text:
octave power spectra of long-term constant-Q transform (CQT) for spoofing attack detection. A novel framework based on multi-level transform (MLT) is proposed that can capture the relevant information from octave power spectra using level by level in a multi-level manner. We then derive a novel feature referred to as constant-Q multi-level coefficient (CMC) based on proposed MLT. The proposed feature is evaluated on synthetic as well as replay speech detection studies on ASVspoof 2015 and ASVspoof 2017 version 2.0 database, respectively. We find the proposed CMC feature outperforms the conventional constant-Q cepstral coefficient based long-term feature obtained from linear power spectrum after uniform resampling. This depicts the usefulness of MLT to extract salient artifacts from octave power spectrum. Further, the proposed CMC feature performs better than the existing the well known other state-of-the-art systems for spoofing attack detection that showcases its importance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2373–2384},
numpages = {12}
}

@article{10.1109/TASLP.2019.2945485,
author = {Tiwari, Nitya and Pandey, Prem C.},
title = {Speech Enhancement Using Noise Estimation With Dynamic Quantile Tracking},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2945485},
doi = {10.1109/TASLP.2019.2945485},
abstract = {A technique for quantile-based noise estimation is presented for single-input speech enhancement in hearing aids and speech communication devices. The noise spectrum is updated by dynamic tracking of quantiles of the samples of the magnitude spectrum of the noisy speech without sorting of the past samples. Another technique is presented for improved tracking of nonstationary noise using adaptive quantiles, which are calculated by estimation of the quantile functions. The two noise estimation techniques are compared with some of the earlier techniques in terms of computational requirement, error in noise tracking, and speech enhancement using spectral subtraction based on the geometric approach. The technique with fixed quantiles has the lowest computational requirement and its performance in terms of noise tracking and speech enhancement for different SNRs and noise types is found to be better than or comparable to the earlier techniques. The technique with adaptive quantiles, having a higher computational requirement, provides better performance at low SNRs and for nonstationary noises.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2301–2312},
numpages = {12}
}

@article{10.1109/TASLP.2019.2944568,
author = {Barkan, Oren and Tsiris, David and Katz, Ori and Koenigstein, Noam},
title = {InverSynth: Deep Estimation of Synthesizer Parameter Configurations From Audio Signals},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2944568},
doi = {10.1109/TASLP.2019.2944568},
abstract = {Sound synthesis is a complex field that requires domain expertise. Manual tuning of synthesizer parameters to match a specific sound can be an exhaustive task, even for experienced sound engineers. In this paper, we introduce InverSynth - an automatic method for synthesizer parameters tuning to match a given input sound. InverSynth is based on strided convolutional neural networks and is capable of inferring the synthesizer parameters configuration from the input spectrogram and even from the raw audio. The effectiveness InverSynth is demonstrated on a subtractive synthesizer with four frequency modulated oscillators, envelope generator and a gater effect. We present extensive quantitative and qualitative results that showcase the superiority InverSynth over several baselines. Furthermore, we show that the network depth is an important factor that contributes to the prediction accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2385–2396},
numpages = {12}
}

@article{10.1109/TASLP.2019.2933325,
author = {Das, Amit and Li, Jinyu and Ye, Guoli and Zhao, Rui and Gong, Yifan},
title = {Advancing Acoustic-to-Word CTC Model With Attention and Mixed-Units},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2933325},
doi = {10.1109/TASLP.2019.2933325},
abstract = {The acoustic-to-word model based on the Connectionist Temporal Classification CTC criterion is a natural end-to-end E2E system directly targeting word as output unit. Two issues exist in the system: first, the current output of the CTC model relies on the current input and does not account for context weighted inputs. This is the hard alignment issue. Second, the word-based CTC model suffers from the out-of-vocabulary OOV issue. This means it can model only frequently occurring words while tagging the remaining words as OOV. Hence, such a model is limited in its capacity in recognizing only a fixed set of frequent words. In this study, we propose addressing these problems using a combination of attention mechanism and mixed-units. In particular, we introduce Attention CTC, Self-Attention CTC, Hybrid CTC, and Mixed-unit CTC. First, we blend attention modeling capabilities directly into the CTC network using Attention CTC and Self-Attention CTC. Second, to alleviate the OOV issue, we present Hybrid CTC which uses a word and letter CTC with shared hidden layers. The Hybrid CTC consults the letter CTC when the word CTC emits an OOV. Then, we propose a much better solution by training a Mixed-unit CTC which decomposes all the OOV words into sequences of frequent words and multi-letter units. Evaluated on a 3400 hours Microsoft Cortana voice assistant task, our final acoustic-to-word solution using attention and mixed-units achieves a relative reduction in word error rate WER over the vanilla word CTC by 12.09%. Such an E2E model without using any language model LM or complex decoder also outperforms a traditional context-dependent CD phoneme CTC with strong LM and decoder by 6.79% relative.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {1880–1892},
numpages = {13}
}

@article{10.1109/TASLP.2019.2941587,
author = {Liu, Xuebo and Wong, Derek F. and Chao, Lidia S. and Liu, Yang},
title = {Latent Attribute Based Hierarchical Decoder for Neural Machine Translation},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2941587},
doi = {10.1109/TASLP.2019.2941587},
abstract = {Neural machine translation NMT has achieved state-of-the-art performance in many translation tasks. However, because the computational cost increases with the size of the search space for predicting the target words, the translation quality of NMT is constrained by the limited vocabulary. To alleviate this problem, we propose a novel dynamic hierarchical decoder for NMT to utilize all of the target words in the training and decoding process. In the proposed model, a target word is represented by two latent attribute vectors rather than a word vector. The model is trained to dynamically put together those words that share similar linguistic attributes. The prediction of a target word is, therefore, turned into the prediction of attribute vectors, where the $mathrm{softmax}$ functions are performed at the attribute level. This greatly reduces the model size and the decoding time. Our experimental results demonstrate that the proposed model significantly outperforms the NMT baselines in both Chinese-English and English-German translation tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2103–2112},
numpages = {10}
}

@article{10.1109/TASLP.2019.2946043,
author = {Remaggi, Luca and Jackson, Philip J. B. and Wang, Wenwu},
title = {Modeling the Comb Filter Effect and Interaural Coherence for Binaural Source Separation},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2946043},
doi = {10.1109/TASLP.2019.2946043},
abstract = {Typical methods for binaural source separation consider only the direct sound as the target signal in a mixture. However, in most scenarios, this assumption limits the source separation performance. It is well known that the early reflections interact with the direct sound, producing acoustic effects at the listening position, e.g. the so-called comb filter effect. In this article, we propose a novel source separation model, that utilizes both the direct sound and the first early reflection information to model the comb filter effect. This is done by observing the interaural phase difference obtained from the time-frequency representation of binaural mixtures. Furthermore, a method is proposed to model the interaural coherence of the signals. Including information related to the sound multipath propagation, the performance of the proposed separation method is improved with respect to the baselines that did not use such information, as illustrated by using binaural recordings made in four rooms, having different sizes and reverberation times.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2263–2277},
numpages = {15}
}

@article{10.1109/TASLP.2019.2944563,
author = {Zhao, Lujun and Qiu, Xipeng and Zhang, Qi and Huang, Xuanjing},
title = {Sequence Labeling With Deep Gated Dual Path CNN},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2944563},
doi = {10.1109/TASLP.2019.2944563},
abstract = {Sequence labeling, such as part-of-speech (POS) tagging, named entity recognition (NER), text chunking, is a classic task in natural language processing. Most existing neural networks models for sequence labeling are based on recurrent neural networks. Recently, convolutional neural networks have been proposed to replace the recurrent components for sequence labeling. However, they are usually shallow compared to deep convolutional networks that achieve start-of-the-art performance in other fields. Due to the vanishing gradient problem, these models usually can not work well when simply increasing the number of layers. In this paper, we propose using deep CNN architecture in sequence labeling, which can capture a large context through stacked convolutions. To reduce the vanishing gradient problem, the proposed method incorporates gated linear units, residual connections, and dense connections. Experimental results on three sequence labeling tasks show that the proposed model can achieve competitive performance to the RNN-based state-of-the-art method while maintaining <inline-formula><tex-math notation="LaTeX">$2.41times$</tex-math></inline-formula> faster speed, even with up to 10 convolutional layers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2326–2335},
numpages = {10}
}

@article{10.1109/TASLP.2019.2944078,
author = {Guo, Qipeng and Qiu, Xipeng and Xue, Xiangyang and Zhang, Zheng},
title = {Low-Rank and Locality Constrained Self-Attention for Sequence Modeling},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2944078},
doi = {10.1109/TASLP.2019.2944078},
abstract = {Self-attention mechanism becomes more and more popular in natural language processing (NLP) applications. Recent studies show the Transformer architecture which relies mainly on the attention mechanism achieves much success on large datasets. But a raised problem is its generalization ability is weaker than CNN and RNN on many moderate-sized datasets. We think the reason can be attributed to its unsuitable inductive bias of the self-attention structure. In this paper, we regard the self-attention as matrix decomposition problem and propose an improved self-attention module by introducing two linguistic constraints: low-rank and locality. We further develop the low-rank attention and band attention to parameterize the self-attention mechanism under the low-rank and locality constraints. Experiments on several real NLP tasks show our model outperforms the vanilla Transformer and other self-attention models on moderate size datasets. Additionally, evaluation on a synthetic task gives us a more detailed understanding of working mechanisms of different architectures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2213–2222},
numpages = {10}
}

@article{10.1109/TASLP.2019.2942160,
author = {Ma, Qianli and Yu, Liuhong and Tian, Shuai and Chen, Enhuan and Ng, Wing W. Y.},
title = {Global-Local Mutual Attention Model for Text Classification},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2942160},
doi = {10.1109/TASLP.2019.2942160},
abstract = {Text classification is a central field of inquiry in natural language processing NLP. Although some models learn local semantic features and global long-term dependencies simultaneously, they simply combine them through concatenation either in a cascade way or in parallel while mutual effects between them are ignored. In this paper, we propose the Global-Local Mutual Attention GLMA model for text classification problems, which introduces a mutual attention mechanism for mutual learning between local semantic features and global long-term dependencies. The mutual attention mechanism consists of a Local-Guided Global-Attention LGGA and a Global-Guided Local-Attention GGLA. The LGGA allows to assign weights and combine global long-term dependencies of word positions that are semantic related. It captures combined semantics and alleviates the gradient vanishing problem. The GGLA automatically assigns more weights to relevant local semantic features, which captures key local semantic information and filters both noises and irrelevant words/phrases. Furthermore, a weighted-over-time pooling operation is developed to aggregate the most informative and discriminative features for classification. Extensive experiments demonstrate that our model obtains the state-of-the-art performance on seven benchmark datasets and sixteen Amazon product reviews datasets. Both the result analysis and the mutual attention weights visualization further demonstrate the effectiveness of the proposed model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2127–2139},
numpages = {13}
}

@article{10.1109/TASLP.2019.2937953,
author = {Feng, Siyuan and Lee, Tan},
title = {Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2937953},
doi = {10.1109/TASLP.2019.2937953},
abstract = {This research addresses the problem of acoustic modeling of low-resource languages for which transcribed training data is absent. The goal is to learn robust frame-level feature representations that can be used to identify and distinguish subword-level speech units. The proposed feature representations comprise various types of multilingual bottleneck features BNFs that are obtained via multi-task learning of deep neural networks MTL-DNN. One of the key problems is how to acquire high-quality frame labels for untranscribed training data to facilitate supervised DNN training. It is shown that learning of robust BNF representations can be achieved by effectively leveraging transcribed speech data and well-trained automatic speech recognition ASR systems from one or more out-of-domain resource-rich languages. Out-of-domain ASR systems can be applied to perform speaker adaptation with untranscribed training data of the target language, and to decode the training speech into frame-level labels for DNN training. It is also found that better frame labels can be generated by considering temporal dependency in speech when performing frame clustering. The proposed methods of feature learning are evaluated on the standard task of unsupervised subword modeling in Track 1 of the ZeroSpeech 2017 Challenge. The best performance achieved by our system is $9.7%$ in terms of across-speaker triphone minimal-pair ABX error rate, which is comparable to the best systems reported recently. Lastly, our investigation reveals that the closeness between target languages and out-of-domain languages and the amount of available training data for individual target languages could have significant impact on the goodness of learned features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2000–2011},
numpages = {12}
}

@article{10.1109/TASLP.2019.2943018,
author = {Liu, Dayiheng and Fu, Jie and Qu, Qian and Lv, Jiancheng},
title = {BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2943018},
doi = {10.1109/TASLP.2019.2943018},
abstract = {Incorporating prior knowledge like <italic>lexical constraints</italic> into the model’s output to generate meaningful and coherent sentences has many applications in dialogue system, machine translation, image captioning, etc. However, existing auto-regressive models incrementally generate sentences from left to right via beam search, which makes it difficult to directly introduce lexical constraints into the generated sentences. In this paper, we propose a new algorithmic framework, dubbed BFGAN, to address this challenge. Specifically, we employ a backward generator and a forward generator to generate lexically constrained sentences together, and use a discriminator to guide the joint training of two generators by assigning them reward signals. Due to the difficulty of BFGAN training, we propose several training techniques to make the training process more stable and efficient. Our extensive experiments on three large-scale datasets with human evaluation demonstrate that BFGAN has significant improvements over previous methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2350–2361},
numpages = {12}
}

@article{10.1109/TASLP.2019.2945479,
author = {Ben-Hur, Zamir and Alon, David Lou and Mehra, Ravish and Rafaely, Boaz},
title = {Efficient Representation and Sparse Sampling of Head-Related Transfer Functions Using Phase-Correction Based on Ear Alignment},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2945479},
doi = {10.1109/TASLP.2019.2945479},
abstract = {With the proliferation of high quality virtual reality systems, the demand for high fidelity spatial audio reproduction has grown. This requires individual head-related transfer functions (HRTFs) with high spatial resolution. Acquiring such HRTFs is not always possible, which motivates the need for sparsely sampled HRTFs. Additionally, real-time applications require compact representation of HRTFs. Recently, spherical-harmonics (SH) has been suggested for efficient interpolation and representation of HRTFs. However, representation of sparse HRTFs with a limited SH order may introduce spatial aliasing and truncation errors, which have a detrimental effect on the reproduced spatial audio. This is because the HRTF is inherently of a high spatial order. One approach to overcome this limitation is to pre-process the HRTF, with the aim of reducing its effective SH order. A recent study showed that order-reduction can be achieved by time-alignment of HRTFs, through numerical estimation of the time delays of the HRTFs. In this paper, a new method for pre-processing HRTFs in order to reduce their effective order is presented. The method uses phase-correction based on ear alignment, by exploiting the dual-centering nature of HRTF measurements. In contrast to time-alignment, the phase-correction is performed parametrically, making it more robust to measurement noise. The SH order reduction and ensuing interpolation errors due to sparse sampling were analyzed for these two methods. Results indicate significant reduction in the effective SH order, where only 100 measurements and order 6 are required to achieve a normalized mean square error below <inline-formula><tex-math notation="LaTeX">$-$</tex-math></inline-formula>10 dB compared to a fully-sampled, high-order HRTF.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2249–2262},
numpages = {14}
}

@article{10.1109/TASLP.2019.2947364,
author = {Duan, Junwen and Ding, Xiao and Zhang, Yue and Liu, Ting},
title = {TEND: A Target-Dependent Representation Learning Framework for News Document},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2947364},
doi = {10.1109/TASLP.2019.2947364},
abstract = {Real-time news documents published on the Internet have global financial and political impacts. Pioneering statistical approaches investigate manually defined features to capture lexical, sentiment, and event information, which suffer from feature sparsity. As a remedy, recent work has considered learning dense vector representations for documents. Such representations are general, which can not model target-dependent scenarios, such as stance detection towards a specific claim. There has been work on target-specific word and sentence representations, but little was done on target-dependent document representation. Moreover, documents contain more potentially helpful information, but also noise compared to events and sentences. To address the above issues, we focus on models that are: 1. <italic>task-driven</italic>, which optimize the neural network representations for the end task; 2. <italic>target-specific</italic>, learning news representations by considering the influence of specific targets. In particular, we propose a novel document-level target-dependent learning framework TEND. The framework employs the information of the target and the news abstract as clues, obtaining relatively informative sentences from the entire document for our objectives. The framework assembles a document representation by integrating the news abstract representation and a weighted sum of sentence representations in the document. To the best of our knowledge, we are among the first to investigate target-dependent document representation. Existing text representation models can be easily integrated into our TEND framework, and it is general enough to be applied to different target-dependent document representation tasks. We empirically evaluate our framework on two target-dependent document-level tasks, including a cumulative abnormal return prediction task and a news stance detection task. Results show that our models give the best performances compared to state-of-the-art document embedding methods, yielding robust and consistent performances across datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2313–2325},
numpages = {13}
}

@article{10.1109/TASLP.2019.2939782,
author = {Varanasi, Vishnuvardhan and Agarwal, Ayushya and Hegde, Rajesh M.},
title = {Near-Field Acoustic Source Localization Using Spherical Harmonic Features},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2939782},
doi = {10.1109/TASLP.2019.2939782},
abstract = {Near-field acoustic source localization and beamforming has hitherto not been investigated extensively in the spherical harmonic domain under reverberant conditions. In this paper, a novel method for the near-field direction of arrival DOA and range estimation using signal invariant and direction independent spherical harmonic features is proposed. A spatial pressure interpolation method that effectively captures the acoustic energy on the surface of the sphere is first developed in the spherical harmonic domain. Near-field DOA estimates are then computed using this pressure distribution. Spherical harmonic features that are signal invariant and direction independent are then extracted using the near field DOA estimates. Signal invariant features are obtained by normalizing spherical harmonic coefficients with a component that is proportional to the source signal strength. Direction independent features are obtained using two methods. Rotation of spherical harmonic functions over a sphere is performed using Wigner-D functions in one method, whereas in the other, the effect of DOA is compensated by spherical harmonic normalization. Using the signal invariant and direction independent features, a learning-based framework which utilizes a convolutional neural network and voicing activity detection is also developed to compute the range of the near-field source. Experiments are conducted both on simulated and real speech data for evaluating the performance of the proposed spherical harmonic features in the context of near-field localization as well as beamforming. Root mean square error of both near-field DOA and source range estimates are obtained. Objective evaluation of near-field beamformed acoustic outputs is also performed. Results obtained are motivating enough for the method to be used in practical near-field beamforming applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2054–2066},
numpages = {13}
}

@article{10.1109/TASLP.2019.2940662,
author = {Tu, Yan-Hui and Du, Jun and Lee, Chin-Hui},
title = {Speech Enhancement Based on Teacher–Student Deep Learning Using Improved Speech Presence Probability for Noise-Robust Speech Recognition},
year = {2019},
issue_date = {Dec. 2019},
publisher = {IEEE Press},
volume = {27},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2940662},
doi = {10.1109/TASLP.2019.2940662},
abstract = {In this paper, we propose a novel teacher-student learning framework for the preprocessing of a speech recognizer, leveraging the online noise tracking capabilities of improved minima controlled recursive averaging IMCRA and deep learning of nonlinear interactions between speech and noise. First, a teacher model with deep architectures is built to learn the target of ideal ratio masks IRMs using simulated training pairs of clean and noisy speech data. Next, a student model is trained to learn an improved speech presence probability by incorporating the estimated IRMs from the teacher model into the IMCRA approach. The student model can be compactly designed in a causal processing mode having no latency with the guidance of a complex and noncausal teacher model. Moreover, the clean speech requirement, which is difficult to meet in real-world adverse environments, can be relaxed for training the student model, implying that noisy speech data can be directly used to adapt the regression-based enhancement model to further improve speech recognition accuracies for noisy speech collected in such conditions. Experiments on the CHiME-4 challenge task show that our best student model with bidirectional gated recurrent units BGRUs can achieve a relative word error rate WER reduction of 18.85% for the real test set when compared to unprocessed system without acoustic model retraining. However, the traditional teacher model degrades the performance of the unprocessed system in this case. In addition, the student model with a deep neural network DNN in causal mode having no latency yields a relative WER reduction of 7.94% over the unprocessed system with 670 times less computing cycles when compared to the BGRU-equipped student model. Finally, the conventional speech enhancement and IRM-based deep learning method destroyed the ASR performance when the recognition system became more powerful. While our proposed approach could still improve the ASR performance even in the more powerful recognition system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2080–2091},
numpages = {12}
}

@article{10.1109/TASLP.2019.2933326,
author = {Deng, Dong and Jing, Liping and Yu, Jian and Sun, Shaolong},
title = {Sparse Self-Attention LSTM for Sentiment Lexicon Construction},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2933326},
doi = {10.1109/TASLP.2019.2933326},
abstract = {Sentiment lexicon is a very important resource for opinion mining. Recently, many state-of-the-art works employ deep learning techniques to construct sentiment lexicons. In general, they firstly learn sentiment-aware word embeddings, and then use it as word features to construct sentiment lexicons. However, these methods do not consider the importance of each word to the distinguish of documents’ sentiment polarities. As we know, most words among a document do not contribute to understand documents’ semantic or sentiment. For example, in the tweet It's a good day, but i can't feel it. I'm really unhappy. The words ‘unhappy’, ‘feel’ and ‘can't’ are much more important than the words ‘good’, ‘day’ in predicting the sentiment polarity of this twitter. Meanwhile, many words, such as ‘the’, ‘in’, ‘it’ and ‘I'm’ are uninformative. In this paper, we propose a novel sparse self-attention LSTM SSALSTM to efficiently capture the above intuitive facts, and then construct a large scale sentiment lexicons in twitter. In SSALSTM, we use a novel self-attention mechanism to capture the importance of each words to the distinguish of documents’ sentiment polarities. In addition, a $L_1$ regularize is applied in the attentions which can ensure the sparsity characters that most words in a document are semantic and sentiment indistinguishable. Once we learn an efficient sentiment-aware word embedding, we train a classifier which uses sentiment-aware word embedding as features to predict the sentiment polarities of words. Extensive experiments on four publicly available datasets, SemEval 2013–2016, indicate that the sentiment lexicon generated by our proposed model achieves state-of-the-art performance on both supervised and unsupervised sentiment classification tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1777–1790},
numpages = {14}
}

@article{10.1109/TASLP.2019.2933698,
author = {Elshamy, Samy and Fingscheidt, Tim},
title = {DNN-Based Cepstral Excitation Manipulation for Speech Enhancement},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2933698},
doi = {10.1109/TASLP.2019.2933698},
abstract = {This contribution aims at speech model-based speech enhancement by exploiting the source-filter model of human speech production. The proposed method enhances the excitation signal in the cepstral domain by making use of a deep neural network DNN. We investigate two types of target representations along with the significant effects of their normalization. The new approach exceeds the performance of a formerly introduced classical signal processing-based cepstral excitation manipulation CEM method in terms of noise attenuation by about 1.5 dB. We show that this gain also holds true when comparing serial combinations of envelope and excitation enhancement. In the important low-SNR conditions, no significant trade-off for speech component quality or speech intelligibility is induced, while allowing for substantially higher noise attenuation. In total, a traditional purely statistical state-of-the-art speech enhancement system is outperformed by more than 3 dB noise attenuation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1803–1814},
numpages = {12}
}

@article{10.1109/TASLP.2019.2934319,
author = {Delfarah, Masood and Wang, DeLiang},
title = {Deep Learning for Talker-Dependent Reverberant Speaker Separation: An Empirical Study},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2934319},
doi = {10.1109/TASLP.2019.2934319},
abstract = {Speaker separation refers to the problem of separating speech signals from a mixture of simultaneous speakers. Previous studies are limited to addressing the speaker separation problem in anechoic conditions. This paper addresses the problem of talker-dependent speaker separation in reverberant conditions, which are characteristic of real-world environments. We employ recurrent neural networks with bidirectional long short-term memory BLSTM to separate and dereverberate the target speech signal. We propose two-stage networks to effectively deal with both speaker separation and speech dereverberation. In the two-stage model, the first stage separates and dereverberates two-talker mixtures and the second stage further enhances the separated target signal. We have extensively evaluated the two-stage architecture, and our empirical results demonstrate large improvements over unprocessed mixtures and clear performance gain over single-stage networks in a wide range of target-to-interferer ratios and reverberation times in simulated as well as recorded rooms. Moreover, we show that time-frequency masking yields better performance than spectral mapping for reverberant speaker separation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1839–1848},
numpages = {10}
}

@article{10.1109/TASLP.2019.2925934,
author = {Xie, Yue and Liang, Ruiyu and Liang, Zhenlin and Huang, Chengwei and Zou, Cairong and Schuller, Bjorn},
title = {Speech Emotion Classification Using Attention-Based LSTM},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2925934},
doi = {10.1109/TASLP.2019.2925934},
abstract = {Automatic speech emotion recognition has been a research hotspot in the field of human–computer interaction over the past decade. However, due to the lack of research on the inherent temporal relationship of the speech waveform, the current recognition accuracy needs improvement. To make full use of the difference of emotional saturation between time frames, a novel method is proposed for speech recognition using frame-level speech features combined with attention-based long short-term memory LSTM recurrent neural networks. Frame-level speech features were extracted from waveform to replace traditional statistical features, which could preserve the timing relations in the original speech through the sequence of frames. To distinguish emotional saturation in different frames, two improvement strategies are proposed for LSTM based on the attention mechanism: first, the algorithm reduces the computational complexity by modifying the forgetting gate of traditional LSTM without sacrificing performance and second, in the final output of the LSTM, an attention mechanism is applied to both the time and the feature dimension to obtain the information related to the task, rather than using the output from the last iteration of the traditional algorithm. Extensive experiments on the CASIA, eNTERFACE, and GEMEP emotion corpora demonstrate that the performance of the proposed approach is able to outperform the state-of-the-art algorithms reported to date.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1675–1685},
numpages = {11}
}

@article{10.1109/TASLP.2019.2922537,
author = {Zhang, Zhuosheng and Zhao, Hai and Ling, Kangwei and Li, Jiangtong and Li, Zuchao and He, Shexia and Fu, Guohong},
title = {Effective Subword Segmentation for Text Comprehension},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2922537},
doi = {10.1109/TASLP.2019.2922537},
abstract = {Representation learning is the foundation of machine reading comprehension and inference. In state-of-the-art models, character-level representations have been broadly adopted to alleviate the problem of effectively representing rare or complex words. However, character itself is not a natural minimal linguistic unit for representation or word embedding composing due to ignoring the linguistic coherence of consecutive characters inside word. This paper presents a general subword-augmented embedding framework for learning and composing computationally derived subword-level representations. We survey a series of unsupervised segmentation methods for subword acquisition and different subword-augmented strategies for text understanding, showing that subword-augmented embedding significantly improves our baselines in various types of text understanding tasks on both English and Chinese benchmarks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1664–1674},
numpages = {11}
}

@article{10.1109/TASLP.2019.2928140,
author = {Lu, Rui and Duan, Zhiyao and Zhang, Changshui},
title = {Audio–Visual Deep Clustering for Speech Separation},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2928140},
doi = {10.1109/TASLP.2019.2928140},
abstract = {Speech separation aims to separate individual voices from an audio mixture of multiple simultaneous talkers. Audio-only approaches show unsatisfactory performance when the speakers are of the same gender or share similar voice characteristics. This is due to challenges on learning appropriate feature representations for separating voices in single frames and streaming voices across time. Visual signals of speech e.g., lip movements, if available, can be leveraged to learn better feature representations for separation. In this paper, we propose a novel audio–visual deep clustering model AVDC to integrate visual information into the process of learning better feature representations embeddings for Time–Frequency T–F bin clustering. It employs a two-stage audio–visual fusion strategy where speaker-wise audio–visual T–F embeddings are first computed after the first-stage fusion to model the audio–visual correspondence for each speaker. In the second-stage fusion, audio–visual embeddings of all speakers and audio embeddings calculated by deep clustering from the audio mixture are concatenated to form the final T–F embedding for clustering. Through a series of experiments, the proposed AVDC model is shown to outperform the audio-only deep clustering and utterance-level permutation invariant training baselines and three other state-of-the-art audio–visual approaches. Further analyses show that the AVDC model learns a better T–F embedding for alleviating the source permutation problem across frames. Other experiments show that the AVDC model is able to generalize across different numbers of speakers between training and testing and shows some robustness when visual information is partially missing.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1697–1712},
numpages = {16}
}

@article{10.1109/TASLP.2019.2928128,
author = {Wang, Shuai and Huang, Zili and Qian, Yanmin and Yu, Kai},
title = {Discriminative Neural Embedding Learning for Short-Duration Text-Independent Speaker Verification},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2928128},
doi = {10.1109/TASLP.2019.2928128},
abstract = {Short duration text-independent speaker verification remains a hot research topic in recent years, and deep neural network based embeddings have shown impressive results in such conditions. Good speaker embeddings require the property of both small intra-class variation and large inter-class difference, which is critical for the ability of discrimination and generalization. Current embedding learning strategies can be grouped into two frameworks: “Cascade embedding learning” with multiple stages and “direct embedding learning” from spectral feature directly. We propose new approaches to achieve more discriminant speaker embeddings. Within the cascade framework, a neural network based deep discriminant analysis DDA is proposed to project i-vector to more discriminative embeddings. Within the direct embedding framework, a deep model with more advanced center loss and A-softmax loss is used, the focal loss is also investigated in this framework. Moreover, the traditional i-vector and neural embeddings are finally combined with neural network based DDA to achieve further gain. Main experiments are carried out on a short-duration text-independent speaker verification dataset generated from the SRE corpus. The results show that the newly proposed method is promising for short-duration text-independent speaker verification, and it is consistently better than traditional i-vector and neural embedding baselines. The best embeddings achieve roughly 30% relative EER reduction compared to the i-vector baseline, which could be further enhanced when combined with the i-vector system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1686–1696},
numpages = {11}
}

@article{10.1109/TASLP.2019.2931759,
author = {Bernardini, Alberto and Maffezzoni, Paolo and Sarti, Augusto},
title = {Linear Multistep Discretization Methods With Variable Step-Size in Nonlinear Wave Digital Structures for Virtual Analog Modeling},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2931759},
doi = {10.1109/TASLP.2019.2931759},
abstract = {There is a growing interest in Virtual Analog modeling algorithms for musical audio processing designed in the Wave Digital WD domain. Such algorithms typically employ a discretization strategy based on the trapezoidal rule with fixed sampling step, though this is not the only option. In fact, alternative discretization strategies possibly with an adaptive sampling step can be quite advantageous, particularly when dealing with nonlinear systems characterized by stiff equations. In this paper, we propose a unified approach for modeling capacitors and inductors in the WD domain using generic linear multi-step discretization methods with variable time-step size, and provide generalized adaptation conditions. We also show that the proposed approach for implementing dynamic energy-storing elements in the WD domain is particularly suitable to be combined with a recently developed technique for efficiently solving a class of circuits with multiple one-port nonlinearities, called Scattering Iterative Method. Finally, as examples of application, we develop WD models for a Van Der Pol oscillator and a dynamic diode-based ring modulator, which use different discretization methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1763–1776},
numpages = {14}
}

@article{10.1109/TASLP.2019.2930913,
author = {Kong, Qiuqiang and Yu, Changsong and Xu, Yong and Iqbal, Turab and Wang, Wenwu and Plumbley, Mark D.},
title = {Weakly Labelled AudioSet Tagging With Attention Neural Networks},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2930913},
doi = {10.1109/TASLP.2019.2930913},
abstract = {Audio tagging is the task of predicting the presence or absence of sound classes within an audio clip. Previous work in audio tagging focused on relatively small datasets limited to recognizing a small number of sound classes. We investigate audio tagging on AudioSet, which is a dataset consisting of over 2 million audio clips and 527 classes. AudioSet is weakly labelled, in that only the presence or absence of sound classes is known for each clip, whereas the onset and offset times are unknown. To address the weakly labelled audio tagging problem, we propose attention neural networks as a way to attend the most salient parts of an audio clip. We bridge the connection between attention neural networks and multiple instance learning MIL methods, and propose decision-level and feature-level attention neural networks for audio tagging. We investigate attention neural networks modeled by different functions, depths, and widths. Experiments on AudioSet show that the feature-level attention neural network achieves a state-of-the-art mean average precision of 0.369, outperforming the best MIL method of 0.317 and Google's deep neural network baseline of 0.314. In addition, we discover that the audio tagging performance on AudioSet-embedding features has a weak correlation with the number of training samples and the quality of labels of each sound class.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1791–1802},
numpages = {12}
}

@article{10.1109/TASLP.2019.2929647,
author = {Parshakova, Tetiana and Rameau, Francois and Serdega, Andriy and Kweon, In So and Kim, Dae-Shik},
title = {Latent Question Interpretation Through Variational Adaptation},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2929647},
doi = {10.1109/TASLP.2019.2929647},
abstract = {Most artificial neural network models for question-answering rely on complex attention mechanisms. These techniques demonstrate high performance on existing datasets; however, they are limited in their ability to capture natural language variability, and to generate diverse relevant answers. To address this limitation, we propose a model that learns multiple interpretations of a given question. This diversity is ensured by our interpretation policy module which automatically adapts the parameters of a question-answering model with respect to a discrete latent variable. This variable follows the distribution of interpretations learned by the interpretation policy through a semi-supervised variational inference framework. To boost the performance further, the resulting policy is fine-tuned using the rewards from the answer accuracy with a policy gradient. We demonstrate the relevance and efficiency of our model through a large panel of experiments. Qualitative results, in particular, underline the ability of the proposed architecture to discover multiple interpretations of a question. When tested using the Stanford Question Answering Dataset 1.1, our model outperforms the baseline methods in finding multiple and diverse answers. To assess our strategy from a human standpoint, we also conduct a large-scale user study. This study highlights the ability of our network to produce diverse and coherent answers compared to existing approaches. Our Pytorch implementation is available as open source.11github.com/parshakova/APIP.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1713–1724},
numpages = {12}
}

@article{10.1109/TASLP.2019.2933146,
author = {Sun, Sining and Guo, Pengcheng and Xie, Lei and Hwang, Mei-Yuh},
title = {Adversarial Regularization for Attention Based End-to-End Robust Speech Recognition},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2933146},
doi = {10.1109/TASLP.2019.2933146},
abstract = {End-to-end speech recognition, such as attention based approaches, is an emerging and attractive topic in recent years. It has achieved comparable performance with the traditional speech recognition framework. Because end-to-end approaches integrate acoustic and linguistic information into one model, the perturbation in the acoustic level such as acoustic noise, could be easily propagated to the linguistic level. Thus improving model robustness in real application environments for these end-to-end systems is crucial. In this paper, in order to make the attention based end-to-end model more robust against noises, we formulate regulation of the objective function with adversarial training examples. Particularly two adversarial regularization techniques, the fast gradient-sign method and the local distributional smoothness method, are explored to improve noise robustness. Experiments on two publicly available Chinese Mandarin corpora, AISHELL-1 and AISHELL-2, show that adversarial regularization is an effective approach to improve robustness against noises for our attention-based models. Specifically, we obtained 18.4% relative character error rate CER reduction on the AISHELL-1 noisy test set. Even on the clean test set, we showed 16.7% relative improvement. As the training set increases and covers more environmental varieties, our proposed methods remain effective despite that the improvement shrinks. Training on the large AISHELL-2 training corpus and testing on the various AISHELL-2 test sets, we achieved 7.0%–12.2% relative error rate reduction. To our knowledge, this is the first successful application of adversarial regularization to sequence-to-sequence speech recognition systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1826–1838},
numpages = {13}
}

@article{10.1109/TASLP.2019.2928143,
author = {Maghsoodi, Nooshin and Sameti, Hossein and Zeinali, Hossein and Stafylakis, Themos},
title = {Speaker Recognition With Random Digit Strings Using Uncertainty Normalized HMM-Based i-Vectors},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2928143},
doi = {10.1109/TASLP.2019.2928143},
abstract = {In this paper, we combine Hidden Markov Models HMMs with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform channel and uncertainty compensation, and we propose a novel method for using the uncertainty in the i-vector estimates. The experiments on RSR2015 part III show that the proposed method attains 1.52% and 1.77% Equal Error Rate EER for male and female respectively, outperforming state-of-the-art methods such as x-vectors, trained on vast amounts of data. Furthermore, these results are attained by a single system trained entirely on RSR2015, and by a simple score-normalized cosine distance. Moreover, we show that the omission of channel compensation yields only a minor degradation in performance, meaning that the system attains state-of-the-art results even without recordings from multiple handsets per speaker for training or enrolment. Similar conclusions are drawn from our experiments on the RedDots corpus, where the same method is evaluated on phrases. Finally, we report results with bottleneck features and show that further improvement is attained when fusing them with spectral features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1815–1825},
numpages = {11}
}

@article{10.1109/TASLP.2019.2930917,
author = {Shi, Liming and Nielsen, Jesper Kjaer and Jensen, Jesper Rindom and Little, Max A. and Christensen, Mads Graesboll},
title = {Robust Bayesian Pitch Tracking Based on the Harmonic Model},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2930917},
doi = {10.1109/TASLP.2019.2930917},
abstract = {Fundamental frequency is one of the most important characteristics of speech and audio signals. Harmonic model-based fundamental frequency estimators offer a higher estimation accuracy and robustness against noise than the widely used autocorrelation-based methods. However, the traditional harmonic model-based estimators do not take the temporal smoothness of the fundamental frequency, the model order, and the voicing into account as they process each data segment independently. In this paper, a fully Bayesian fundamental frequency tracking algorithm based on the harmonic model and a first-order Markov process model is proposed. Smoothness priors are imposed on the fundamental frequencies, model orders, and voicing using first-order Markov process models. Using these Markov models, fundamental frequency estimation and voicing detection errors can be reduced. Using the harmonic model, the proposed fundamental frequency tracker has an improved robustness to noise. An analytical form of the likelihood function, which can be computed efficiently, is derived. Compared to the state-of-the-art neural network and nonparametric approaches, the proposed fundamental frequency tracking algorithm has superior performance in almost all investigated scenarios, especially in noisy conditions. For example, under 0 dB white Gaussian noise, the proposed algorithm reduces the mean absolute errors and gross errors by 15% and 20% on the Keele pitch database and 36% and 26% on sustained /a/ sounds from a database of Parkinson's disease voices. A MATLAB version of the proposed algorithm is made freely available for reproduction of the results.11An implementation of the proposed algorithm using MATLAB may be found in https://tinyurl.com/yxn4a543.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1737–1751},
numpages = {15}
}

@article{10.1109/TASLP.2019.2930914,
author = {Yang, Yan and Bao, Changchun},
title = {RS-CAE-Based AR-Wiener Filtering and Harmonic Recovery for Speech Enhancement},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2930914},
doi = {10.1109/TASLP.2019.2930914},
abstract = {By taking into account temporal correlation of speech feature. In this paper, a novel structure of convolutional Auto Encoder CAE was proposed. In this structure, the historical output of the CAE was fed into a CAE stack recurrently. We name this structure as Recurrent Stack Convolutional Auto Encoder RS-CAE. In the training stage, the training feature maps of the RS-CAE comprise of log power spectrum LPS of noisy speech and an additional feature map derived from the LPS of the enhanced speech in the history. In this way, the temporal correlation is incorporated as much as possible in the RS-CAE. The training target is a concatenated vector of auto-regressive AR model parameters of speech and noise. At online stage, the LPS of noisy speech and the LPS of the enhanced speech from the history make up input feature maps together. The outputs of the RS-CAE are the AR model parameters of speech and noise, which are used to construct the AR-Wiener filter. Because the estimated AR model parameters are not completely accurate and some harmonics may be lost in the enhanced speech, the codebook-based harmonic recovery technique was proposed to reconstruct harmonic structure of the enhanced speech. The test results confirmed that the proposed method achieved better performance compared with some existing approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1752–1762},
numpages = {11}
}

@article{10.1109/TASLP.2019.2929859,
author = {Wong, Jeremy Heng Meng and Gales, Mark John Francis and Wang, Yu},
title = {General Sequence Teacher–Student Learning},
year = {2019},
issue_date = {November 2019},
publisher = {IEEE Press},
volume = {27},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2929859},
doi = {10.1109/TASLP.2019.2929859},
abstract = {In automatic speech recognition, performance gains can often be obtained by combining an ensemble of multiple models. However, this can be computationally expensive when performing recognition. Teacher–student learning alleviates this cost by training a single student model to emulate the combined ensemble behaviour. Only this student needs to be used for recognition. Previously investigated teacher–student criteria often limit the forms of diversity allowed in the ensemble, and only propagate information from the teachers to the student at the frame level. This paper addresses both of these issues by examining teacher–student learning within a sequence-level framework, and assessing the flexibility that these approaches offer. Various sequence-level teacher–student criteria are examined in this work, to propagate sequence posterior information. A training criterion based on the Kullback–Leibler KL-divergence between context-dependent state sequence posteriors is proposed that allows for a diversity of state cluster sets to be present in the ensemble. This criterion is shown to be an upper bound to a more general KL-divergence between word sequence posteriors, which places even fewer restrictions on the ensemble diversity, but whose gradient can be expensive to compute. These methods are evaluated on the augmented multi-party interaction AMI meeting transcription and MGB-3 television broadcast audio tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {1725–1736},
numpages = {12}
}

@article{10.1109/TASLP.2019.2921423,
author = {Wang, Yijun and Xia, Yingce and Zhao, Li and Bian, Jiang and Qin, Tao and Chen, Enhong and Liu, Tie-Yan},
title = {Semi-Supervised Neural Machine Translation via Marginal Distribution Estimation},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2921423},
doi = {10.1109/TASLP.2019.2921423},
abstract = {Neural machine translation NMT heavily relies on parallel bilingual corpora for training. Since large-scale, high-quality parallel corpora are usually costly to collect, it is appealing to exploit monolingual corpora to improve NMT. Inspired by the law of total probability, which connects the probability of a given target-side monolingual sentence to the conditional probability of translating from a source sentence to the target one, we propose to explicitly exploit this connection and help the training procedure of NMT models using monolingual data. The key technical challenge of this approach is that there are exponentially many source sentences for a target monolingual sentence while computing the sum of the conditional probability given each possible source sentence. We address this challenge by leveraging the reverse translation model target-to-source translation model to sample several mostly likely source-side sentences and avoid enumerating all possible candidate source sentences. Then we propose two different methods to leverage the law of total probability, including marginal distribution regularization and likelihood maximization of monolingual corpora. Experiment results on English$rightarrow$French and German$rightarrow$English tasks demonstrate that our methods achieve significant improvement over several strong baselines.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1564–1576},
numpages = {13}
}

@article{10.1109/TASLP.2019.2922845,
author = {Li, Pairui and Chen, Chuan and Zheng, Wujie and Deng, Yuetang and Ye, Fanghua and Zheng, Zibin},
title = {STD: An Automatic Evaluation Metric for Machine Translation Based on Word Embeddings},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2922845},
doi = {10.1109/TASLP.2019.2922845},
abstract = {Lexical-based metrics such as BLEU, NIST, and WER have been widely used in machine translation MT evaluation. However, these metrics badly represent semantic relationships and impose strict identity matching, leading to moderate correlation with human judgments. In this paper, we propose a novel MT automatic evaluation metric Semantic Travel Distance STD based on word embeddings. STD incorporates both semantic and lexical features word embeddings and n-gram and word order into one metric. It measures the semantic distance between the hypothesis and reference by calculating the minimum cumulative cost that the embedded n-grams of the hypothesis need to “travel” to reach the embedded n-grams of the reference. Experiment results show that STD has a better and more robust performance than a range of state-of-the-art metrics for both the segment-level and system-level evaluation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1497–1506},
numpages = {10}
}

@article{10.1109/TASLP.2019.2926754,
author = {Zhao, Guanlong and Gutierrez-Osuna, Ricardo},
title = {Using Phonetic Posteriorgram Based Frame Pairing for Segmental Accent Conversion},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2926754},
doi = {10.1109/TASLP.2019.2926754},
abstract = {Accent conversion AC aims to transform non-native utterances to sound as if the speaker had a native accent. This can be achieved by mapping source speech spectra from a native speaker into the acoustic space of the target non-native speaker. In prior work, we proposed an AC approach that matches frames between the two speakers based on their acoustic similarity after compensating for differences in vocal tract length. In this paper, we propose a new approach that matches frames between the two speakers based on their phonetic rather than acoustic similarity. Namely, we map frames from the two speakers into a phonetic posteriorgram using speaker-independent acoustic models trained on native speech. We thoroughly evaluate the approach on a speech corpus containing multiple native and non-native speakers. The proposed algorithm outperforms the prior approach, improving ratings of acoustic quality 22% increase in mean opinion score and native accent 69% preference while retaining the voice quality of the non-native speaker. Furthermore, we show that the approach can be used in the reverse conversion direction, i.e., generating speech with a native speaker's voice quality and a non-native accent. Finally, we show that this approach can be applied to non-parallel training data, achieving the same accent conversion performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1649–1660},
numpages = {12}
}

@article{10.1109/TASLP.2019.2923969,
author = {Park, Jihwan and Chang, Joon-Hyuk},
title = {State-Space Microphone Array Nonlinear Acoustic Echo Cancellation Using Multi-Microphone Near-End Speech Covariance},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2923969},
doi = {10.1109/TASLP.2019.2923969},
abstract = {Nonlinear acoustic echo cancellation AEC is a highly challenging task in a single-microphone; hence, the AEC technique with a microphone array has also been considered to more effectively reduce the residual echo. However, these algorithms track only a linear acoustic path between the loudspeaker and the microphone array. This study proposes a microphone array form of the single-microphone nonlinear AEC NAEC algorithm in the reverberant condition. We extend a single-microphone-based model of the nonlinear acoustic echo to the microphone array case and propose the modeling of the acoustic transfer function ATF vector extended with a power series using a state-space equation. The Kalman filter is also adapted to optimally and recursively estimate the ATF vector. Furthermore, low-rank approximation and multi-microphone Wiener filtering are applied to estimate the multi-microphone near-end speech covariance, which results in the microphone array NAEC algorithm showing a consistently outstanding performance under severe signal-to-echo ratio SER and highly reverberant conditions. Consequently, our approach outperforms conventional methods regarding echo reduction and near-end speech quality for a wide range of SER and reverberation conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1520–1534},
numpages = {15}
}

@article{10.1109/TASLP.2019.2923951,
author = {Luo, Zhaojie and Chen, Jinhui and Takiguchi, Tetsuya and Ariki, Yasuo},
title = {Emotional Voice Conversion Using Dual Supervised Adversarial Networks With Continuous Wavelet Transform F0 Features},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2923951},
doi = {10.1109/TASLP.2019.2923951},
abstract = {In emotional voice conversion VC tasks, it is difficult to deal with a simple representation of fundamental frequency F0, which is the most important feature in emotional voice representation. In order to address this issue, we propose the adaptive scales continuous wavelet transform ADS-CWT method to systematically capture F0 features of different temporal levels, which can represent different prosodic aspects, ranging from micro-prosody to sentences. Moreover, in an emotional VC task, each dataset is paired with the labeled emotional voice and neutral voice, which can be regarded as a dual task. Owing to, first, dual supervised learning's ability to improve the training performances by using the leveraging probabilistic connection between the dual tasks to enhance the learning from labeled data and, second, generative adversarial networks’ GANs’ ability to mitigate the over-smoothing problem caused in the low-level data space when converting the acoustic features, we further present a novel training framework for emotional VC using GANs combined with dual supervised learning, named as dual supervised adversarial networks. In emotional VC experiments, we confirmed the high similarity performance of our method when using limited labeled data for emotional VC. Our method achieves good and consistent performance, in both objective and subjective evaluations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1535–1548},
numpages = {14}
}

@article{10.1109/TASLP.2019.2926125,
author = {Lan, Yunshi and Wang, Shuohang and Jiang, Jing},
title = {Knowledge Base Question Answering With a Matching-Aggregation Model and Question-Specific Contextual Relations},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2926125},
doi = {10.1109/TASLP.2019.2926125},
abstract = {Making use of knowledge bases to answer questions KBQA is a key direction in question answering systems. Researchers have developed a diverse range of methods to address this problem, but there are still some limitations with the existing methods. Specifically, the existing neural network-based methods for KBQA have not taken advantage of the recent “matching-aggregation” framework for the sequence matching, and when representing a candidate answer entity, they may not choose the most useful context of the candidate for matching. In this paper, we explore the use of a “matching-aggregation” framework to match candidate answers with questions. We further make use of question-specific contextual relations to enhance the representations of candidate answer entities. Our complete method is able to achieve state-of-the-art performance on two benchmark datasets: WebQuestions and SimpleQuestions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1629–1638},
numpages = {10}
}

@article{10.1109/TASLP.2019.2924842,
author = {Fontana, Federico and Bozzo, Enrico},
title = {Newton–Raphson Solution of Nonlinear Delay-Free Loop Filter Networks},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2924842},
doi = {10.1109/TASLP.2019.2924842},
abstract = {For their numerical properties and speed of convergence, Newton–Raphson methods are frequently used to compute nonlinear audio electronic circuit models in the digital domain. These methods are traditionally employed regardless of preliminary considerations about their applicability, primarily because of a lack of flexible mathematical tools making the convergence analysis an easy task. We define the basin delimiter, a tool that can be applied to the case when the nonlinear circuit is modeled by a delay-free loop network. This tool is derived from a known convergence theorem providing a sufficient condition for quadratic speed of convergence of the method. After substituting the nonlinear characteristics with equivalent linear filters that compute Newton–Raphson on the existing network, through the basin delimiter, we figure out constraints guaranteeing quadratic convergence speed in the diode clipper. Further application to a ring modulator circuit does not lead to comparably useful constraints for quadratic convergence; however, also in this circuit, the basin delimiter has a magnitude roughly proportional to the number of iterations needed by the solver to find a solution. Together, such case studies foster refinement and generalization of this tool as a speed predictor, with potential application to the design of virtual analogue systems for real-time digital audio effects.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1590–1600},
numpages = {11}
}

@article{10.1109/TASLP.2019.2925450,
author = {Makishima, Naoki and Mogami, Shinichi and Takamune, Norihiro and Kitamura, Daichi and Sumino, Hayato and Takamichi, Shinnosuke and Saruwatari, Hiroshi and Ono, Nobutaka},
title = {Independent Deeply Learned Matrix Analysis for Determined Audio Source Separation},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2925450},
doi = {10.1109/TASLP.2019.2925450},
abstract = {In this paper, we propose a new framework called independent deeply learned matrix analysis IDLMA, which unifies a deep neural network DNN and independence-based multichannel audio source separation. IDLMA utilizes both pretrained DNN source models and statistical independence between sources for the separation, where the time-frequency structures of each source are iteratively optimized by a DNN while enhancing the estimation accuracy of the spatial demixing filters. As the source generative model, we introduce a complex heavy-tailed distribution to improve the separation performance. In addition, we address a semi-supervised situation; namely, a solo-recorded audio dataset can be prepared for only one source in the mixture signal. To solve the limited-data problem, we propose an appropriate data augmentation method to adapt the DNN source models to the observed signal, which enables IDLMA to work even in the semi-supervised situation. Experiments are conducted using music signals with a training dataset in both supervised and semi-supervised situations. The results show the validity of the proposed method in terms of the separation accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1601–1615},
numpages = {15}
}

@article{10.1109/TASLP.2019.2924321,
author = {As'ad, Hala and Bouchard, Martin and Kamkar-Parsi, Homayoun},
title = {A Robust Target Linearly Constrained Minimum Variance Beamformer With Spatial Cues Preservation for Binaural Hearing Aids},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2924321},
doi = {10.1109/TASLP.2019.2924321},
abstract = {In this paper, a binaural beamforming algorithm for hearing aid applications is introduced. The beamforming algorithm is designed to be robust to some error in the estimate of the target speaker direction. The algorithm has two main components: a robust target linearly constrained minimum variance TLCMV algorithm based on imposing two constraints around the estimated direction of the target signal, and a post-processor to help with the preservation of binaural cues. The robust TLCMV provides a good level of noise reduction and low level of target distortion under realistic conditions. The post-processor enhances the beamformer abilities to preserve the binaural cues for both diffuse-like background noise and directional interferers competing speakers, while keeping a good level of noise reduction. The introduced algorithm does not require knowledge or estimation of the directional interferers’ directions nor the second-order statistics of noise-only components. The introduced algorithm requires an estimate of the target speaker direction, but it is designed to be robust to some deviation from the estimated direction. Compared with recently proposed state-of-the-art methods, comprehensive evaluations are performed under complex realistic acoustic scenarios generated in both anechoic and mildly reverberant environments, considering a mismatch between estimated and true sources direction of arrival. Mismatch between the anechoic propagation models used for the design of the beamformers and the mildly reverberant propagation models used to generate the simulated directional signals is also considered. The results illustrate the robustness of the proposed algorithm to such mismatches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1549–1563},
numpages = {15}
}

@article{10.1109/TASLP.2019.2925973,
author = {Bai, Xuefeng and Cao, Hailong and Chen, Kehai and Zhao, Tiejun},
title = {A Bilingual Adversarial Autoencoder for Unsupervised Bilingual Lexicon Induction},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2925973},
doi = {10.1109/TASLP.2019.2925973},
abstract = {Unsupervised bilingual lexicon induction aims to generate bilingual lexicons without any cross-lingual signals. Successfully solving this problem would benefit many downstream tasks, such as unsupervised machine translation and transfer learning. In this work, we propose an unsupervised framework, named bilingual adversarial autoencoder, which automatically generates bilingual lexicon for a pair of languages from their monolingual word embeddings. In contrast to existing frameworks which learn a direct cross-lingual mapping of word embeddings from the source language to the target language, we train two autoencoders jointly to transform the source and the target monolingual word embeddings into a shared embedding space, where a word and its translation are close to each other. In this way, we capture the cross-lingual features of word embeddings from different languages and use them to induce bilingual lexicons. By conducting extensive experiments across eight language pairs, we demonstrate that the proposed method significantly outperforms the existing adversarial methods and even achieves best-published results across most language pairs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1639–1648},
numpages = {10}
}

@article{10.1109/TASLP.2019.2923542,
author = {Zhang, Jie and Heusdens, Richard and Hendriks, Richard Christian},
title = {Relative Acoustic Transfer Function Estimation in Wireless Acoustic Sensor Networks},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2923542},
doi = {10.1109/TASLP.2019.2923542},
abstract = {In this paper, we present an algorithm to estimate the relative acoustic transfer function RTF of a target source in wireless acoustic sensor networks WASNs. Two well-known methods to estimate the RTF are the covariance subtraction CS method and the covariance whitening CW approach, the latter based on the generalized eigenvalue decomposition. Both methods depend on the use of the noisy correlation matrix, which, in practice, has to be estimated using limited and in WASNs quantized data. The bit rate and the fact that we use limited data records therefore directly affect the accuracy of the estimated RTFs. Therefore, we first theoretically analyze the estimation performance of the two approaches in terms of bit rate. Second, we propose a rate-distribution method by minimizing the power usage and constraining the expected estimation error for both RTF estimators. The optimal rate distributions are found by using convex optimization techniques. The model-based methods, however, are impractical due to the dependence on the true RTFs. We therefore further develop two greedy rate-distribution methods for both approaches. Finally, numerical simulations on synthetic data and real audio recordings show the superiority of the proposed approaches in power usage compared to uniform rate allocation. We find that in order to satisfy the same RTF estimation accuracy, the rate-distributed CW methods consume much less transmission energy than the CS-based methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1507–1519},
numpages = {13}
}

@article{10.1109/TASLP.2019.2924534,
author = {Prakash, Jeena J. and Murthy, Hema A.},
title = {Analysis of Inter-Pausal Units in Indian Languages and Its Application to Text-to-Speech Synthesis},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2924534},
doi = {10.1109/TASLP.2019.2924534},
abstract = {Lack of punctuation in Indian language text makes the analysis of phrases difficult. In this paper, inter-pausal units IPUs in read sentences are considered as phrases and are analyzed. A key observation from this analysis is that the length of the IPUs in read sentences follow uniformly across all languages a Gamma distribution. Additionally, an analysis of the scale and shape parameters suggest that these parameters are governed by the location of the IPU in an utterance. This information is used in text-to-speech TTS systems for four Indian languages leading to an improvement in naturalness. A novel IPU-based TTS system is proposed for better prosody modeling as well. A given text is parsed into IPUs, and an appropriate TTS system for different IPUs is used for synthesis. It is observed that there is a significant improvement in the naturalness of synthesized speech compared to that of a single TTS system being used for the entire sentence.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1616–1628},
numpages = {13}
}

@article{10.1109/TASLP.2019.2921890,
author = {Jati, Arindam and Georgiou, Panayiotis},
title = {Neural Predictive Coding Using Convolutional Neural Networks Toward Unsupervised Learning of Speaker Characteristics},
year = {2019},
issue_date = {October 2019},
publisher = {IEEE Press},
volume = {27},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2921890},
doi = {10.1109/TASLP.2019.2921890},
abstract = {Learning speaker-specific features is vital in many applications like speaker recognition, diarization, and speech recognition. This paper provides a novel approach, we term neural predictive coding NPC, to learn speaker-specific characteristics in a completely unsupervised manner from large amounts of unlabeled training data that even contain many non-speech events and multi-speaker audio streams. The NPC framework exploits the proposed short-term active-speaker stationarity hypothesis which assumes two temporally close short speech segments belong to the same speaker, and thus a common representation that can encode the commonalities of both the segments, should capture the vocal characteristics of that speaker. We train a convolutional deep siamese network to produce “speaker embeddings” by learning to separate “same” versus “different” speaker pairs which are generated from an unlabeled data of audio streams. Two sets of experiments are done in different scenarios to evaluate the strength of NPC embeddings and compare with state-of-the-art in-domain supervised methods. First, two speaker identification experiments with different context lengths are performed in a scenario with comparatively limited within-speaker channel variability. NPC embeddings are found to perform the best at short duration experiment, and they provide complementary information to i-vectors for full utterance experiments. Second, a large-scale speaker verification task having a wide range of within-speaker channel variability is adopted as an upper-bound experiment where comparisons are drawn with in-domain supervised methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1577–1589},
numpages = {13}
}

@article{10.1109/TASLP.2019.2917232,
author = {Kameoka, Hirokazu and Kaneko, Takuhiro and Tanaka, Kou and Hojo, Nobukatsu},
title = {ACVAE-VC: Non-Parallel Voice Conversion With Auxiliary Classifier Variational Autoencoder},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2917232},
doi = {10.1109/TASLP.2019.2917232},
abstract = {This paper proposes a non-parallel voice conversion VC method using a variant of the conditional variational autoencoder VAE called an auxiliary classifier VAE. The proposed method has two key features. First, it adopts fully convolutional architectures to construct the encoder and decoder networks so that the networks can learn conversion rules that capture the time dependencies in the acoustic feature sequences of source and target speech. Second, it uses information-theoretic regularization for the model training to ensure that the information in the attribute class label will not be lost in the conversion process. With regular conditional VAEs, the encoder and decoder are free to ignore the attribute class label input. This can be problematic since in such a situation, the attribute class label will have little effect on controlling the voice characteristics of input speech at test time. Such situations can be avoided by introducing an auxiliary classifier and training the encoder and decoder so that the attribute classes of the decoder outputs are correctly predicted by the classifier. We also present several ways to convert the feature sequence of input speech using the trained encoder and decoder and compare them in terms of audio quality through objective and subjective evaluations. We confirmed experimentally that the proposed method outperformed baseline non-parallel VC systems and performed comparably to an open-source parallel VC system trained using a parallel corpus in a speaker identity conversion task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1432–1443},
numpages = {12}
}

@article{10.1109/TASLP.2019.2913499,
author = {Lee, Chia-Hsuan and Lee, Hung-yi and Wu, Szu-Lin and Liu, Chi-Liang and Fang, Wei and Hsu, Juei-Yang and Tseng, Bo-Hsiang},
title = {Machine Comprehension of Spoken Content: TOEFL Listening Test and Spoken SQuAD},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2913499},
doi = {10.1109/TASLP.2019.2913499},
abstract = {A user can scan through a text easily, but it is not the case for spoken content, because they cannot be directly displayed on-screen. As a result, accessing large collections of spoken content is much more difficult and time-consuming than doing so for the text content. It would therefore be helpful to develop machines that understand spoken content. In this paper, we propose two new tasks for machine comprehension of spoken content. The first is a listening comprehension test for TOEFL, a challenging academic English examination for English learners who are not the native English speakers. We show that the proposed model outperforms the naive approaches and other neural network based models by exploiting the hierarchical structures of natural languages and the selective power of attention mechanism. For the second listening comprehension task – spoken SQuAD – we find that speech recognition errors severely impair machine comprehension; we propose the use of subword units to mitigate the impact of these errors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1469–1480},
numpages = {12}
}

@article{10.1109/TASLP.2019.2921892,
author = {Wang, Rui and Chen, Zhe and Yin, Fuliang Yin},
title = {DOA-Based Three-Dimensional Node Geometry Calibration in Acoustic Sensor Networks and Its Cram\'{e}R–Rao Bound and Sensitivity Analysis},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2921892},
doi = {10.1109/TASLP.2019.2921892},
abstract = {Acoustic sensor networks ASNs are widely applied in scenarios like teleconference, teaching, and theatre. ASNs can be used in tracking speakers, enhancing the speaker's speech and human–machine interactions, etc., but the geometric structure of the ASN has to be calibrated. ASN geometry calibration is a challenging task due to the irregular geometric structures of ASNs. A three-dimensional 3D node geometry calibration approach based on direction of arrival DOA measurements and artificial bee colony ABC algorithm is proposed in this paper. The theoretical DOAs of sound sources relative to nodes are first derived based on 3D rotation matrices and translation vectors, and the corresponding measured DOAs are estimated by the time-difference-of-arrival. Then, the node geometry calibration problem is formulated as the minimization of a cost function measuring the mismatch between theoretical and measured DOAs, and such non-convex minimization is effectively solved by the ABC algorithm. Next, Cram\'{e}r–Rao bound is presented to provide a theoretical lower bound for DOA-based node geometry calibration. Finally, the sensitivity of the proposed method to the sound source position error is discussed. The proposed method can calibrate node geometry positions successfully in both 2D plane and 3D space and requires no information transmission among nodes when the positions of few sound sources and the relative geometry of microphones in each node are known. Experimental results reveal the validity of the proposed node geometry calibration method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1455–1468},
numpages = {14}
}

@article{10.1109/TASLP.2019.2915922,
author = {Xu, Zhen and Sun, Chengjie and Long, Yinong and Liu, Bingquan and Wang, Baoxun and Wang, Mingjiang and Zhang, Min and Wang, Xiaolong},
title = {Dynamic Working Memory for Context-Aware Response Generation},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2915922},
doi = {10.1109/TASLP.2019.2915922},
abstract = {In human-to-human conversations, the context generally provides several backgrounds and strategic points for the following response. Therefore, many response generation approaches have explored the methodologies to incorporate the context into the encoder–decoder architecture, to generate context-aware responses that are remarkably relevant and cohesive to the given context. However, most approaches pay less attention to semantic interactions implicitly existing within contextual utterances, which are of great importance to capture semantic clues of the given dialog context, indeed. This paper proposes a dynamic working memory mechanism to model long-term semantic hints in the conversation context, by performing semantic interactions between utterances and updating context representation dynamically. Then, the outputs of the dynamic working memory are employed to provide helpful clues for the encoder–decoder architecture to generate responses to the given dialog. We have evaluated the proposed approach on Twitter Customer Service Corpus and OpenSubtitles Corpus, with several automatic evaluation metrics and the human evaluation, and the empirical results show the effectiveness of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1419–1431},
numpages = {13}
}

@article{10.1109/TASLP.2019.2919183,
author = {Li, Xiaofei and Girin, Laurent and Gannot, Sharon and Horaud, Radu},
title = {Multichannel Online Dereverberation Based on Spectral Magnitude Inverse Filtering},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2919183},
doi = {10.1109/TASLP.2019.2919183},
abstract = {This paper addresses the problem of multichannel online dereverberation. The proposed method is carried out in the short-time Fourier transform STFT domain, and for each frequency band independently. In the STFT domain, the time-domain room impulse response is approximately represented by the convolutive transfer function CTF. The multichannel CTFs are adaptively identified based on the cross-relation method, and using the recursive least square criterion. Instead of the complex-valued CTF convolution model, we use a nonnegative convolution model between the STFT magnitude of the source signal and the CTF magnitude, which is just a coarse approximation of the former model, but is shown to be more robust against the CTF perturbations. Based on this nonnegative model, we propose an online STFT magnitude inverse filtering method. The inverse filters of the CTF magnitude are formulated based on the multiple-input/output inverse theorem, and adaptively estimated based on the gradient descent criterion. Finally, the inverse filtering is applied to the STFT magnitude of the microphone signals, obtaining an estimate of the STFT magnitude of the source signal. Experiments regarding both speech enhancement and automatic speech recognition are conducted, which demonstrate that the proposed method can effectively suppress reverberation, even for the difficult case of a moving speaker.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1365–1377},
numpages = {13}
}

@article{10.1109/TASLP.2019.2922048,
author = {Chen, Xie and Liu, Xunying and Wang, Yu and Ragni, Anton and Wong, Jeremy H. M. and Gales, Mark J. F.},
title = {Exploiting Future Word Contexts in Neural Network Language Models for Speech Recognition},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2922048},
doi = {10.1109/TASLP.2019.2922048},
abstract = {Language modeling is a crucial component in a wide range of applications including speech recognition. Language models LMs are usually constructed by splitting a sentence into words and computing the probability of a word based on its word history. This sentence probability calculation, making use of conditional probability distributions, assumes that there is little impact from approximations used in the LMs, including the word history representations and finite training data. This motivates examining models that make use of additional information from the sentence. In this paper, future word information, in addition to the history, is used to predict the probability of the current word. For recurrent neural network LMs RNNLMs, this information can be encapsulated in a bi-directional model. However, if used directly, this form of model is computationally expensive when trained on large quantities of data, and can be problematic when used with word lattices. This paper proposes a novel neural network language model structure, the succeeding-word RNNLM, su-RNNLM, to address these issues. Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a fixed finite number of succeeding words. This is more efficient in training than bi-directional models and can be applied to lattice rescoring. The generated lattices can be used for downstream applications, such as confusion network decoding and keyword search. Experimental results on speech recognition and keyword spotting tasks illustrate the empirical usefulness of future word information, and the flexibility of the proposed model to represent this information.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1444–1454},
numpages = {11}
}

@article{10.1109/TASLP.2019.2922832,
author = {Chen, Yi-Chen and Huang, Sung-Feng and Lee, Hung-yi and Wang, Yu-Hsuan and Shen, Chia-Hao},
title = {Audio Word2vec: Sequence-to-Sequence Autoencoding for Unsupervised Learning of Audio Segmentation and Representation},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2922832},
doi = {10.1109/TASLP.2019.2922832},
abstract = {In text, word2vec transforms each word into a fixed-size vector used as the basic component in applications of natural language processing. Given a large collection of unannotated audio, audio word2vec can also be trained in an unsupervised way using a sequence-to-sequence autoencoder SA. These vector representations are shown to effectively describe the sequential phonetic structures of the audio segments. In this paper, we further extend this research in the following two directions. First, we disentangle phonetic information and speaker information from the SA vector representations. Second, we extend audio word2vec from the word level to the utterance level by proposing a new segmental audio word2vec in which unsupervised spoken word boundary segmentation and audio word2vec are jointly learned and mutually enhanced, and utterances are directly represented as sequences of vectors carrying phonetic information. This is achieved by means of a segmental sequence-to-sequence autoencoder, in which a segmentation gate trained with reinforcement learning is inserted in the encoder.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1481–1493},
numpages = {13}
}

@article{10.1109/TASLP.2019.2919872,
author = {Chen, Lu and Chen, Zhi and Tan, Bowen and Long, Sishan and Gasic, Milica and Yu, Kai},
title = {AgentGraph: Toward Universal Dialogue Management With Structured Deep Reinforcement Learning},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2919872},
doi = {10.1109/TASLP.2019.2919872},
abstract = {Dialogue policy plays an important role in task-oriented spoken dialogue systems. It determines how to respond to users. The recently proposed deep reinforcement learning DRL approaches have been used for policy optimization. However, these deep models are still challenging for two reasons: first, many DRL-based policies are not sample efficient; and second, most models do not have the capability of policy transfer between different domains. In this paper, we propose a universal framework, AgentGraph, to tackle these two problems. The proposed AgentGraph is the combination of graph neural network GNN based architecture and DRL-based algorithm. It can be regarded as one of the multi-agent reinforcement learning approaches. Each agent corresponds to a node in a graph, which is defined according to the dialogue domain ontology. When making a decision, each agent can communicate with its neighbors on the graph. Under AgentGraph framework, we further propose dual GNN-based dialogue policy, which implicitly decomposes the decision in each turn into a high-level global decision and a low-level local decision. Experiments show that AgentGraph models significantly outperform traditional reinforcement learning approaches on most of the 18 tasks of the PyDial benchmark. Moreover, when transferred from the source task to a target task, these models not only have acceptable initial performance but also converge much faster on the target task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1378–1391},
numpages = {14}
}

@article{10.1109/TASLP.2019.2921151,
author = {Moller, Martin Bo and Nielsen, Jesper Kjaer and Fernandez-Grande, Efren and Olesen, Soren Krarup},
title = {On the Influence of Transfer Function Noise on Sound Zone Control in a Room},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2921151},
doi = {10.1109/TASLP.2019.2921151},
abstract = {Sound zones are valuable in scenarios where multiple people are present in the same room but want to listen to individual audio content without wearing headphones. The purpose of sound zone methods is to minimize the acoustic leakage between the zones by controlling multiple loudspeakers. This requires knowledge of how the loudspeakers interact with the room and radiate sound to the zones. That interaction is characterized by the transfer functions between the loudspeakers and microphones sampling the sound field in the zones. In this paper, the effect on the acoustic separation due to inherent noise in in situ transfer function measurements is investigated. The attainable separation is analyzed in the frequency range 20–300 Hz by means of the eigenfunctions of a rectangular room. The concept of observable degrees of freedom is introduced to indicate the number of active eigenfunctions, which are different within the zones at a given frequency. Likewise, controllable degrees of freedom indicate whether each source can excite the active eigenfunctions independently. It is argued that high separation can be achieved when the observable degrees of freedom are fewer than the controllable, and the target sound field can be described by the observable degrees of freedom. However, to attain this high separation it is a requirement that the details in the transfer functions associated with these degrees of freedom can be resolved in the presence of the measurement noise. For both simulated and experimental conditions the transfer functions are estimated using Bayesian inference and the uncertainty in the estimates is used to automatically regularize the sound field control. This regularization is seen to improve the performance when the measurement noise is correlated between the microphones and have little effect when the noise is uncorrelated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1405–1418},
numpages = {14}
}

@article{10.1109/TASLP.2019.2918400,
author = {Ali, Randall and Bernardi, Giuliano and van Waterschoot, Toon and Moonen, Marc},
title = {Methods of Extending a Generalized Sidelobe Canceller With External Microphones},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2918400},
doi = {10.1109/TASLP.2019.2918400},
abstract = {While substantial noise reduction and speech enhancement can be achieved with multiple microphones organized in an array, in some cases, such as when the microphone spacings are quite close, it can also be quite limited. This degradation can, however, be resolved by the introduction of one or more external microphones $text{XM}$s into the same physical space as the local microphone array $text{LMA}$. In this paper, three methods of extending an $text{LMA}$-based generalized sidelobe canceller $text{GSC-LMA}$ with multiple $text{XM}$s are proposed in such a manner that the relative transfer function pertaining to the $text{LMA}$ is treated as a priori knowledge. Two of these methods involve a procedure for completing an extended blocking matrix, whereas the third uses the speech estimate from the $text{GSC-LMA}$ directly with an orthogonalized version of the $text{XM}$ signals to obtain an improved speech estimate via a rank-1 generalized eigenvalue decomposition. All three methods were evaluated with recorded data from an office room and it was found that the third method could offer the most improvement. It was also shown that in using this method, the speech estimate from the $text{GSC-LMA}$ was not compromised and would be available to the listener if so desired, along with the improved speech estimate that uses both the $text{LMA}$ and $text{XM}$s.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1349–1364},
numpages = {16}
}

@article{10.1109/TASLP.2019.2921726,
author = {Li, Luoqin and Wang, Jiabing and Li, Jichang and Ma, Qianli and Wei, Jia},
title = {Relation Classification via Keyword-Attentive Sentence Mechanism and Synthetic Stimulation Loss},
year = {2019},
issue_date = {September 2019},
publisher = {IEEE Press},
volume = {27},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2921726},
doi = {10.1109/TASLP.2019.2921726},
abstract = {Previous studies have shown that attention mechanisms and shortest dependency paths have a positive effect on relation classification. In this paper, a keyword-attentive sentence mechanism is proposed to effectively combine the two methods. Furthermore, to effectively handle the imbalanced classification problem, this paper proposes a new loss function called the synthetic stimulation loss, which uses a modulating factor to allow the model to focus on hard-to-classify samples. The proposed two methods are integrated into a bidirectional gated recurrent unit BiGRU. As a single model is not strong in noise immunity, this paper applies the mutual learning method to our model and forces the networks to teach each other. Therefore, we call the final model SSL-KAS-MuBiGRU. Experiments on the SemEval-2010 Task 8 data set and the TAC40 data set demonstrate that the keyword-attentive sentence mechanism and synthetic stimulation loss are useful for relation classification, and our model achieves state-of-the-art results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1392–1404},
numpages = {13}
}

@article{10.1109/TASLP.2019.2918081,
author = {Pan, Chao and Chen, Jingdong and Benesty, Jacob and Shi, Guangming},
title = {On the Design of Target Beampatterns for Differential Microphone Arrays},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2918081},
doi = {10.1109/TASLP.2019.2918081},
abstract = {Differential microphone arrays DMAs have many interesting properties and have been widely used in acoustic, audio, and speech applications. A critical part of a DMA is the differential beamformer, which is generally designed in two important steps: 1 specifying a target beampattern based on what differential sound pressure field the DMA is expected to respond to and 2 designing the differential beamforming filter so that the resulting beampattern matches the target one. Most efforts in the study of DMAs so far have focused on the second step while choosing one of the limited patterns available in the literature as the target beampattern. Since it governs how the array performs, how to design the target beampattern is an important problem, which this paper addresses. The major contributions of this paper consists of the following four aspects. First, a positive superposition theorem is presented, which shows that the linear combination of effective beampatterns with non-negative coefficients is always an effective beampattern. Second, we propose a general approach to the design of target DMA beampatterns based on the positive superposition theorem. Third, an overview of the classical target beampatterns is provided and discussion is made on how to form effective base patterns. Fourth, we show that the smallest first null of a DMA is $pi /2N$ with $N$ being the DMA order, which provides the rule of setting nulls in practice. Finally, with examples, we show that with the use of the alternating-direction-method-of-multipliers algorithm, the proposed approach is able to generate useful DMA target beampatterns.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1295–1307},
numpages = {13}
}

@article{10.1109/TASLP.2019.2913091,
author = {Zhang, Teng and Wu, Ji},
title = {Constrained Learned Feature Extraction for Acoustic Scene Classification},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2913091},
doi = {10.1109/TASLP.2019.2913091},
abstract = {Deep neural networks DNNs have been proven to be powerful models for acoustic scene classification tasks. State-of-the-art DNNs have millions of connections and are computationally intensive, making them difficult to deploy on systems with limited resources. With a focus on acoustic scene classification, we describe a new learnable module, the simulated Fourier transform module, which allows deep neural networks to implement the discrete Fourier transform operation 8x faster on a graphics processing unit GPU. We frame the signal processing procedure as an adaptive machine learning problem and introduce learnable parameters in the module to facilitate fast adaptation for the complex and variable acoustic signal. This module gives neural networks the ability to model audio signals from raw waveforms, without extra fast Fourier transform and filter bank patches. Then, we use the temporal transformer module, which has been previously published, to alleviate the information loss caused by the simulated Fourier transform module. These techniques can be integrated into an existing fully connected neural network FCNN, convolutional neural network CNN, or recurrent neural network RNN models. We evaluate the proposed strategy using four acoustic scene datasets LITIS Rouen, DCASE2016, DCASE2017, and DCASE2018 as target tasks. We show that the proposed approach significantly outperforms the vanilla FCNN, CNN, and RNN approach on both efficiency and performance. For instance, the proposed approach can reduce inference time by 8x while reducing the classification error on LITIS Rouen dataset from 3.21% to 1.81%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1216–1228},
numpages = {13}
}

@article{10.1109/TASLP.2019.2915167,
author = {Luo, Yi and Mesgarani, Nima},
title = {Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2915167},
doi = {10.1109/TASLP.2019.2915167},
abstract = {Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time–frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time–frequency representation for speech separation, and the long latency in calculating the spectrograms. To address these shortcomings, we propose a fully convolutional time-domain audio separation network Conv-TasNet, a deep learning framework for end-to-end time-domain speech separation. Conv-TasNet uses a linear encoder to generate a representation of the speech waveform optimized for separating individual speakers. Speaker separation is achieved by applying a set of weighting functions masks to the encoder output. The modified encoder representations are then inverted back to the waveforms using a linear decoder. The masks are found using a temporal convolutional network consisting of stacked one-dimensional dilated convolutional blocks, which allows the network to model the long-term dependencies of the speech signal while maintaining a small model size. The proposed Conv-TasNet system significantly outperforms previous time–frequency masking methods in separating two- and three-speaker mixtures. Additionally, Conv-TasNet surpasses several ideal time–frequency magnitude masks in two-speaker speech separation as evaluated by both objective distortion measures and subjective quality assessment by human listeners. Finally, Conv-TasNet has a significantly smaller model size and a shorter minimum latency, making it a suitable solution for both offline and real-time speech separation applications. This study, therefore, represents a major step toward the realization of speech separation systems for real-world speech processing technologies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1256–1266},
numpages = {11}
}

@article{10.1109/TASLP.2019.2918404,
author = {Azmi, Aqil M. and Almutery, Manal N. and Aboalsamh, Hatim A.},
title = {Real-Word Errors in Arabic Texts: A Better Algorithm for Detection and Correction},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2918404},
doi = {10.1109/TASLP.2019.2918404},
abstract = {Real-word also known as semantic or context-sensitive spelling error is a class of error that escapes the typical spell checker which relies on dictionary look-up. This kind of error occurs when a user types a correctly spelled word–by mistake–when another is intended, e.g., “I want a peace piece of cake.” Further, these errors commonly arise in text written by people with dyslexia. Real-word errors are harder to detect as we need to consider the context. In this paper, we propose a spell checker that detects and corrects real-word errors for the Arabic language. Our method avoids predefined confusion sets–a simple approach used by many works tackling this problem–which limits the list of words that can be detected and corrected. Thus, our system can detect and correct a larger set of real-word errors. For the detection phase, we employ word and stem n-gram n = 1–3 language model along with machine learning, achieving a precision and recall of 83.5% and 99.2%, respectively. And for the correction phase we use n-gram, which results in an accuracy of 98%. Our scheme is robust, with an excellent performance even when the percentage of real-word error words is high. This makes the system suitable for handling errors in post OCR recognition of Arabic text.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1308–1320},
numpages = {13}
}

@article{10.1109/TASLP.2019.2915322,
author = {Sarkar, Achintya Kumar and Tan, Zheng-Hua and Tang, Hao and Shon, Suwon and Glass, James},
title = {Time-Contrastive Learning Based Deep Bottleneck Features for Text-Dependent Speaker Verification},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2915322},
doi = {10.1109/TASLP.2019.2915322},
abstract = {There are a number of studies about extraction of bottleneck BN features from deep neural networks DNNs trained to discriminate speakers, pass-phrases, and triphone states for improving the performance of text-dependent speaker verification TD-SV. However, a moderate success has been achieved. A recent study presented a time contrastive learning TCL concept to explore the non-stationarity of brain signals for classification of brain states. Speech signals have similar non-stationarity property, and TCL further has the advantage of having no need for labeled data. We therefore present a TCL based BN feature extraction method. The method uniformly partitions each speech utterance in a training dataset into a predefined number of multi-frame segments. Each segment in an utterance corresponds to one class, and class labels are shared across utterances. DNNs are then trained to discriminate all speech frames among the classes to exploit the temporal structure of speech. In addition, we propose a segment-based unsupervised clustering algorithm to re-assign class labels to the segments. TD-SV experiments were conducted on the RedDots challenge database. The TCL-DNNs were trained using speech data of fixed pass-phrases that were excluded from the TD-SV evaluation set, so the learned features can be considered phrase-independent. We compare the performance of the proposed TCL BN feature with those of short-time cepstral features and BN features extracted from DNNs discriminating speakers, pass-phrases, speaker+pass-phrase, as well as monophones whose labels and boundaries are generated by three different automatic speech recognition ASR systems. Experimental results show that the proposed TCL-BN outperforms cepstral features and speaker+pass-phrase discriminant BN features, and its performance is on par with those of ASR derived BN features. Moreover, the clustering method improves the TD-SV performance of TCL-BN and ASR derived BN features with respect to their standalone counterparts. We further study the TD-SV performance of fusing cepstral and BN features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1267–1279},
numpages = {13}
}

@article{10.1109/TASLP.2019.2914530,
author = {Gabrielli, Leonardo and Tomassetti, Stefano and Squartini, Stefano and Zinato, Carlo and Guaiana, Stefano},
title = {A Multi-Stage Algorithm for Acoustic Physical Model Parameters Estimation},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2914530},
doi = {10.1109/TASLP.2019.2914530},
abstract = {One of the challenges in computational acoustics is the identification of models that can simulate and predict the physical behavior of a system generating an acoustic signal. Whenever such models are used for commercial applications, an additional constraint is the time to market, making automation of the sound design process desirable. In previous works, a computational sound design approach has been proposed for the parameter estimation problem involving timbre matching by deep learning, which was applied to the synthesis of pipe organ tones. In this paper, we refine previous results by introducing the former approach in a multi-stage algorithm that also adds heuristics and a stochastic optimization method operating on perceptually motivated objective cost functions. The optimization method shows to be able to refine the first estimate given by the deep learning approach and substantially improve the objective metrics, with the additional benefit of reducing the sound design process time. Subjective listening tests are also conducted to gather additional insights on the results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1229–1240},
numpages = {12}
}

@article{10.1109/TASLP.2019.2918618,
author = {Korpusik, Mandy and Glass, James},
title = {Deep Learning for Database Mapping and Asking Clarification Questions in Dialogue Systems},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2918618},
doi = {10.1109/TASLP.2019.2918618},
abstract = {A dialogue system will often ask followup clarification questions when interacting with a user if the agent is unsure how to respond. In this new study, we explore deep reinforcement learning RL for asking followup questions when a user records a meal description, and the system needs to narrow down the options for which foods the person has eaten. We build off of prior work in which we use novel convolutional neural network models to bypass the standard feature engineering used in dialogue systems to handle the text mismatch between natural language user queries and structured database entries, demonstrating that our model learns semantically meaningful embedding representations of natural language. In this new nutrition domain, the followup clarification questions consist of possible attributes for each food that was consumed; for example, if the user drinks a cup of milk, the system should ask about the percent milkfat. We investigate an RL agent to dynamically follow up with the user, which we compare to rule-based and entropy-based methods. On a held-out test set, assuming the followup questions are answered correctly, deep RL significantly boosts top five food recall from 54.9% without followup to 89.0%. We also demonstrate that a hybrid RL model achieves the best perceived naturalness ratings in a human evaluation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1321–1334},
numpages = {14}
}

@article{10.1109/TASLP.2019.2916360,
author = {Chua, Jiawen and Kleijn, W. Bastiaan},
title = {A Low Latency Approach for Blind Source Separation},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2916360},
doi = {10.1109/TASLP.2019.2916360},
abstract = {We present a low latency approach for blind source separation BSS. BSS algorithms generally require a long window to estimate the demixing parameters. In traditional approaches, the long analysis window leads to a long algorithmic delay. Hence, traditional BSS approaches cannot be used in real-time systems. In contrast, our approach reduces the algorithmic delay independently of the window length used for estimation, while retaining separation performance. The new method exploits that the information about the sources provided by additional microphones can be traded against algorithmic delay. The method can be integrated with existing BSS algorithms and can be implemented in the time domain or in the time-frequency domain. Our experimental results confirm the effectiveness of our approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1280–1294},
numpages = {15}
}

@article{10.1109/TASLP.2019.2919378,
author = {Pak, Junhyeong and Shin, Jong Won},
title = {Sound Localization Based on Phase Difference Enhancement Using Deep Neural Networks},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2919378},
doi = {10.1109/TASLP.2019.2919378},
abstract = {The performance of most of the classical sound source localization algorithms degrades seriously in the presence of background noise or reverberation. Recently, deep neural networks DNNs have successfully been applied to sound source localization, which mainly aim to classify the direction-of-arrival DoA into one of the candidate sectors. In this paper, we propose a DNN-based phase difference enhancement for DoA estimation, which turned out to be better than the direct estimation of the DoAs from the input interchannel phase differences IPDs. The sinusoidal functions of the phase differences for “clean and dry” source signals are estimated from the sinusoidal functions of the IPDs for the input signals, which may include directional signals, diffuse noise, and reverberation. The resulted DoA is further refined to compensate for the estimation bias near the end-fire directions. From the enhanced IPDs, we can determine the DoA for each frequency bin and the DoAs for the current frame from the distributions of the DoAs for frequencies. Experimental results with various types and levels of background noise, reverberation times, numbers of sources, room impulse responses, and DoAs showed that the proposed method outperformed conventional approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1335–1345},
numpages = {11}
}

@article{10.1109/TASLP.2019.2915785,
author = {Yang, Bing and Liu, Hong and Pang, Cheng and Li, Xiaofei},
title = {Multiple Sound Source Counting and Localization Based on TF-Wise Spatial Spectrum Clustering},
year = {2019},
issue_date = {August 2019},
publisher = {IEEE Press},
volume = {27},
number = {8},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2915785},
doi = {10.1109/TASLP.2019.2915785},
abstract = {This paper addresses the problem of multiple sound source counting and localization in adverse acoustic environments, using microphone array recordings. The proposed time-frequency TF wise spatial spectrum clustering based method contains two stages. First, given the received sensor signals, the spatial correlation matrix is computed and denoised in the TF domain. The TF-wise spatial spectrum is estimated based on the signal subspace information, and further enhanced by an exponential transform, which can increase the reliability of the source presence possibility reflected by spatial spectrum. Second, to jointly count and localize sound sources, the enhanced TF-wise spatial spectra are divided into several clusters with each cluster corresponding to one source. Sources are successively detected by searching the significant peaks of the remaining global spatial spectrum, which is formed using unassigned spatial spectra. After each new source detection, spatial spectra are reassigned to detected sources according to the dominance association between them. The interaction between sources is reduced by iteratively performing new source detection and spatial spectrum assignment. Experiments on both simulated data and real-world data demonstrate the superiority of the proposed method for multiple sound source counting and localization in the environment with different levels of noise and reverberation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = aug,
pages = {1241–1255},
numpages = {15}
}

@article{10.1109/TASLP.2019.2911167,
author = {Koutrouvelis, Andreas I. and Hendriks, Richard C. and Heusdens, Richard and Jensen, Jesper},
title = {Robust Joint Estimation of Multimicrophone Signal Model Parameters},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2911167},
doi = {10.1109/TASLP.2019.2911167},
abstract = {One of the biggest challenges in multimicrophone applications is the estimation of the parameters of the signal model, such as the power spectral densities PSDs of the sources, the early relative acoustic transfer functions of the sources with respect to the microphones, the PSD of late reverberation, and the PSDs of microphone-self noise. Typically, existing methods estimate subsets of the aforementioned parameters and assume some of the other parameters to be known a priori. This may result in inconsistencies and inaccurately estimated parameters and potential performance degradation in the applications using these estimated parameters. So far, there is no method to jointly estimate all the aforementioned parameters. In this paper, we propose a robust method for jointly estimating all the aforementioned parameters using confirmatory factor analysis. The estimation accuracy of the signal-model parameters thus obtained outperforms existing methods in most cases. We experimentally show significant performance gains in several multimicrophone applications over state-of-the-art methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jul,
pages = {1136–1150},
numpages = {15}
}

@article{10.1109/TASLP.2019.2904850,
author = {Flesner, Jan-Hendrik and Biberger, Thomas and Ewert, Stephan D.},
title = {Subjective and Objective Assessment of Monaural and Binaural Aspects of Audio Quality},
year = {2019},
issue_date = {July 2019},
publisher = {IEEE Press},
volume = {27},
number = {7},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2019.2904850},
doi = {10.1109/TASLP.2019.2904850},
abstract = {Recently, the binaural auditory-model-based quality prediction BAM-Q was successfully applied to predict binaural audio quality degradations, while the generalized power-spectrum model for quality GPSM$^text{q}$ has been demonstrated to account for a large variety of monaural signal distortions. For many applications, a combined monaural and binaural model would be advantageous, however, the contribution of monaural and binaural quality aspects to overall spatial quality is not conclusively clarified. Thus, the current study systematically investigated overall audio quality in a listening experiment for monaural and binaural distortions on music, speech, and noise, applied either in isolation or in combination. The resulting database was used for assessing different methods for combining BAM-Q and GPSM$^text{q}$ to joint overall audio predictions for monaural and binaural signal distortions. It was investigated, if monaural or binaural

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr