterion (FxMCC) algorithm is the Gaussian kernel, which is desirable in many cases for its smooth and strict positive-definite. However, it is not always the best choice. In this study, a filtered-x generalized maximum correntropy criterion (FxGMCC) algorithm is proposed, which adopts the generalized Gaussian density (GGD) function as its kernel. The FxGMCC algorithm has greater robust ability against non-Gaussian environments, but, it still adopts a single error norm which exhibits poor convergence rate and noise reduction performance. To surmount this problem, an improved FxGMCC (IFxGMCC) algorithm with continuous mixed <italic>L<sub>p</sub>-</italic>norm is proposed. Moreover, to make a trade-off between fast convergence rate and low steady-state misalignment, a convexly combined IFxGMCC (C-IFxGMCC) algorithm is further developed. The stability mechanism and computational complexity of the proposed algorithms are analyzed. Simulation results in the context of different impulsive noises as well as the real noise signals verify that the proposed algorithms are superior to most of the existing robust adaptive algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1282–1292},
numpages = {11}
}

@article{10.1109/TASLP.2019.2947737,
author = {Gupta, Chitralekha and Li, Haizhou and Wang, Ye},
title = {Automatic Leaderboard: Evaluation of Singing Quality Without a Standard Reference},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2947737},
doi = {10.1109/TASLP.2019.2947737},
abstract = {Automatic evaluation of singing quality can be done with the help of a reference singing or the digital sheet music of the song. However, such a standard reference is not always available. In this article, we propose a framework to rank a large pool of singers according to their singing quality without any standard reference. We define musically motivated absolute measures based on pitch histogram, and relative measures based on inter-singer statistics to evaluate the quality of singing attributes such as intonation, and rhythm. The absolute measures evaluate the goodness of pitch histogram specific to a singer, while the relative measures use the similarity between singers in terms of pitch, rhythm, and timbre as an indicator of singing quality. With the relative measures, we formulate the concept of <italic>veracity</italic> or <italic>truth-finding</italic> for the ranking of singing quality. We successfully validate a self-organizing approach to rank-ordering a large pool of singers. The fusion of absolute and relative measures results in an average Spearman's rank correlation of 0.71 with human judgments in a 10-fold cross-validation experiment, which is close to the inter-judge correlation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {13–26},
numpages = {14}
}

@article{10.1109/TASLP.2020.2982029,
author = {Fan, Cunhang and Tao, Jianhua and Liu, Bin and Yi, Jiangyan and Wen, Zhengqi and Liu, Xuefei},
title = {End-to-End Post-Filter for Speech Separation With Deep Attention Fusion Features},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982029},
doi = {10.1109/TASLP.2020.2982029},
abstract = {In this article, we propose an end-to-end post-filter method with deep attention fusion features for monaural speaker-independent speech separation. At first, a time-frequency domain speech separation method is applied as the pre-separation stage. The aim of pre-separation stage is to separate the mixture preliminarily. Although this stage can separate the mixture, it still contains the residual interference. In order to enhance the pre-separated speech and improve the separation performance further, the end-to-end post-filter (E2EPF) with deep attention fusion features is proposed. The E2EPF can make full use of the prior knowledge of the pre-separated speech, which contributes to speech separation. It is a fully convolutional speech separation network and uses the waveform as the input features. Firstly, the 1-D convolutional layer is utilized to extract the deep representation features for the mixture and pre-separated signals in the time domain. Secondly, to pay more attention to the outputs of the pre-separation stage, an attention module is applied to acquire deep attention fusion features, which are extracted by computing the similarity between the mixture and the pre-separated speech. These deep attention fusion features are conducive to reduce the interference and enhance the pre-separated speech. Finally, these features are sent to the post-filter to estimate each target signals. Experimental results on the WSJ0-2mix dataset show that the proposed method outperforms the state-of-the-art speech separation method. Compared with the pre-separation method, our proposed method can acquire 64.1%, 60.2%, 25.6% and 7.5% relative improvements in scale-invariant source-to-noise ratio (SI-SNR), the signal-to-distortion ratio (SDR), the perceptual evaluation of speech quality (PESQ) and the short-time objective intelligibility (STOI) measures, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1303–1314},
numpages = {12}
}

@article{10.1109/TASLP.2019.2952013,
author = {Mimilakis, Stylianos Ioannis and Drossos, Konstantinos and Cano, Estefan\'{\i}a and Schuller, Gerald},
title = {Examining the Mapping Functions of Denoising Autoencoders in Singing Voice Separation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2952013},
doi = {10.1109/TASLP.2019.2952013},
abstract = {The goal of this article is to investigate what singing voice separation approaches based on neural networks learn from the data. We examine the mapping functions of neural networks based on the denoising autoencoder (DAE) model that are conditioned on the mixture magnitude spectra. To approximate the mapping functions, we propose an algorithm inspired by the knowledge distillation, denoted the neural couplings algorithm (NCA). The NCA yields a matrix that expresses the mapping of the mixture to the target source magnitude information. Using the NCA, we examine the mapping functions of three fundamental DAE-based models in music source separation; one with single-layer encoder and decoder, one with multi-layer encoder and single-layer decoder, and one using skip-filtering connections (SF) with a single-layer encoding and decoding. We first train these models with realistic data to estimate the singing voice magnitude spectra from the corresponding mixture. We then use the optimized models and test spectral data as input to the NCA. Our experimental findings show that approaches based on the DAE model learn scalar filtering operators, exhibiting a predominant diagonal structure in their corresponding mapping functions, limiting the exploitation of inter-frequency structure of music data. In contrast, skip-filtering connections are shown to assist the DAE model in learning filtering operators that exploit richer inter-frequency structures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {266–278},
numpages = {13}
}

@article{10.1109/TASLP.2019.2950597,
author = {Yu, Yi and He, Hongsen and Chen, Badong and Li, Jianghui and Zhang, Youwen and Lu, Lu},
title = {M-Estimate Based Normalized Subband Adaptive Filter Algorithm: Performance Analysis and Improvements},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2950597},
doi = {10.1109/TASLP.2019.2950597},
abstract = {This article studies the mean and mean-square behaviors of the M-estimate based normalized subband adaptive filter algorithm (M-NSAF) with robustness against impulsive noise. Based on the contaminated-Gaussian noise model, the stability condition, transient and steady-state results of the algorithm are formulated analytically. These analysis results help us to better understand the M-NSAF performance in impulsive noise. To further obtain fast convergence and low steady-state estimation error, we derive a variable step size (VSS) M-NSAF algorithm. This VSS scheme is also generalized to the proportionate M-NSAF variant for sparse systems. Computer simulations on the system identification in impulsive noise and the acoustic echo cancellation with double-talk are performed to demonstrate our theoretical analysis and the effectiveness of the proposed algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {225–239},
numpages = {15}
}

@article{10.1109/TASLP.2020.2982297,
author = {Fernando, Tharindu and Sridharan, Sridha and McLaren, Mitchell and Priyasad, Darshana and Denman, Simon and Fookes, Clinton},
title = {Temporarily-Aware Context Modeling Using Generative Adversarial Networks for Speech Activity Detection},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982297},
doi = {10.1109/TASLP.2020.2982297},
abstract = {This paper presents a novel framework for Speech Activity Detection (SAD). Inspired by the recent success of multi-task learning approaches in the speech processing domain, we propose a novel joint learning framework for SAD. We utilise generative adversarial networks to automatically learn a loss function for joint prediction of the frame-wise speech/ non-speech classifications together with the next audio segment. In order to exploit the temporal relationships within the input signal, we propose a temporal discriminator which aims to ensure that the predicted signal is temporally consistent. We evaluate the proposed framework on multiple public benchmarks, including NIST OpenSAT’ 17, AMI Meeting and HAVIC, where we demonstrate its capability to outperform state-of-the-art SAD approaches. Furthermore, our cross-database evaluations demonstrate the robustness of the proposed approach across different languages, accents, and acoustic environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1159–1169},
numpages = {11}
}

@article{10.1109/TASLP.2020.2975390,
author = {Schepker, Henning and Nordholm, Sven and Doclo, Simon},
title = {Acoustic Feedback Suppression for Multi-Microphone Hearing Devices Using a Soft-Constrained Null-Steering Beamformer},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2975390},
doi = {10.1109/TASLP.2020.2975390},
abstract = {Acoustic feedback occurs in hearing aids due to the coupling between the hearing aid loudspeaker and microphone(s). In order to reduce the acoustic feedback, adaptive filters are commonly used to estimate the feedback contribution in the microphone(s). While theoretically allowing for perfect feedback cancellation, in practice the adaptive filter typically converges to a biased optimal solution due to the closed-loop acoustical system of the hearing aid. Previously it has therefore been proposed to suppress the acoustic feedback contribution for an earpiece with multiple integrated microphones and loudspeakers using a fixed null-steering beamformer and hence avoiding a biased adaption. While previous null-steering beamforming approaches aimed at perfect preservation of the incoming signal using its relative transfer function (RTF), in this article we propose to use a soft constraint that allows to trade off between incoming signal preservation and feedback suppression. We formulate the computation of the beamformer coefficients both as a least-squares optimization procedure, aiming to minimize the residual feedback power, and as a min-max optimization procedure, aiming to directly maximize the maximum stable gain of the hearing aid. Experimental evaluations were performed using measured acoustic feedback paths from a custom earpiece with two microphones in the vent and a third microphone in the concha. Results show that the proposed fixed null-steering beamformer using the RTF-based soft constraint provides a reduction of the acoustic feedback by 7–8 dB compared to the previously proposed RTF-based hard constraint while limiting the distortions of the incoming signal in the beamformer output.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {929–940},
numpages = {12}
}

@article{10.1109/TASLP.2019.2960721,
author = {Zhang, Jing-Xuan and Ling, Zhen-Hua and Dai, Li-Rong},
title = {Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2960721},
doi = {10.1109/TASLP.2019.2960721},
abstract = {This article presents a method of sequence-to-sequence (seq2seq) voice conversion using non-parallel training data. In this method, disentangled linguistic and speaker representations are extracted from acoustic features, and voice conversion is achieved by preserving the linguistic representations of source utterances while replacing the speaker representations with the target ones. Our model is built under the framework of encoder-decoder neural networks. A recognition encoder is designed to learn the disentangled linguistic representations with two strategies. First, phoneme transcriptions of training data are introduced to provide the references for leaning linguistic representations of audio signals. Second, an adversarial training strategy is employed to further wipe out speaker information from the linguistic representations. Meanwhile, speaker representations are extracted from audio signals by a speaker encoder. The model parameters are estimated by two-stage training, including a pre-training stage using a multi-speaker dataset and a fine-tuning stage using the dataset of a specific conversion pair. Since both the recognition encoder and the decoder for recovering acoustic features are seq2seq neural networks, there are no constrains of frame alignment and frame-by-frame conversion in our proposed method. Experimental results showed that our method obtained higher similarity and naturalness than the best non-parallel voice conversion method in Voice Conversion Challenge 2018. Besides, the performance of our proposed method was closed to the state-of-the-art parallel seq2seq voice conversion method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {540–552},
numpages = {13}
}

@article{10.1109/TASLP.2020.2982578,
author = {Ho, Chung-Ying and Shyu, Kuo-Kai and Chang, Cheng-Yuan and Kuo, Sen M.},
title = {Efficient Narrowband Noise Cancellation System Using Adaptive Line Enhancer},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982578},
doi = {10.1109/TASLP.2020.2982578},
abstract = {Rotating machines such as motors, generate noise at the fundamental frequency and its harmonics. The narrowband active noise control (NANC) algorithm is widely used to cancel such noise. Traditional feedforward NANC systems use non-acoustic sensors to measure rotation speeds, and then a bank of signal generators produce synchronized tonal signals as the reference signals according to the fundamental frequency of the undesired noise. However, the non-acoustic sensors such as encoders usually cost a lot and are not reliable. This study proposes using the feedback ANC structure and incorporates with several adaptive line enhancers (ALEs) to generate the reference signals. Without using non-acoustic sensors, the proposed system only updates the center frequency of the first ALE to track the fundamental frequency change, which greatly reduces complexity of the proposed ANC system. The frequency mismatch (FM) problem is overcome by properly setting the bandwidths of the ALE. Performance were verified by using both simulations and real-time experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1094–1103},
numpages = {10}
}

@article{10.1109/TASLP.2019.2960734,
author = {Fahim, Abdullah and Samarasinghe, Prasanga N. and Abhayapala, Thushara D.},
title = {Multi-Source DOA Estimation Through Pattern Recognition of the Modal Coherence of a Reverberant Soundfield},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2960734},
doi = {10.1109/TASLP.2019.2960734},
abstract = {We propose a novel multi-source direction of arrival (DOA) estimation technique using a convolutional neural network algorithm which learns the modal coherence patterns of an incident soundfield through measured spherical harmonic coefficients. We train our model for individual time-frequency bins in the short-time Fourier transform spectrum by analyzing the unique snapshot of modal coherence for each desired direction. The proposed method is capable of estimating simultaneously active multiple sound sources on a 3D space using a single-source training scheme. This single-source training scheme reduces the training time and resource requirements as well as allows the reuse of the same trained model for different multi-source combinations. The method is evaluated against various simulated and practical noisy and reverberant environments with varying acoustic criteria and found to outperform the baseline methods in terms of DOA estimation accuracy. Furthermore, the proposed algorithm allows independent training of azimuth and elevation during a full DOA estimation over 3D space which significantly improves its training efficiency without affecting the overall estimation accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {605–618},
numpages = {14}
}

@article{10.1109/TASLP.2019.2949687,
author = {Su, Ming-Hsiang and Wu, Chung-Hsien and Chen, Liang-Yu},
title = {Attention-Based Response Generation Using Parallel Double Q-Learning for Dialog Policy Decision in a Conversational System},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2949687},
doi = {10.1109/TASLP.2019.2949687},
abstract = {This article proposes an approach to response generation using a Parallel Double Q-learning algorithm for dialog policy decision in a conversational system. First, a new semantic representation of the user's input sentence is presented by using the CKIP parser to derive the semantic dependency sequence of the input sentence. Then, a Gated Recurrent Unit-based Autoencoder is used to obtain the user's turn representation as well as context representation. A Parallel Double Q-learning algorithm with a Deep Neural Network (PD-DQN), combining two Double DQNs in parallel for the contextual and semantic information in the user's message, respectively, are proposed to determine the dialog act. Finally, the user's input and the determined dialog act are fed to an attention-based Transformer model to generate the response template. With the generated response template, the semantic slots are filled with their corresponding values to obtain the final sentence response. This article collects a multi-turn conversation database consisting of 4186 turns in the travel domain and 447 chitchat question-answer pairs as the evaluation corpus. Five-fold cross validation is employed for performance evaluation. Experimental results show that the proposed approach based on semantic dependency for intent detection increases the accuracy by 4.3%. For dialog policy decision, the PD-DQN achieves 87.57% task success rate, which is 13.9% higher than the baseline Double DQN (73.67%). Finally, using the attention-based Transformer for response template generation obtains a Bleu score of 13.6, improved by 1.5 compared to the Sequence-to-Sequence model. In subjective evaluation, both the dialog policy and sentence generation model achieve a higher appropriateness and grammatical correctness scores than the baseline system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {131–143},
numpages = {13}
}

@article{10.1109/TASLP.2020.2986877,
author = {T, Lavanya and T, Nagarajan and P, Vijayalakshmi},
title = {Multi-Level Single-Channel Speech Enhancement Using a Unified Framework for Estimating Magnitude and Phase Spectra},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2986877},
doi = {10.1109/TASLP.2020.2986877},
abstract = {Speech enhancement algorithms aim to improve the quality and intelligibility of noise corrupted speech, through spectral or temporal modifications. Most of the existing speech enhancement algorithms achieve this by modifying the magnitude spectrum alone, while keeping the phase spectrum intact. In the current work, both phase and magnitude spectra are modified to enhance noisy speech using a multi-level speech enhancement technique. Proposed phase compensation (PC) function achieves first-level enhancement by modifying the phase spectrum alone. Second-level enhancement performs energy redistribution in the phase compensated speech signal to make weak speech and non-speech regions highly contrastive. Energy redistribution from the energy-rich voiced to the weak unvoiced regions is carried out using adaptive power law transformation (APLT) technique by optimizing the parameters with a total energy constraint employing particle swarm optimization algorithm. Log MMSE technique with a novel speech presence uncertainty (SPU) estimation method is proposed for third-level enhancement. The compensated phase spectrum and the magnitude spectrum estimated using log MMSE, with proposed SPU estimation (log MMSE + proposed SPU), are used to reconstruct the enhanced speech signal. The proposed speech enhancement technique is compared with recent speech enhancement techniques that estimate both magnitude and phase, for various noise levels (−5 to +5 dB), in terms of objective and subjective measures. It is observed that the proposed technique improves signal quality and maintains or improves intelligibility under stationary, and non-stationary noise conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1315–1327},
numpages = {13}
}

@article{10.1109/TASLP.2019.2959721,
author = {Li, Ruizhi and Wang, Xiaofei and Mallidi, Sri Harish and Watanabe, Shinji and Hori, Takaaki and Hermansky, Hynek},
title = {Multi-Stream End-to-End Speech Recognition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2959721},
doi = {10.1109/TASLP.2019.2959721},
abstract = {Attention-based methods and Connectionist Temporal Classification (CTC) network have been promising research directions for end-to-end (E2E) Automatic Speech Recognition (ASR). The joint CTC/Attention model has achieved great success by utilizing both architectures during multi-task training and joint decoding. In this article, we present a multi-stream framework based on joint CTC/Attention E2E ASR with parallel streams represented by separate encoders aiming to capture diverse information. On top of the regular attention networks, the Hierarchical Attention Network (HAN) is introduced to steer the decoder toward the most informative encoders. A separate CTC network is assigned to each stream to force monotonic alignments. Two representative framework have been proposed and discussed, which are Multi-Encoder Multi-Resolution (MEM-Res) framework and Multi-Encoder Multi-Array (MEM-Array) framework, respectively. In MEM-Res framework, two heterogeneous encoders with different architectures, temporal resolutions and separate CTC networks work in parallel to extract complementary information from same acoustics. Experiments are conducted on Wall Street Journal (WSJ) and CHiME-4, resulting in relative Word Error Rate (WER) reduction of <inline-formula><tex-math notation="LaTeX">$text{18.0}!-!text{32.1}%$</tex-math></inline-formula> and the best WER of <inline-formula><tex-math notation="LaTeX">$text{3.6}%$</tex-math></inline-formula> in the WSJ eval92 test set. The MEM-Array framework aims at improving the far-field ASR robustness using multiple microphone arrays which are activated by separate encoders. Compared with the best single-array results, the proposed framework has achieved relative WER reduction of <inline-formula><tex-math notation="LaTeX">$text{3.7}%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$text{9.7}%$</tex-math></inline-formula> in AMI and DIRHA multi-array corpora, respectively, which also outperforms conventional fusion strategies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {646–655},
numpages = {10}
}

@article{10.1109/TASLP.2020.2987748,
author = {Zuo, Huanyu and Samarasinghe, Prasanga N. and Abhayapala, Thushara D.},
title = {Intensity Based Spatial Soundfield Reproduction Using an Irregular Loudspeaker Array},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2987748},
doi = {10.1109/TASLP.2020.2987748},
abstract = {Sound intensity is an acoustic quantity closely linked with human perception of sound location, and it can be controlled to create a high level of realism to humans in soundfield reproduction systems. In this article, we present an intensity matching technique to optimally reproduce sound intensity over a continuous spatial region using an irregular loudspeaker array. This avoids several known limitations in the previous works on intensity based soundfield reproduction, such as a single sweet spot for the listener and a regular loudspeaker geometry that is difficult to implement in real-world applications. In contrast to the previous works, the new technique uses a cost function we built to optimize sound intensity over space by exploiting spatial sound intensity distributions. The spatial sound intensity distribution is represented by spherical harmonic coefficients of sound pressure, which are widely used to describe a spatial soundfield. Compared to the conventional spatial soundfield reproduction method of pressure matching in the spherical harmonic domain and the HOA <inline-formula><tex-math notation="LaTeX">$max$</tex-math></inline-formula>-<inline-formula><tex-math notation="LaTeX">$r_E$</tex-math></inline-formula> decoding method optimizing sound intensity at a single position, we show that the intensity matching technique has better overall performance with two different irregular loudspeaker layouts through simulations. The impact of microphone noise on reproduction performance is also assessed. Finally, we carry out perceptual localization experiments to validate the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1356–1369},
numpages = {14}
}

@article{10.1109/TASLP.2019.2947777,
author = {Amini, Jamal and Hendriks, Richard Christian and Heusdens, Richard and Guo, Meng and Jensen, Jesper},
title = {Rate-Constrained Noise Reduction in Wireless Acoustic Sensor Networks},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2947777},
doi = {10.1109/TASLP.2019.2947777},
abstract = {Wireless acoustic sensor networks (WASNs) can be used for centralized multi-microphone noise reduction, where the processing is done in a fusion center (FC). To perform the noise reduction, the data needs to be transmitted to the FC. Considering the limited battery life of the devices in a WASN, the total data rate at which the FC can communicate with the different network devices should be constrained. In this article, we propose a rate-constrained multi-microphone noise reduction algorithm, which jointly finds the best rate allocation and estimation weights for the microphones across all frequencies. The optimal linear estimators are found to be the quantized Wiener filters, and the rates are the solutions to a filter-dependent reverse water-filling problem. The performance of the proposed framework is evaluated using simulations in terms of mean square error and predicted speech intelligibility. The results show that the proposed method is very close in performance to that of the existing optimal method based on discrete optimization. However, the proposed approach can do this at a much lower complexity, while the existing optimal reference method needs a non-tractable exhaustive search to find the best rate allocation across microphones.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1–12},
numpages = {12}
}

@article{10.1109/TASLP.2019.2955252,
author = {Zhang, Weijian and Song, Peng},
title = {Transfer Sparse Discriminant Subspace Learning for Cross-Corpus Speech Emotion Recognition},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955252},
doi = {10.1109/TASLP.2019.2955252},
abstract = {Cross-corpus speech emotion recognition has attracted much attention due to the widespread existence of various emotional speech in life. It takes one corpus for training and another corpus for testing, and generally involves the following two basic problems: the corpus-invariant feature representation and relevance across different corpora. To deal with these two problems, we propose a novel transfer learning method called transfer sparse discriminant subspace learning (TSDSL) in this article. Specifically, to solve the first problem, we learn a common feature subspace of different corpora by introducing the discriminative learning and <inline-formula><tex-math notation="LaTeX">$ell _{2,1}-$</tex-math></inline-formula>norm penalty, which can learn the most discriminative features across different corpora. To address the second problem, we construct a novel nearest neighbor graph as the distance metric, in which the similarity between different corpora can be measured simultaneously. Extensive experiments are carried out on cross-corpus speech emotion recognition tasks, and the results show that our method can achieve competitive performance compared with state-of-the-art algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {307–318},
numpages = {12}
}

@article{10.1109/TASLP.2020.2982282,
author = {Sun, Haipeng and Wang, Rui and Chen, Kehai and Utiyama, Masao and Sumita, Eiichiro and Zhao, Tiejun},
title = {Unsupervised Neural Machine Translation With Cross-Lingual Language Representation Agreement},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982282},
doi = {10.1109/TASLP.2020.2982282},
abstract = {Unsupervised cross-lingual language representation initialization methods such as unsupervised bilingual word embedding (UBWE) pre-training and cross-lingual masked language model (CMLM) pre-training, together with mechanisms such as denoising and back-translation, have advanced unsupervised neural machine translation (UNMT), which has achieved impressive results on several language pairs, particularly French-English and German-English. Typically, UBWE focuses on initializing the word embedding layer in the encoder and decoder of UNMT, whereas the CMLM focuses on initializing the entire encoder and decoder of UNMT. However, UBWE/CMLM training and UNMT training are independent, which makes it difficult to assess how the quality of UBWE/CMLM affects the performance of UNMT during UNMT training. In this paper, we first empirically explore relationships between UNMT and UBWE/CMLM. The empirical results demonstrate that the performance of UBWE and CMLM has a significant influence on the performance of UNMT. Motivated by this, we propose a novel UNMT structure with cross-lingual language representation agreement to capture the interaction between UBWE/CMLM and UNMT during UNMT training. Experimental results on several language pairs demonstrate that the proposed UNMT models improve significantly over the corresponding state-of-the-art UNMT baselines.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1170–1182},
numpages = {13}
}

@article{10.1109/TASLP.2019.2955858,
author = {Duan, Richeng and Kawahara, Tatsuya and Dantsuji, Masatake and Nanjo, Hiroaki},
title = {Cross-Lingual Transfer Learning of Non-Native Acoustic Modeling for Pronunciation Error Detection and Diagnosis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2955858},
doi = {10.1109/TASLP.2019.2955858},
abstract = {In computer-assisted pronunciation training (CAPT), the scarcity of large-scale non-native corpora and human expert annotations are two fundamental challenges to non-native acoustic modeling. Most existing approaches of acoustic modeling in CAPT are based on non-native corpora while there are so many living languages in the world. It is impractical to collect and annotate every non-native speech corpus considering different language pairs. In this work, we address non-native acoustic modeling (both on phonetic and articulatory level) based on transfer learning. In order to effectively train acoustic models of non-native speech without using such data, we propose to exploit two large native speech corpora of learner's native language (L1) and target language (L2) to model cross-lingual phenomena. This kind of transfer learning can provide a better feature representation of non-native speech. Experimental evaluations are carried out for Japanese speakers learning English. We first demonstrate the proposed acoustic-phone model achieves a lower word error rate in non-native speech recognition. It also improves the pronunciation error detection based on goodness of pronunciation (GOP) score. For diagnosis of pronunciation errors, the proposed acoustic-articulatory modeling method is effective for providing detailed feedback at the articulation level.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {391–401},
numpages = {11}
}

@article{10.1109/TASLP.2019.2948770,
author = {Mitsufuji, Yuki and Uhlich, Stefan and Takamune, Norihiro and Kitamura, Daichi and Koyama, Shoichi and Saruwatari, Hiroshi},
title = {Multichannel Non-Negative Matrix Factorization Using Banded Spatial Covariance Matrices in Wavenumber Domain},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2948770},
doi = {10.1109/TASLP.2019.2948770},
abstract = {Blind source separation exploiting multichannel information has long been a popular topic, and recently proposed methods based on the local Gaussian model have shown promising results despite its high computational cost for the case of many microphone signals. The low updating speed for such a model is mainly due to the inversion of a spatial covariance matrix, for which the complexity increases with the number of microphones, <inline-formula><tex-math notation="LaTeX">$M$</tex-math></inline-formula>, and is generally of order <inline-formula><tex-math notation="LaTeX">$O(M^3)$</tex-math></inline-formula>. Several projection-based approaches that attempt to concentrate energy on the diagonal part of the spatial covariance matrix have been introduced to circumvent the matrix inversion, which can reduce the complexity to <inline-formula><tex-math notation="LaTeX">$O(M)$</tex-math></inline-formula>. In this article, we focus on the fast Fourier transform as a projection method because the energy concentration on the diagonal can be efficiently achieved compared with other projection-based methods. For the case where the diagonalization is imperfect, for example, owing to discontinuities at the edge of a linear array, we also developed a more robust algorithm approximating the tri-diagonal part of the spatial covariance matrix, which requires a complexity of <inline-formula><tex-math notation="LaTeX">$O(M^2)$</tex-math></inline-formula> for the inversion by applying the Thomas algorithm. To remove the ad-hoc integration of post clustering after the decomposition, we also examine a self-clustering algorithm. Our evaluation shows better results than other previously proposed methods in terms of the separation quality under reverberant conditions as well as higher efficiency than multichannel non-negative matrix factorization.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {49–60},
numpages = {12}
}

@article{10.1109/TASLP.2020.2964958,
author = {Koyama, Shoichi and Chardon, Gilles and Daudet, Laurent},
title = {Optimizing Source and Sensor Placement for Sound Field Control: An Overview},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2964958},
doi = {10.1109/TASLP.2020.2964958},
abstract = {In order to control an acoustic field inside a target region, it is important to choose suitable positions of secondary sources (loudspeakers) and sensors (control points/microphones). This article provides an overview of state-of-the-art source and sensor placement methods in sound field control. Although the placement of both sources and sensors greatly affects control accuracy and filter stability, their joint optimization has not been thoroughly investigated in the acoustics literature. In this context, we reformulate five general source and/or sensor placement methods that can be applied for sound field control. We compare the performance of these methods through extensive numerical simulations in both narrowband and broadband scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {696–714},
numpages = {19}
}

@article{10.1109/TASLP.2020.2970241,
author = {Ai, Yang and Ling, Zhen-Hua},
title = {A Neural Vocoder With Hierarchical Generation of Amplitude and Phase Spectra for Statistical Parametric Speech Synthesis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2970241},
doi = {10.1109/TASLP.2020.2970241},
abstract = {This article presents a neural vocoder named HiNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra hierarchically. Different from existing neural vocoders such as WaveNet, SampleRNN and WaveRNN which directly generate waveform samples using single neural networks, the HiNet vocoder is composed of an amplitude spectrum predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a simple DNN model which predicts log amplitude spectra (LAS) from acoustic features. The predicted LAS are sent into the PSP for phase recovery. Considering the issue of phase warping and the difficulty of phase modeling, the PSP is constructed by concatenating a neural source-filter (NSF) waveform generator with a phase extractor. We also introduce generative adversarial networks (GANs) into both ASP and PSP. Finally, the outputs of ASP and PSP are combined to reconstruct speech waveforms by short-time Fourier synthesis. Since there are no autoregressive structures in both predictors, the HiNet vocoder can generate speech waveforms with high efficiency. Objective and subjective experimental results show that our proposed HiNet vocoder achieves better naturalness of reconstructed speech than the conventional STRAIGHT vocoder, a 16-bit WaveNet vocoder using open source implementation and an NSF vocoder with similar complexity to the PSP and obtains similar performance with a 16-bit WaveRNN vocoder. We also find that the performance of HiNet is insensitive to the complexity of the neural waveform generator in PSP to some extend. After simplifying its model structure, the time consumed for generating 1 s waveforms of 16 kHz speech using a GPU can be further reduced from 0.34 s to 0.19 s without significant quality degradation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {839–851},
numpages = {13}
}

@article{10.1109/TASLP.2019.2946789,
author = {Wang, Peidong and Tan, Ke and Wang, De Liang},
title = {Bridging the Gap Between Monaural Speech Enhancement and Recognition With Distortion-Independent Acoustic Modeling},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2946789},
doi = {10.1109/TASLP.2019.2946789},
abstract = {Monaural speech enhancement has made dramatic advances since the introduction of deep learning a few years ago. Although enhanced speech has been demonstrated to have better intelligibility and quality for human listeners, feeding it directly to automatic speech recognition (ASR) systems trained with noisy speech has not produced expected improvements in ASR performance. The lack of an enhancement benefit on recognition, or the gap between monaural speech enhancement and recognition, is often attributed to speech distortions introduced in the enhancement process. In this article, we analyze the distortion problem, compare different acoustic models, and investigate a distortion-independent training scheme for monaural speech recognition. Experimental results suggest that distortion-independent acoustic modeling is able to overcome the distortion problem. Such an acoustic model can also work with speech enhancement models different from the one used during training. Moreover, the models investigated in this paper outperform the previous best system on the CHiME-2 corpus.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {39–48},
numpages = {10}
}

@article{10.1109/TASLP.2020.2966891,
author = {Dietzen, Thomas and Doclo, Simon and Moonen, Marc and van Waterschoot, Toon},
title = {Square Root-Based Multi-Source Early PSD Estimation and Recursive RETF Update in Reverberant Environments by Means of the Orthogonal Procrustes Problem},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2966891},
doi = {10.1109/TASLP.2020.2966891},
abstract = {Multi-channel short-time Fourier transform (STFT) domain-based processing of reverberant microphone signals commonly relies on power-spectral-density (PSD) estimates of early source images, where early refers to reflections contained within the same STFT frame. State-of-the-art approaches to multi-source early PSD estimation, given an estimate of the associated relative early transfer functions (RETFs), conventionally minimize the approximation error defined with respect to the early correlation matrix, requiring non-negative inequality constraints on the PSDs. Instead, we here propose to factorize the early correlation matrix and minimize the approximation error defined with respect to the early-correlation-matrix square root. The proposed minimization problem—constituting a generalization of the so-called orthogonal Procrustes problem—seeks a unitary matrix and the square roots of the early PSDs up to an arbitrary complex argument, whereby non-negative inequality constraints become redundant. A solution is obtained iteratively, requiring one singular value decomposition (SVD) per iteration. The estimated unitary matrix and early PSD square roots further allow to recursively update the RETF estimate, which is not inherently possible in the conventional approach. An estimate of the said early-correlation-matrix square root itself is obtained by means of the generalized eigenvalue decomposition (GEVD), where we further propose to restore non-stationarities by desmoothing the generalized eigenvalues in order to compensate for inevitable recursive averaging. Simulation results indicate fast convergence of the proposed multi-source early PSD estimation approach in only one iteration if initialized appropriately, and better performance as compared to the conventional approach. A MATLAB implementation is available.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {755–769},
numpages = {15}
}

@article{10.1109/TASLP.2019.2950099,
author = {Wang, Xin and Takaki, Shinji and Yamagishi, Junichi and King, Simon and Tokuda, Keiichi},
title = {A Vector Quantized Variational Autoencoder (VQ-VAE) Autoregressive Neural <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> Model for Statistical Parametric Speech Synthesis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2950099},
doi = {10.1109/TASLP.2019.2950099},
abstract = {Recurrent neural networks (RNNs) can predict fundamental frequency (F0) for statistical parametric speech synthesis systems, given linguistic features as input. However, these models assume conditional independence between consecutive <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> values, given the RNN state. In a previous study, we proposed autoregressive (AR) neural <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> models to capture the causal dependency of successive <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> values. In subjective evaluations, a deep AR model (DAR) outperformed an RNN. Here, we propose a Vector Quantized Variational Autoencoder (VQ-VAE) neural <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> model that is both more efficient and more interpretable than the DAR. This model has two stages: one uses the VQ-VAE framework to learn a latent code for the <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> contour of each linguistic unit, and other learns to map from linguistic features to latent codes. In contrast to the DAR and RNN, which process the input linguistic features frame-by-frame, the new model converts one linguistic feature vector into one latent code for each linguistic unit. The new model achieves better objective scores than the DAR, has a smaller memory footprint and is computationally faster. Visualization of the latent codes for phones and moras reveals that each latent code represents an <inline-formula><tex-math notation="LaTeX">$F_0$</tex-math></inline-formula> shape for a linguistic unit.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {157–170},
numpages = {14}
}

@article{10.1109/TASLP.2020.2973896,
author = {Scharenborg, Odette and Ondel, Lucas and Palaskar, Shruti and Arthur, Philip and Ciannella, Francesco and Du, Mingxing and Larsen, Elin and Merkx, Danny and Riad, Rachid and Wang, Liming and Dupoux, Emmanuel and Besacier, Laurent and Black, Alan and Hasegawa-Johnson, Mark and Metze, Florian and Neubig, Graham and St\"{u}ker, Sebastian and Godard, Pierre and M\"{u}ller, Markus},
title = {Speech Technology for Unwritten Languages},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2973896},
doi = {10.1109/TASLP.2020.2973896},
abstract = {Speech technology plays an important role in our everyday life. Among others, speech is used for human-computer interaction, for instance for information retrieval and on-line shopping. In the case of an unwritten language, however, speech technology is unfortunately difficult to create, because it cannot be created by the standard combination of pre-trained speech-to-text and text-to-speech subsystems. The research presented in this article takes the first steps towards speech technology for unwritten languages. Specifically, the aim of this work was 1) to learn speech-to-meaning representations without using text as an intermediate representation, and 2) to test the sufficiency of the learned representations to regenerate speech or translated text, or to retrieve images that depict the meaning of an utterance in an unwritten language. The results suggest that building systems that go directly from speech-to-meaning and from meaning-to-speech, bypassing the need for text, is possible.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {964–975},
numpages = {12}
}

@article{10.1109/TASLP.2020.2969845,
author = {Gribben, Christopher and Lee, Hyunkook},
title = {The Perception of Band-Limited Decorrelation Between Vertically Oriented Loudspeakers},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2969845},
doi = {10.1109/TASLP.2020.2969845},
abstract = {Two experiments have been conducted to investigate the perceptual effect of band-limited interchannel decorrelation between vertically oriented loudspeakers. The perceived vertical image spread (VIS) and tonal quality (TQ) of phantom auditory images have been subjectively assessed in multiple comparison trials. The aim of the article was to find a lower decorrelation boundary that provides a significant increase of VIS, whilst maintaining TQ close to that of the original source. For test stimuli, decorrelation was applied to natural sound sources and pink noise in groups of octave-bands, where the lowest band was varied between 63 Hz and 8 kHz and the upper band was fixed at 16 kHz, resulting in eight decorrelated conditions for each source. Unprocessed octave-bands below the lower boundary were reproduced simultaneously through the lower main-layer loudspeaker only, and a monophonic main-layer only condition was also included in the comparison alongside the decorrelated stimuli. Results reveal that vertical decorrelation of the 500 Hz octave-band and above tends to significantly increase VIS, similar to that of broadband decorrelation, with little impact on TQ. In some cases, decorrelation of higher octave-bands and above can also produce similar increases of VIS with less impact on TQ, however, this is shown to be largely source-dependent. These results suggest that vertical decorrelation of lower frequencies has little perceptual benefit, and band-limiting vertical decorrelation to higher frequencies is likely to reduce low frequency phase cancellation. Applications of such an approach include 2D-to-3D upmixing and binaural audio rendering, with additional implications for 3D audio recording.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {876–888},
numpages = {13}
}

@article{10.1109/TASLP.2020.2964960,
author = {Liu, Yijia and Che, Wanxiang and Qin, Bing and Liu, Ting},
title = {Exploring Segment Representations for Neural Semi-Markov Conditional Random Fields},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2964960},
doi = {10.1109/TASLP.2020.2964960},
abstract = {Many problems in natural language processing (NLP) can be cast as the problem of segmenting a sequence. In this article, we combine the semi-Markov conditional random fields (semi-CRF) with neural networks to solve NLP segmentation problems. We focus on the segment representation in neural semi-CRF which is important to the performance. Based on our preliminary work in Liu <italic>et al.</italic> <xref ref-type="bibr" rid="ref1">[1]</xref>, we represent a segment by both encoding the subsequence and embedding the segment string. We conduct a systematic study of the utility of various components in subsequence encoding and propose a method of constructing and deriving segment string embeddings. Extensive experiments on three typical segmentation problems, namely, shallow syntax parsing, named entity recognition, and Chinese word segmentation are conducted. The results show that we can achieve equally-performed subsequence encoding with a three times faster concatenation network compared to previous work. The results also show that the segment string embeddings help our neural semi-CRF model to achieve a macro-averaged error reduction of 13.15% over a strong baseline using deep contextualized embeddings and bidirectional long-short-term memory CRF, which also show the usefulness of semi-CRF even with contextualized embeddings. These results are competitive with the state-of-the-art segmentation systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {813–824},
numpages = {12}
}

@article{10.1109/TASLP.2019.2962689,
author = {Laufer, Yaron and Laufer-Goldshtein, Bracha and Gannot, Sharon},
title = {ML Estimation and CRBs for Reverberation, Speech, and Noise PSDs in Rank-Deficient Noise Field},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2962689},
doi = {10.1109/TASLP.2019.2962689},
abstract = {Speech communication systems are prone to performance degradation in reverberant and noisy acoustic environments. Dereverberation and noise reduction algorithms typically require several model parameters, e.g. the speech, reverberation and noise power spectral densities (PSDs). A commonly used assumption is that the noise PSD matrix is known. However, in practical acoustic scenarios, the noise PSD matrix is unknown and should be estimated along with the speech and reverberation PSDs. In this article, we consider the case of rank-deficient noise PSD matrix, which arises when the noise signal consists of multiple directional noise sources, whose number is less than the number of microphones. We derive two closed-form maximum likelihood estimators (MLEs). The first is a non-blocking-based estimator which jointly estimates the speech, reverberation and noise PSDs, and the second is a blocking-based estimator, which first blocks the speech signal and then jointly estimates the reverberation and noise PSDs. Both estimators are analytically compared and analyzed, and mean square errors (MSEs) expressions are derived. Furthermore, Cramér-Rao Bounds (CRBs) on the estimated PSDs are derived. The proposed estimators are examined using both simulation and real reverberant and noisy signals, demonstrating the advantage of the proposed method compared to competing estimators.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {619–634},
numpages = {16}
}

@article{10.1109/TASLP.2020.2980974,
author = {Schymura, Christopher and Kolossa, Dorothea},
title = {Audiovisual Speaker Tracking Using Nonlinear Dynamical Systems With Dynamic Stream Weights},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2980974},
doi = {10.1109/TASLP.2020.2980974},
abstract = {Data fusion plays an important role in many technical applications that require efficient processing of multimodal sensory observations. A prominent example is audiovisual signal processing, which has gained increasing attention in automatic speech recognition, speaker localization and related tasks. If appropriately combined with acoustic information, additional visual cues can help to improve the performance in these applications, especially under adverse acoustic conditions. A dynamic weighting of acoustic and visual streams based on instantaneous sensor reliability measures is an efficient approach to data fusion in this context. This article presents a framework that extends the well-established theory of nonlinear dynamical systems with the notion of dynamic stream weights for an arbitrary number of sensory observations. It comprises a recursive state estimator based on the Gaussian filtering paradigm, which incorporates dynamic stream weights into a framework closely related to the extended Kalman filter. Additionally, a convex optimization approach to estimate oracle dynamic stream weights in fully observed dynamical systems utilizing a Dirichlet prior is presented. This serves as a basis for a generic parameter learning framework of dynamic stream weight estimators. The proposed system is application-independent and can be easily adapted to specific tasks and requirements. A study using audiovisual speaker tracking tasks is considered as an exemplary application in this work. An improved tracking performance of the dynamic stream-weight-based estimation framework over state-of-the-art methods is demonstrated in the experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1065–1078},
numpages = {14}
}

@article{10.1109/TASLP.2020.2987130,
author = {Ycart, Adrien and Benetos, Emmanouil},
title = {Learning and Evaluation Methodologies for Polyphonic Music Sequence Prediction With LSTMs},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2987130},
doi = {10.1109/TASLP.2020.2987130},
abstract = {Music language models play an important role for various music signal and symbolic music processing tasks, such as music generation, symbolic music classification, or automatic music transcription (AMT). In this article, we investigate Long Short-Term Memory (LSTM) networks for polyphonic music prediction, in the form of binary piano rolls. A preliminary experiment, assessing the influence of the timestep of piano rolls on system performance, highlights the need for more musical evaluation metrics. We introduce a range of metrics, focusing on temporal and harmonic aspects. We propose to combine them into a parametrisable loss to train our network. We then conduct a range of experiments with this new loss, both for polyphonic music prediction (intrinsic evaluation) and using our predictive model as a language model for AMT (extrinsic evaluation). Intrinsic evaluation shows that tuning the behaviour of a model is possible by adjusting loss parameters, with consistent results across timesteps. Extrinsic evaluation shows consistent behaviour across timesteps in terms of precision and recall with respect to the loss parameters, leading to an improvement in AMT performance without changing the complexity of the model. In particular, we show that intrinsic performance (in terms of cross entropy) is not related to extrinsic performance, highlighting the importance of using custom training losses for each specific application. Our model also compares favourably with previously proposed MLMs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1328–1341},
numpages = {14}
}

@article{10.1109/TASLP.2020.2985066,
author = {Kodrasi, Ina and Bourlard, Herv\'{e}},
title = {Spectro-Temporal Sparsity Characterization for Dysarthric Speech Detection},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2985066},
doi = {10.1109/TASLP.2020.2985066},
abstract = {To assist the clinical diagnosis and treatment of neurological diseases that cause speech dysarthria such as Parkinson's disease (PD), it is of paramount importance to craft robust features which can be used to automatically discriminate between healthy and dysarthric speech. Since dysarthric speech of patients suffering from PD is breathy, semi-whispery, and is characterized by abnormal pauses and imprecise articulation, it can be expected that its spectro-temporal sparsity differs from the spectro-temporal sparsity of healthy speech. While we have recently successfully used temporal sparsity characterization for dysarthric speech detection, characterizing spectral sparsity poses the challenge of constructing a valid feature vector from signals with a different number of unaligned time frames. Further, although several non-parametric and parametric measures of sparsity exist, it is unknown which sparsity measure yields the best performance in the context of dysarthric speech detection. The objective of this paper is to demonstrate the advantages of spectro-temporal sparsity characterization for automatic dysarthric speech detection. To this end, we first provide a numerical analysis of the suitability of different non-parametric and parametric measures (i.e., <inline-formula><tex-math notation="LaTeX">$l_1$</tex-math></inline-formula>-norm, kurtosis, Shannon entropy, Gini index, shape parameter of a Chi distribution, and shape parameter of a Weibull distribution) for sparsity characterization. It is shown that kurtosis, the Gini index, and the parametric sparsity measures are advantageous sparsity measures, whereas the <inline-formula><tex-math notation="LaTeX">$l_1$</tex-math></inline-formula>-norm and entropy measures fail to robustly characterize the temporal sparsity of signals with a different number of time frames. Second, we propose to characterize the spectral sparsity of an utterance by initially time-aligning it to the same utterance uttered by a (arbitrarily selected) reference speaker using dynamic time warping. Experimental results on a Spanish database of healthy and dysarthric speech show that estimating the spectro-temporal sparsity using the Gini index or the parametric sparsity measures and using it as a feature in a support vector machine results in a high classification accuracy of 83.3%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1210–1222},
numpages = {13}
}

@article{10.1109/TASLP.2020.2975902,
author = {Wang, Zhong-Qiu and Wang, DeLiang},
title = {Deep Learning Based Target Cancellation for Speech Dereverberation},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2975902},
doi = {10.1109/TASLP.2020.2975902},
abstract = {This article investigates deep learning based single- and multi-channel speech dereverberation. For single-channel processing, we extend magnitude-domain masking and mapping based dereverberation to complex-domain mapping, where deep neural networks (DNNs) are trained to predict the real and imaginary (RI) components of the direct-path signal from reverberant (and noisy) ones. For multi-channel processing, we first compute a minimum variance distortionless response (MVDR) beamformer to cancel the direct-path signal, and then feed the RI components of the cancelled signal, which is expected to be a filtered version of non-target signals, as additional features to perform dereverberation. Trained on a large dataset of simulated room impulse responses, our models show excellent speech dereverberation and recognition performance on the test set of the REVERB challenge, consistently better than single- and multi-channel weighted prediction error (WPE) algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {941–950},
numpages = {10}
}

@article{10.1109/TASLP.2019.2959251,
author = {Wang, Jin and Yu, Liang-Chih and Lai, K. Robert and Zhang, Xuejie},
title = {Tree-Structured Regional CNN-LSTM Model for Dimensional Sentiment Analysis},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2959251},
doi = {10.1109/TASLP.2019.2959251},
abstract = {Dimensional sentiment analysis aims to recognize continuous numerical values in multiple dimensions such as the valence-arousal (VA) space. Compared to the categorical approach that focuses on sentiment classification such as binary classification (i.e., positive and negative), the dimensional approach can provide a more fine-grained sentiment analysis. This article proposes a tree-structured regional CNN-LSTM model consisting of two parts: regional CNN and LSTM to predict the VA ratings of texts. Unlike a conventional CNN which considers a whole text as input, the proposed regional CNN uses a part of the text as a region, dividing an input text into several regions such that the useful affective information in each region can be extracted and weighted according to their contribution to the VA prediction. Such regional information is sequentially integrated across regions using LSTM for VA prediction. By combining the regional CNN and LSTM, both local (regional) information within sentences and long-distance dependencies across sentences can be considered in the prediction process. To further improve performance, a region division strategy is proposed to discover task-relevant phrases and clauses to incorporate structured information into VA prediction. Experimental results on different corpora show that the proposed method outperforms lexicon-, regression-, conventional NN and other structured NN methods proposed in previous studies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {581–591},
numpages = {11}
}

@article{10.1109/TASLP.2019.2957872,
author = {Yu, Jianfei and Jiang, Jing and Xia, Rui},
title = {Entity-Sensitive Attention and Fusion Network for Entity-Level Multimodal Sentiment Classification},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2957872},
doi = {10.1109/TASLP.2019.2957872},
abstract = {Entity-level (aka target-dependent) sentiment analysis of social media posts has recently attracted increasing attention, and its goal is to predict the sentiment orientations over individual target entities mentioned in users’ posts. Most existing approaches to this task primarily rely on the textual content, but fail to consider the other important data sources (e.g., images, videos, and user profiles), which can potentially enhance these text-based approaches. Motivated by the observation, we study <italic>entity-level multimodal sentiment classification</italic> in this article, and aim to explore the usefulness of images for entity-level sentiment detection in social media posts. Specifically, we propose an Entity-Sensitive Attention and Fusion Network (ESAFN) for this task. First, to capture the intra-modality dynamics, ESAFN leverages an effective attention mechanism to generate entity-sensitive textual representations, followed by aggregating them with a textual fusion layer. Next, ESAFN learns the entity-sensitive visual representation with an entity-oriented visual attention mechanism, followed by a gated mechanism to eliminate the noisy visual context. Moreover, to capture the inter-modality dynamics, ESAFN further fuses the textual and visual representations with a bilinear interaction layer. To evaluate the effectiveness of ESAFN, we manually annotate the sentiment orientation over each given entity based on two recently released multimodal NER datasets, and show that ESAFN can significantly outperform several highly competitive unimodal and multimodal methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {429–439},
numpages = {11}
}

@article{10.1109/TASLP.2020.2971417,
author = {Perrotin, Olivier and McLoughlin, Ian V.},
title = {Glottal Flow Synthesis for Whisper-to-Speech Conversion},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2971417},
doi = {10.1109/TASLP.2020.2971417},
abstract = {Whisper-to-speech conversion is motivated by laryngeal disorders, in which malfunction of the vocal folds leads to loss of voicing. Many patients with laryngeal disorders can still produce functional whispers, since these are characterised by the absence of vocal fold vibration. Whispers therefore constitute a common ground for speech rehabilitation across many kinds of laryngeal disorder. Whisper-to-speech conversion involves recreating natural-sounding speech from recorded whispers, and is a non-invasive and non-surgical rehabilitation that can maintain a natural method of speaking, unlike the existing methods of rehabilitation. This article proposes a new rule-based method for whisper-to-speech conversion that replaces the noisy whisper sound source with a synthesised speech-like harmonic source, while maintaining the vocal tract component unaltered. In particular, a novel glottal source generator is developed in which whisper information is used to parameterise the excitation through a high-quality glottis model. Evaluation of the system against the standard pulse train excitation method reveals significantly improved performance. Since our method is glottis-based, it is potentially compatible with the many existing vocal tract component adaptation systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {889–900},
numpages = {12}
}

@article{10.1109/TASLP.2019.2959224,
author = {Nishimura, Yuta and Sudoh, Katsuhito and Neubig, Graham and Nakamura, Satoshi},
title = {Multi-Source Neural Machine Translation With Missing Data},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2959224},
doi = {10.1109/TASLP.2019.2959224},
abstract = {Machine translation is rife with ambiguities in word ordering and word choice, and even with the advent of machine-learning methods that learn to resolve this ambiguity based on statistics from large corpora, mistakes are frequent. <italic>Multi-source translation</italic> is an approach that attempts to resolve these ambiguities by exploiting multiple inputs (e.g. sentences in three different languages) to increase translation accuracy. These methods are trained on multilingual corpora, which include the multiple source languages and the target language, and then at test time uses information from both source languages while generating the target. While there are many of these multilingual corpora, such as multilingual translations of TED talks or European parliament proceedings, in practice, many multilingual corpora are not complete due to the difficulty to provide translations in <italic>all</italic> of the relevant languages. Existing studies on multi-source translation did not explicitly handle such situations, and thus are only applicable to complete corpora that have all of the languages of interest, severely limiting their practical applicability. In this article, we examine approaches for multi-source neural machine translation (NMT) that can learn from and translate such incomplete corpora. Specifically, we propose methods to deal with incomplete corpora at both training time and test time. For training time, we examine two methods: (1) a simple method that simply replaces missing source translations with a special NULL symbol, and (2) a data augmentation approach that fills in incomplete parts with source translations created from multi-source NMT. For test-time, we examine methods that use multi-source translation even when only a single source is provided by first translating into an additional auxiliary language using standard NMT, then using multi-source translation on the original source and this generated auxiliary language sentence. Extensive experiments demonstrate that the proposed training-time and test-time methods both significantly improve translation performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {569–580},
numpages = {12}
}

@article{10.1109/TASLP.2019.2950596,
author = {Parcollet, Titouan and Morchid, Mohamed and Bost, Xavier and Linar\`{e}s, Georges and De Mori, Renato},
title = {Real to H-Space Autoencoders for Theme Identification in Telephone Conversations},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2950596},
doi = {10.1109/TASLP.2019.2950596},
abstract = {Machine learning (ML) and deep learning with deep neural networks (DNN), have drastically improved the performances of modern systems on numerous spoken language understanding (SLU) related tasks. Since most of current researches focus on new neural architectures to enhance the performances in realistic conditions, few recent works investigated the use of different algebras with neural networks (NN), to better represent the nature of the data being processed. To this extent, quaternion-valued neural networks (QNN) have shown better performances, and an important reduction of the number of neural parameters compared to traditional real-valued neural networks, when dealing with multidimensional signal. Nonetheless, the use of QNNs is strictly limited to quaternion input or output features. This article introduces a new unsupervised method based on a hybrid autoencoder (AE) called real-to-quaternion autoencoder (R2H), to extract a quaternion-valued input signal from any real-valued data, to be processed by QNNs. The experiments performed to identify the most related theme of a given telephone conversation from a customer care service (CCS), demonstrate that the R2H approach outperforms all the previously established models, either real- or quaternion-valued ones, in term of accuracy and with up to four times fewer neural parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {198–210},
numpages = {13}
}

@article{10.1109/TASLP.2020.2982799,
author = {Zhang, Qiaoling and Xu, Weiqiang and Zhang, Weiwei and Feng, Jie and Chen, Zhiyong},
title = {Multi-Hypothesis Square-Root Cubature Kalman Particle Filter for Speaker Tracking in Noisy and Reverberant Environments},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2982799},
doi = {10.1109/TASLP.2020.2982799},
abstract = {In this paper, a multi-hypothesis square-root cubature Kalman particle filter (MH-SRCKPF) is proposed for speaker tracking in noisy and reverberant environments with distributed microphone arrays. The conventional cubature Kalman particle filter (CKPF) uses the cubature Kalman filter (CKF) to generate its proposal for particle sampling. Such a proposal incorporates only one observation from a certain localization function for the state estimation, which is vulnerable to noise or reverberation, yielding the degraded tracking performance. To tackle the problem, by incorporating multiple possible observations into CKF for the proposal, a multi-hypothesis CKPF (MH-CKPF) algorithm is first developed. Furthermore, to improve the numerical stability, an MH-SRCKPF algorithm is developed, where the state estimate and the square root of the error covariance are propagated at each time. Finally, the MH-SRCKPF is applied to the speaker tracking problems in distributed microphone arrays. Experimental results demonstrate that the proposed MH-SRCKPF outperforms the competing methods in the presence of noise and reverberation. Meanwhile, by propagating the square root of the state covariance, the proposed method exhibits attractive numerical characteristics.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {1183–1197},
numpages = {15}
}

@article{10.1109/TASLP.2020.2969779,
author = {Aroudi, Ali and Doclo, Simon},
title = {Cognitive-Driven Binaural Beamforming Using EEG-Based Auditory Attention Decoding},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2020.2969779},
doi = {10.1109/TASLP.2020.2969779},
abstract = {Identifying the target speaker in hearing aid applications is an essential ingredient to improve speech intelligibility. Recently, a least-squares-based auditory attention decoding (AAD) method has been proposed to identify the target speaker from single-trial EEG recordings in an acoustic scenario with two competing speakers. Aiming at enhancing the target speaker and suppressing the interfering speaker and ambient noise, in this article, we propose a cognitive-driven speech enhancement system, consisting of a binaural beamformer which is steered based on AAD and estimated relative transfer function (RTF) vectors, which require estimates of the direction-of-arrivals (DOAs) of both speakers. For binaural beamforming and to generate reference signals for AAD, we consider either minimum-variance-distortionless-response (MVDR) beamformers or linearly-constrained-minimum-variance (LCMV) beamformers. Contrary to the binaural MVDR beamformer, the binaural LCMV beamformer allows to preserve the spatial impression of the acoustic scene and to control the suppression of the interfering speaker, which is important when intending to switch attention between speakers. The speech enhancement performance of the proposed system is evaluated in terms of the binaural signal-to-interference-plus-noise ratio (<inline-formula><tex-math notation="LaTeX">$text {SINR}$</tex-math></inline-formula>) improvement in anechoic and reverberant conditions. Furthermore, we investigate the impact of RTF and DOA estimation errors and AAD errors on the speech enhancement performance. The experimental results show that the proposed system using LCMV beamformers yields a larger decoding performance and binaural <inline-formula><tex-math notation="LaTeX">$text {SINR}$</tex-math></inline-formula> improvement compared to using MVDR beamformers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {862–875},
numpages = {14}
}

@article{10.1109/TASLP.2019.2948773,
author = {Shin, Youhyun and Lee, Sang-goo},
title = {Learning Context Using Segment-Level LSTM for Neural Sequence Labeling},
year = {2020},
issue_date = {2020},
publisher = {IEEE Press},
volume = {28},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2948773},
doi = {10.1109/TASLP.2019.2948773},
abstract = {This article introduces an approach that learns segment-level context for sequence labeling in natural language processing (NLP). Previous approaches limit their basic unit to a word for feature extraction because sequence labeling is a token-level task in which labels are annotated word-by-word. However, the text segment is an ultimate unit for labeling, and we are easily able to obtain segment information from annotated labels in a IOB/IOBES format. Most neural sequence labeling models expand their learning capacity by employing additional layers, such as a character-level layer, or jointly training NLP tasks with common knowledge. The architecture of our model is based on the charLSTM-BiLSTM-CRF model, and we extend the model with an additional segment-level layer called segLSTM. We therefore suggest a sequence labeling algorithm called charLSTM-BiLSTM-CRF-segLSTM<inline-formula><tex-math notation="LaTeX">$^{sLM}$</tex-math></inline-formula> which employs an additional segment-level long short-term memory (LSTM) that trains features by learning adjacent context in a segment. We demonstrate the performance of our model on four sequence labeling datasets, namely, Peen Tree Bank, CoNLL 2000, CoNLL 2003, and OntoNotes 5.0. Experimental results show that our model performs better than state-of-the-art variants of BiLSTM-CRF. In particular, the proposed model enhances the performance of tasks for finding appropriate labels of multiple token segments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {105–115},
numpages = {11}
}

@article{10.1109/T

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr