text:
. Audio, Speech and Lang. Proc.},
month = may,
pages = {980–995},
numpages = {16}
}

@article{10.1109/TASLP.2017.2674968,
author = {Bao, Yu and Chen, Huawei and Huawei Chen},
title = {Design of Robust Broadband Beamformers Using Worst-Case Performance Optimization: A Semidefinite Programming Approach},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2674968},
doi = {10.1109/TASLP.2017.2674968},
abstract = {Broadband beamformers are known sensitive to microphone mismatches, especially for small-sized arrays. To address the problem, the worst-case performance optimization WCPO criterion has been used to design robust broadband beamformers. Recently, a circular-model-based design using the WCPO criterion was proposed, which has been shown superior to the existing second-order cone programming SOCP based design also using the WCPO criterion. In this paper, however, we find that the circular-model-based design is sensitive to stopband level constraint. If the constraint is chosen tightly, it may become less robust and degrades even worse than the SOCP-based design. To achieve a better tradeoff between robustness and beamforming performance, we propose a semidefinite programming based approach for robust broadband beamformer design using the WCPO criterion. The relations of the proposed design to its existing counterparts are also theoretically analyzed. In particular, we show that the proposed design is less conservative than the SOCP-based design, and is also less sensitive to the setting of stopband level constraint and more robust when compared to the circular model-based design. Another contribution of this paper is that the relation between the SOCP- and the circular-model-based designs is theoretically revealed. Simulation results and real experimental results are presented to show the superior of the proposed design over its existing counterparts.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {895–907},
numpages = {13}
}

@article{10.1109/TASLP.2017.2658019,
author = {Yang, Liner and Chen, Xinxiong and Liu, Zhiyuan and Sun, Maosong and Liner Yang and Xinxiong Chen and Zhiyuan Liu and Maosong Sun},
title = {Improving Word Representations with Document Labels},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2658019},
doi = {10.1109/TASLP.2017.2658019},
abstract = {Word representations, aiming to build vectors for each word, have been successfully used in a variety of applications. Most word representations are learned from large amounts of documents ignoring other information. This is rather suboptimal because the side information of the documents, such as document labels, is not used in learning word representations. In this paper, we focus on how to exploit these side information to improve word representations. We propose to incorporate document labels into the learning process of word representations in two frameworks: neural network and matrix factorization. The experimental results on word analogy and word similarity task show that our models can better capture the semantic and syntactic information than the original models. Our models also improve the performance of word representations on text classification task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {863–870},
numpages = {8}
}

@article{10.1109/TASLP.2016.2625458,
author = {Ying, Dongwen and Zhou, Ruohua and Li, Junfeng and Yan, Yonghong and Dongwen Ying and Ruohua Zhou and Junfeng Li and Yonghong Yan},
title = {Window-Dominant Signal Subspace Methods for Multiple Short-Term Speech Source Localization},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2625458},
doi = {10.1109/TASLP.2016.2625458},
abstract = {Signal subspace has been widely exploited to localize multiple speech sources. However, most signal subspace methods cannot count the number of sources, and do not make use of speech sparsity in the frequency domain. This paper presents a grid search window-dominant signal subspace GS-WDSS method and a closed-form WDSS CF-WDSS method to localize short-term speech sources. Such methods are based upon the generalized sparsity assumption that each window containing some time-adjacent bins is dominated by one source, as opposed to the conventional assumption that each individual bin is dominated by one source. The generalized assumption enables the principal eigenvector of the spatial correlation matrix on each window to span the signal subspace of the window-dominant source. The direction-of-arrival DOA of the dominant source is estimated from the principal eigenvector. The DOAs and the number of sources are eventually summarized from the DOA histogram of all dominant sources. The conventional assumption is a special case of the generalized assumption. By using the generalized assumption, the performance in estimating DOAs of the window-dominant sources is significantly improved at the cost of acceptable masking effect. The superiority of the proposed methods is verified by simulated and real experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {731–744},
numpages = {14}
}

@article{10.1109/TASLP.2017.2667880,
author = {Sarfjoo, Seyyed Saeed and Demiroglu, Cenk and King, Simon and Sarfjoo, Seyyed Saeed and Demiroglu, Cenk and King, Simon},
title = {Using Eigenvoices and Nearest-Neighbors in HMM-Based Cross-Lingual Speaker Adaptation With Limited Data},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2667880},
doi = {10.1109/TASLP.2017.2667880},
abstract = {Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. To that end, we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish-English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping, which performed significantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intralingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {839–851},
numpages = {13}
}

@article{10.1109/TASLP.2017.2670141,
author = {Karanasou, Penny and Wu, Chunyang and Gales, Mark and Woodland, Philip C. and Karanasou, Penny and Chunyang Wu and Gales, Mark and Woodland, Philip C.},
title = {I-Vectors and Structured Neural Networks for Rapid Adaptation of Acoustic Models},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2670141},
doi = {10.1109/TASLP.2017.2670141},
abstract = {A lot of interest has been risen in the last years on the adaptation of deep neural network DNN acoustic models, as the latter become the state-of-art in automatic speech recognition. This work focuses on approaches that allow for rapid and robust adaptation of such models. First, i-vectors are added to the DNN input as speaker-informed features. An informative prior is introduced to i-vector estimation to improve the robustness to limited adaptation data. I-vectors are then combined with a structured adaptive DNN, the multibasis adaptive neural network MBANN, and the complementarity of these adaptation techniques is investigated. Moreover, i-vectors are used to predict the MBANN transforms, avoiding the initial decoding pass and alignment. These approaches are evaluated on a U.S. English Broadcast News BN transcription task with two distinct sets of test data. The first, from the BN task and BN-style Youtube videos, yields test data acoustically matched to the training data, while the second set is from acoustically mismatched Youtube videos of diverse context. The performance gains from these schemes are found to be sensitive to the level of mismatch between training and test sets. The MBANN system combined with i-vector input achieves best performance for BN test sets. The i-vector-based predictive MBANN scheme is proven to be more robust to acoustically mismatched conditions and outperforms the other adaptation schemes in such scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {818–828},
numpages = {11}
}

@article{10.1109/TASLP.2016.2647702,
author = {Gannot, Sharon and Vincent, Emmanuel and Markovich-Golan, Shmulik and Ozerov, Alexey and Gannot, Sharon and Vincent, Emmanuel and Markovich-Golan, Shmulik and Ozerov, Alexey},
title = {A Consolidated Perspective on Multimicrophone Speech Enhancement and Source Separation},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2647702},
doi = {10.1109/TASLP.2016.2647702},
abstract = {Speech enhancement and separation are core problems in audio signal processing, with commercial applications in devices as diverse as mobile phones, conference call systems, hands-free systems, or hearing aids. In addition, they are crucial preprocessing steps for noise-robust automatic speech and speaker recognition. Many devices now have two to eight microphones. The enhancement and separation capabilities offered by these multichannel interfaces are usually greater than those of single-channel interfaces. Research in speech enhancement and separation has followed two convergent paths, starting with microphone array processing and blind source separation, respectively. These communities are now strongly interrelated and routinely borrow ideas from each other. Yet, a comprehensive overview of the common foundations and the differences between these approaches is lacking at present. In this paper, we propose to fill this gap by analyzing a large number of established and recent techniques according to four transverse axes: 1 the acoustic impulse response model, 2 the spatial filter design criterion, 3 the parameter estimation algorithm, and 4 optional postfiltering. We conclude this overview paper by providing a list of software and data resources and by discussing perspectives and future trends in the field.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {692–730},
numpages = {39}
}

@article{10.1109/TASLP.2017.2661705,
author = {Ghahabi, Omid and Hernando, Javier and Ghahabi, Omid and Hernando, Javier},
title = {Deep Learning Backend for Single and Multisession I-Vector Speaker Recognition},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2661705},
doi = {10.1109/TASLP.2017.2661705},
abstract = {The lack of labeled background data makes a big performance gap between cosine and Probabilistic Linear Discriminant Analysis PLDA scoring baseline techniques for i-vectors in speaker recognition. Although there are some unsupervised clustering techniques to estimate the labels, they cannot accurately predict the true labels and they also assume that there are several samples from the same speaker in the background data that could not be true in reality. In this paper, the authors make use of Deep Learning DL to fill this performance gap given unlabeled background data. To this goal, the authors have proposed an impostor selection algorithm and a universal model adaptation process in a hybrid system based on deep belief networks and deep neural networks to discriminatively model each target speaker. In order to have more insight into the behavior of DL techniques in both single- and multisession speaker enrollment tasks, some experiments have been carried out in this paper in both scenarios. Experiments on National Institute of Standards and Technology 2014 i-vector challenge show that 46% of this performance gap, in terms of minimum of the decision cost function, is filled by the proposed DL-based system. Furthermore, the score combination of the proposed DL-based system and PLDA with estimated labels covers 79% of this gap.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {807–817},
numpages = {11}
}

@article{10.1109/TASLP.2016.2572259,
author = {Chen, Yung-Yue and Zhang, Jia-Hao and Yung-Yue Chen and Jia-Hao Zhang},
title = {Background Noise Reduction Design for Dual Microphone Cellular Phones: Robust Approach},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2572259},
doi = {10.1109/TASLP.2016.2572259},
abstract = {Cellular phones are used almost everywhere for the communication purpose around the world. However, until now, in noisy environments, the speech communication quality of cellular phones is still awful because the noisy environments where people truly talk are quite complicated and there exists no suitable background noise reduction solution for dealing with this problem effectively. Based on these reasons, this research successfully proposes a novel noise reduction methodology to solve the background noise corrupted problem of cellular phones via a systematic design that comprises a dual microphone array hardware and a novel algorithm combined with a background noise whitening process, the system modeling method, and the robust estimation concept software. After practical experiments, a fact that this proposed method has the immunity to the really noisy environment where cellular phones are used can be found. This background noise reduction design possesses the following impressive advantages: an average of a 20 dB enhanced noise reduction performance the averaged value of state-of-the-art methods are about 10-15 dB, and an average similarity of 93% of pure speeches can be obtained for the purified speeches by applying this proposed method. Besides, this proposed method can be easily implemented in any of the one shelf or newly developed cellular phones with a really low cost.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {852–862},
numpages = {11}
}

@article{10.1109/TASLP.2017.2666425,
author = {Aneeja, G. and Yegnanarayana, B. and Aneeja, G. and Yegnanarayana, B.},
title = {Extraction of Fundamental Frequency From Degraded Speech Using Temporal Envelopes at High SNR Frequencies},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2666425},
doi = {10.1109/TASLP.2017.2666425},
abstract = {In this paper we propose a method for extracting the fundamental frequency fo from degraded speech signals using single frequency filtering SFF approach. The SFF of frequency-shifted speech signal gives high signal-to-noise ratio SNR segments at some frequencies and hence the SFF approach can be exploited for fo extraction using autocorrelation function of those segments. Since the fo is computed from the envelope of a single frequency component of the signal, the vocal tract resonances do not affect the fo extraction. The use of the high SNR frequency component in a given segment helps in overcoming the effects of degradations in the speech signal, without explicitly estimating the characteristics of noise. The proposed method of fo extraction is shown to give better performance for several types of real and simulated degradations, in comparison with some of the methods reported recently in the literature.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {829–838},
numpages = {10}
}

@article{10.1109/TASLP.2016.2644863,
author = {Yang, Xuefeng and Mao, Kezhi and Xuefeng Yang and Kezhi Mao},
title = {Task Independent Fine Tuning for Word Embeddings},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2644863},
doi = {10.1109/TASLP.2016.2644863},
abstract = {Representation learning of words, also known as word embedding technique, is based on the distributional hypothesis that words with similar semantic meanings have similar context. The selection of context window naturally has an influence on word vectors learned. However, it is found that the word vectors are often very sensitive to the defined context window, and unfortunately there is no unified optimal context window for all words. One impact of this issues is that, under a predefined context window, the semantic meanings of some words may not be well represented by the learned vectors. To alleviate the problem and improve word embeddings, we propose a task-independent fine-tuning framework in this paper. The main idea of the task-independent fine tuning is to integrate multiple word embeddings and lexical semantic resources to fine tune a target word embedding. The effectiveness of the proposed framework is tested by tasks of semantic similarity prediction, analogical reasoning, and sentence completion. Experiments results on six word embeddings and eight datasets show that the proposed fine-tuning framework could significantly improve word embeddings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {885–894},
numpages = {10}
}

@article{10.1109/TASLP.2017.2662232,
author = {Koizumi, Yuma and Niwa, Kenta and Hioka, Yusuke and Kobayashi, Kazunori and Ohmuro, Hitoshi and Koizumi, Yuma and Niwa, Kenta and Hioka, Yusuke and Kobayashi, Kazunori and Ohmuro, Hitoshi},
title = {Informative Acoustic Feature Selection to Maximize Mutual Information for Collecting Target Sources},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2662232},
doi = {10.1109/TASLP.2017.2662232},
abstract = {An informative acoustic-feature-selection method for collecting target sources in noisy environments is proposed. Wiener filtering is a powerful framework for sound-source enhancement. For Wiener-filter estimation, statistical-mapping functions, such as deep neural network based or Gaussian mixture model based mappings, have been used. In this framework, it is essential to find informative acoustic features that provide effective cues for Wiener-filter estimation. In this study, we measured the informativeness of acoustic features using mutual information between acoustic features and supervised Wiener-filter parameters, e.g., prior signal-to-noise ratios, and developed a method for automatically selecting informative acoustic features from a large number of feature candidates. To automatically select optimum features, we derived a differentiable objective function in proportion to mutual information based on the kernel method. Since the higher order correlations between acoustic features and Wiener-filter parameters are calculated using the kernel method, the statistical dependence of these variables is accurately calculated; thus, only meaningful acoustic features are selected. Through several experiments conducted on a mock sports field, we confirmed that the signal-to-distortion ratio score improved when various types of target sources were surrounded by loud cheering noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {768–779},
numpages = {12}
}

@article{10.1109/TASLP.2017.2661712,
author = {Spille, Constantin and Kollmeier, Birger and Meyer, Bernd T. and Spille, Constantin and Kollmeier, Birger and Meyer, Bernd T.},
title = {Combining Binaural and Cortical Features for Robust Speech Recognition},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2661712},
doi = {10.1109/TASLP.2017.2661712},
abstract = {The segregation of concurrent speakers and other sound sources is an important ability of the human auditory system, but is missing in most current systems for automatic speech recognition ASR, resulting in a large gap between human and machine performance. This study combines processing related to peripheral and cortical stages of the auditory pathway: A physiologically motivated binaural model estimates the positions of moving speakers to enhance the desired speech signal. Second, signals are converted to spectro-temporal Gabor features that resemble cortical speech representations and which have been shown to improve ASR in noisy conditions. Spectro-temporal Gabor features improve recognition results in all acoustic conditions under consideration compared with Mel-frequency cepstral coefficients. Binaural processing results in lower word error rates WERs in acoustic scenes with a concurrent speaker, whereas monaural processing should be preferred in the presence of a stationary masking noise. In-depth analysis of binaural processing identifies crucial processing steps such as localization of sound sources and estimation of the beamformer's noise coherence matrix, and shows how much each processing step affects the recognition performance in acoustic conditions with different complexity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {756–767},
numpages = {12}
}

@article{10.1109/TASLP.2017.2674966,
author = {Cumani, Sandro and Laface, Pietro and Cumani, Sandro and Laface, Pietro},
title = {Nonlinear I-Vector Transformations for PLDA-Based Speaker Recognition},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2674966},
doi = {10.1109/TASLP.2017.2674966},
abstract = {This paper proposes to estimate parametric nonlinear transformations of i-vectors for speaker recognition systems based on probabilistic linear discriminant analysis PLDA classification. The Gaussian PLDA model assumes that the i-vectors are distributed according to the standard normal distribution. However, it has been shown that the i-vectors are better modeled, for example, by Heavy-Tailed distributions, and that significant improvement of the classification performance can be obtained by whitening and length normalizing the i-vectors. In this paper, we propose to transform the i-vectors so that their distribution becomes more suitable to discriminate speakers using the PLDA model. This is performed by means of a sequence of affine and nonlinear transformations whose parameters are obtained by maximum likelihood estimation on the development set. Another contribution of this paper is the reduction of the mismatch between the development and evaluation i-vector length distributions by means of a scaling factor tuned for the estimated i-vector distribution, rather than by means of a blind length normalization. Relative improvement between 7% and 14% of the detection cost function was obtained with the proposed technique on the NIST SRE-2010 and SRE-2012 evaluation datasets, using both the traditional GMM/UBM and the hybrid DNN/GMM-based systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {908–919},
numpages = {12}
}

@article{10.1109/TASLP.2017.2656805,
author = {Wood, Sean U. N. and Rouat, Jean and Dupont, Stephane and Pironkov, Gueorgui and Wood, Sean U. N. and Rouat, Jean and Dupont, Stephane and Pironkov, Gueorgui},
title = {Blind Speech Separation and Enhancement With GCC-NMF},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2656805},
doi = {10.1109/TASLP.2017.2656805},
abstract = {We present a blind source separation algorithm named GCC-NMF that combines unsupervised dictionary learning via non-negative matrix factorization NMF with spatial localization via the generalized cross correlation GCC method. Dictionary learning is performed on the mixture signal, with separation subsequently achieved by grouping dictionary atoms, at each point in time, according to their spatial origins. The resulting source separation algorithm is simple yet flexible, requiring no prior knowledge or information. Separation quality is evaluated for three tasks using stereo recordings from the publicly available SiSEC signal separation evaluation campaign: 3 and 4 concurrent speakers in reverberant environments, speech mixed with real-world background noise, and noisy recordings of a moving speaker. Performance is quantified using perceptually motivated and SNR-based measures with the PEASS and BSS Eval toolkits, respectively. We evaluate the effects of model parameters on separation quality, and compare our approach with other unsupervised and semi-supervised speech separation and enhancement approaches. We show that GCC-NMF is a flexible source separation algorithm, outperforming task-specific approaches in each of the three settings, including both blind as well as several informed approaches that require prior knowledge or information.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {745–755},
numpages = {11}
}

@article{10.1109/TASLP.2017.2665341,
author = {Higuchi, Takuya and Ito, Nobutaka and Araki, Shoko and Yoshioka, Takuya and Delcroix, Marc and Nakatani, Tomohiro and Higuchi, Takuya and Ito, Nobutaka and Araki, Shoko and Yoshioka, Takuya and Delcroix, Marc and Nakatani, Tomohiro},
title = {Online MVDR Beamformer Based on Complex Gaussian Mixture Model With Spatial Prior for Noise Robust ASR},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2665341},
doi = {10.1109/TASLP.2017.2665341},
abstract = {This paper considers acoustic beamforming for noise robust automatic speech recognition. A beamformer attenuates background noise by enhancing sound components coming from a direction specified by a steering vector. Hence, accurate steering vector estimation is paramount for successful noise reduction. Recently, time-frequency masking has been proposed to estimate the steering vectors that are used for a beamformer. In particular, we have developed a new form of this approach, which uses a speech spectral model based on a complex Gaussian mixture model CGMM to estimate the time-frequency masks needed for steering vector estimation, and extended the CGMM-based beamformer to an online speech enhancement scenario. Our previous experiments showed that the proposed CGMM-based approach outperforms a recently proposed mask estimator based on a Watson mixture model and the baseline speech enhancement system of the CHiME-3 challenge. This paper provides additional experimental results for our online processing, which achieves performance comparable to that of batch processing with a suitable block-batch size. This online version reduces the CHiME-3 word error rate WER on the evaluation set from 8.37% to 8.06%. Moreover, in this paper, we introduce a probabilistic prior distribution for a spatial correlation matrix a CGMM parameter, which enables more stable steering vector estimation in the presence of interfering speakers. In practice, the performance of the proposed online beamformer degrades with observations that contain only noise or/and interference because of the failure of the CGMM parameter estimation. The introduced spatial prior enables the target speaker's parameter to avoid overfitting to noise or/and interference. Experimental results show that the spatial prior reduces the WER from 38.4% to 29.2% in a conversation recognition task compared with the CGMM-based approach without the prior, and outperforms a conventional online speech enhancement approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {780–793},
numpages = {14}
}

@article{10.1109/TASLP.2017.2662479,
author = {Nakamura, Eita and Yoshii, Kazuyoshi and Sagayama, Shigeki and Nakamura, Eita and Yoshii, Kazuyoshi and Sagayama, Shigeki},
title = {Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2662479},
doi = {10.1109/TASLP.2017.2662479},
abstract = {In a recent conference paper, we have reported a rhythm transcription method based on a merged-output hidden Markov model HMM that explicitly describes the multiple-voice structure of polyphonic music. This model solves a major problem of conventional methods that could not properly describe the nature of multiple voices as in polyrhythmic scores or in the phenomenon of loose synchrony between voices. In this paper, we present a complete description of the proposed model and develop an inference technique, which is valid for any merged-output HMMs, for which output probabilities depend on past events. We also examine the influence of the architecture and parameters of the method in terms of accuracies of rhythm transcription and voice separation and perform comparative evaluations with six other algorithms. Using MIDI recordings of classical piano pieces, we found that the proposed model outperformed other methods by more than 12 points in the accuracy for polyrhythmic performances and performed almost as good as the best one for non-polyrhythmic performances. This reveals the state-of-the-art methods of rhythm transcription for the first time in the literature. Publicly available source codes are also provided for future comparisons.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {794–806},
numpages = {13}
}

@article{10.1109/TASLP.2017.2672398,
author = {Zhang, Shiliang and Liu, Cong and Jiang, Hui and Wei, Si and Dai, Lirong and Hu, Yu and Shiliang Zhang and Cong Liu and Hui Jiang and Si Wei and Lirong Dai and Yu Hu},
title = {Nonrecurrent Neural Structure for Long-Term Dependence},
year = {2017},
issue_date = {April 2017},
publisher = {IEEE Press},
volume = {25},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2672398},
doi = {10.1109/TASLP.2017.2672398},
abstract = {In this paper, we propose a novel neural network structure, namely feedforward sequential memory networks FSMN, to model long-term dependence in time series without using recurrent feedback. The proposed FSMN is a standard fully connected feedforward neural network equipped with some learnable memory blocks in its hidden layers. The memory blocks use a tapped-delay line structure to encode the long context information into a fixed-size representation as short-term memory mechanism which are somehow similar to the time-delay neural networks layers. We have evaluated the FSMNs in several standard benchmark tasks, including speech recognition and language modeling. Experimental results have shown that FSMNs outperform the conventional recurrent neural networks RNN while can be learned much more reliably and faster in modeling sequential signals like speech or language. Moreover, we also propose a compact feedforward sequential memory networks cFSMN by combining FSMN with low-rank matrix factorization and make a slight modification to the encoding method used in FSMNs in order to further simplify the network architecture. On the speech recognition Switchboard task, the proposed cFSMN structures can reduce the model size by 60% and speed up the learning by more than seven times while the model can still significantly outperform the popular bidirectional LSTMs for both frame-level cross-entropy criterion-based training and MMI-based sequence training.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {871–884},
numpages = {14}
}

@article{10.5555/3105672.3105691,
author = {Kuklasinski, Adam and Doclo, Simon and Holdt Jensen, Soren and Jensen, Jesper},
title = {Correction to "Maximum Likelihood PSD Estimation for Speech Enhancement in Reverberation and Noise" [Sep 16 1599-1612]},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
abstract = {Presents corrections to the paper, "A Novel Approach Based on Marine Radar Data Analysis for High-Resolution Bathymetry Map Generation."},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {687},
numpages = {1}
}

@article{10.1109/TASLP.2017.2653941,
author = {Omachi, Motoi and Ogawa, Tetsuji and Kobayashi, Tetsunori and Omachi, Motoi and Ogawa, Tetsuji and Kobayashi, Tetsunori},
title = {Associative Memory Model-Based Linear Filtering and Its Application to Tandem Connectionist Blind Source Separation},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2653941},
doi = {10.1109/TASLP.2017.2653941},
abstract = {We propose a blind source separation method that yields high-quality speech with low distortion. Time-frequency TF masking can effectively reduce interference, but it produces nonlinear distortion. By contrast, linear filtering using a separation matrix such as independent vector analysis IVA can avoid nonlinear distortion, but the separation performance is reduced under reverberant conditions. The tandem connectionist approach combines several separation methods and it has been used frequently to compensate for the disadvantages of these methods. In this study, we propose associative memory model AMM-based linear filtering and a tandem connectionist framework, which applies TF masking followed by linear filtering. By using AMM trained with speech spectra to optimize the separation matrix, the proposed linear filtering method considers the properties of speech that are not considered explicitly in IVA, such as the harmonic components of spectra. TF masking is applied in the proposed tandem connectionist framework to reduce unwanted components that hinder the optimization of the separation matrix, and it is approximated by using a linear separation matrix to reduce nonlinear distortion. The results obtained in simultaneous speech separation experiments demonstrate that although the proposed linear filtering method can increase the signal-to-distortion ratio SDR and signal-to-interference ratio SIR compared with IVA, the proposed tandem connectionist framework can obtain greater increases in SDR and SIR, and it reduces the phoneme error rate more than the proposed linear filtering method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {637–650},
numpages = {14}
}

@article{10.1109/TASLP.2016.2647713,
author = {Bahari, Mohamad Hasan and Bertrand, Alexander and Moonen, Marc and Bahari, Mohamad Hasan and Bertrand, Alexander and Moonen, Marc},
title = {Blind Sampling Rate Offset Estimation for Wireless Acoustic Sensor Networks Through Weighted Least-Squares Coherence Drift Estimation},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2647713},
doi = {10.1109/TASLP.2016.2647713},
abstract = {Microphone arrays allow to exploit the spatial coherence between simultaneously recorded microphone signals, e.g., to perform speech enhancement, i.e., to extract a speech signal and reduce background noise. However, in systems where the microphones are not sampled in a synchronous fashion, as it is often the case in wireless acoustic sensor networks, a sampling rate offset SRO exists between signals recorded in different nodes, which severely affects the speech enhancement performance. To avoid this performance reduction, the SRO should be estimated and compensated for. In this paper, we propose a new approach to blind SRO estimation for an asynchronous wireless acoustic sensor network, which exploits the phase drift of the coherence between the asynchronous microphones signals. We utilize the fact that the SRO causes a linearly increasing time delay between two signals and hence a linearly increasing phase-shift in the short-time Fourier transform domain. The increasing phase shift, observed as a phase drift of the coherence between the signals, is used in a weighted least-squares framework to estimate the SRO. This method is referred to as least-squares coherence drift LCD. Experimental results in different real-world recording and simulated scenarios show the effectiveness of LCD compared to different benchmark methods. The LCD is effective even for short signal segments. We finally demonstrate that the use of the LCD within a conventional compensation approach eliminates the performance loss due to SRO in a speech enhancement algorithm based on the multichannel Wiener filter.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {674–686},
numpages = {13}
}

@article{10.1109/TASLP.2016.2643280,
author = {Do, Quoc Truong and Toda, Tomoki and Neubig, Graham and Sakti, Sakriani and Nakamura, Satoshi and Quoc Truong Do and Toda, Tomoki and Neubig, Graham and Sakti, Sakriani and Nakamura, Satoshi},
title = {Preserving Word-Level Emphasis in Speech-to-Speech Translation},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2643280},
doi = {10.1109/TASLP.2016.2643280},
abstract = {Speech-to-speech translation S2ST is a technology that translates speech across languages, which can remove barriers in cross-lingual communication. In the conventional S2ST systems, the linguistic meaning of speech was translated, but paralinguistic information conveying other features of the speech such as emotion or emphasis were ignored. In this paper, we propose a method to translate paralinguistic information, specifically focusing on emphasis. The method consists of a series of components that can accurately translate emphasis using all acoustic features of speech. First, linear-regression hidden semi-Markov models LRHSMMs are used to estimate a real-numbered emphasis value for every word in an utterance, resulting in a sequence of values for the utterance. After that the emphasis translation module translates the estimated emphasis sequence into a target language emphasis sequence using a conditional random field model considering the features of emphasis levels, words, and part-of-speech tags. Finally, the speech synthesis module synthesizes emphasized speech with LR-HSMMs, taking into account the translated emphasis sequence and transcription. The results indicate that our translation model can translate emphasis information, correctly emphasizing words in the target language with 91.6% F-measure by objective evaluation. A listening test with human subjects further showed that they could identify the emphasized words with 87.8% F-measure, and that the naturalness of the audio was preserved.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {544–556},
numpages = {13}
}

@article{10.1109/TASLP.2016.2641901,
author = {Sharma, Bidisha and Prasanna, S. R. Mahadeva and Sharma, Bidisha and Mahadeva Prasanna, S. R.},
title = {Sonority Measurement Using System, Source, and Suprasegmental Information},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2641901},
doi = {10.1109/TASLP.2016.2641901},
abstract = {Sonorant sounds are characterized by regions with a prominent formant structure, high energy, and high degree of periodicity. In this work, the vocal-tract system, excitation source, and suprasegmental features derived from the speech signal are analyzed to measure the sonority information present in each of them. Vocal-tract system information is extracted from the Hilbert envelope of the numerator of the group-delay function. It is derived from a zero-time-windowed speech signal that provides a better resolution of the formants. A 5-D feature set is computed from the estimated formants to measure the prominence of the spectral peaks. A feature representing strength of excitation is derived from the Hilbert envelope of linear prediction residual, which represents the source information. Correlation of speech over ten consecutive pitch periods is used as the suprasegmental feature representing periodicity information. The combination of evidence from the three different aspects of speech provides a better discrimination among different sonorant classes, compared to the baseline mel frequency cepstral coefficient features. The usefulness of the proposed sonority feature is demonstrated in the tasks of phoneme recognition and sonorant classification.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {505–518},
numpages = {14}
}

@article{10.1109/TASLP.2016.2637280,
author = {Wang, Zhongqing and Lee, Sophia Yat Mei and Li, Shoushan and Zhou, Guodong and Zhongqing Wang and Mei Lee, Sophia Yat and Shoushan Li and Guodong Zhou},
title = {Emotion Analysis in Code-Switching Text With Joint Factor Graph Model},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2637280},
doi = {10.1109/TASLP.2016.2637280},
abstract = {Previous research on emotions analysis has placed much emphasis in monolingual instead of bilingual text. However, emotions on social media platforms are often found in bilingual or code-switching posts. Different from monolingual text, emotions in code-switching text can be expressed in both monolingual and bilingual forms. Moreover, more than one emotion can be expressed within a single post; yet they tend to be related in some ways which offers some implications. It is thus necessary to consider the correlation between different emotions. In this paper, a joint factor graph model is proposed to address this issue. In particular, attribute functions of the factor graph model are utilized to learn both monolingual and bilingual information from each post, factor functions are used to explore the relationship among different emotions, and a belief propagation algorithm is employed to learn and predict the model. Empirical studies demonstrate the importance of emotion analysis in code-switching text and the effectiveness of our proposed joint learning model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {469–480},
numpages = {12}
}

@article{10.1109/TASLP.2017.2651373,
author = {Farmani, Mojtaba and Pedersen, Michael Syskind and Tan, Zheng-Hua and Jensen, Jesper and Farmani, Mojtaba and Pedersen, Michael Syskind and Zheng-Hua Tan and Jensen, Jesper},
title = {Informed Sound Source Localization Using Relative Transfer Functions for Hearing Aid Applications},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2651373},
doi = {10.1109/TASLP.2017.2651373},
abstract = {Recent hearing aid systems HASs can connect to a wireless microphone worn by the talker of interest. This feature gives the HASs access to a noise-free version of the target signal. In this paper, we address the problem of estimating the target sound direction of arrival DoA for a binaural HAS given access to the noise-free content of the target signal. To estimate the DoA, we present a maximum-likelihood framework which takes the shadowing effect of the user's head on the received signals into account by modeling the relative transfer functions RTFs between the HAS's microphones. We propose three different RTF models which have different degrees of accuracy and individualization. Furthermore, we show that the proposed DoA estimators can be formulated in terms of inverse discrete Fourier transforms to evaluate the likelihood function computationally efficiently. We extensively assess the performance of the proposed DoA estimators for various DoAs, signal to noise ratios, and in different noisy and reverberant situations. The results show that the proposed estimators improve the performance markedly over other recently proposed “informed” DoA estimator.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {611–623},
numpages = {13}
}

@article{10.1109/TASLP.2016.2636445,
author = {He, Qi and Bao, Feng and Bao, Changchun and Qi He and Feng Bao and Changchun Bao},
title = {Multiplicative Update of Auto-Regressive Gains for Codebook-Based Speech Enhancement},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2636445},
doi = {10.1109/TASLP.2016.2636445},
abstract = {In this paper, we present a novel method for estimating short-term linear predictive parameters of speech and noise in the codebook-driven Wiener filtering speech enhancement method. We only use pretrained spectral shape codebook of speech to model the a priori information about linear predictive coefficients of speech, and the spectral shape of noise is estimated online directly instead of using noise codebook to solve the problem of noise classification. Differing from the existing codebook-driven methods that the linear predictive gains of speech and noise are estimated by maximum-likelihood method, in the proposed method we exploit a multiplicative update rule to estimate the linear predictive gains more accurately. The estimated gains can help to reserve more speech components in the enhanced speech. Meanwhile, the Bayesian parameter-estimator without the noise codebook is also developed. Moreover, we develop an improved codebook-driven Wiener filter combined with the speech-presence probability, so that the proposed method achieves the goal of removing the residual noise between the harmonics of noisy speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {457–468},
numpages = {12}
}

@article{10.1109/TASLP.2016.2641904,
author = {Doire, Clement Samuel Joseph and Brookes, Mike and Naylor, Patrick A. and Hicks, Christopher M. and Betts, Dave and Dmour, Mohammad A. and Jensen, Soren Holdt and Doire, Clement Samuel Joseph and Brookes, Mike and Naylor, Patrick A. and Hicks, Christopher M. and Betts, Dave and Dmour, Mohammad A. and Jensen, Soren Holdt},
title = {Single-Channel Online Enhancement of Speech Corrupted by Reverberation and Noise},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2641904},
doi = {10.1109/TASLP.2016.2641904},
abstract = {This paper proposes an online single-channel speech enhancement method designed to improve the quality of speech degraded by reverberation and noise. Based on an autoregressive model for the reverberation power and on a hidden Markov model for clean speech production, a Bayesian filtering formulation of the problem is derived and online joint estimation of the acoustic parameters and mean speech, reverberation, and noise powers is obtained in mel-frequency bands. From these estimates, a real-valued spectral gain is derived and spectral enhancement is applied in the short-time Fourier transform STFT domain. The method yields state-of-the-art performance and greatly reduces the effects of reverberation and noise while improving speech quality and preserving speech intelligibility in challenging acoustic environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {572–587},
numpages = {16}
}

@article{10.1109/TASLP.2016.2635445,
author = {Lee, Hung-Yi and Tseng, Bo-Hsiang and Wen, Tsung-Hsien and Tsao, Yu and Hung-Yi Lee and Bo-Hsiang Tseng and Tsung-Hsien Wen and Yu Tsao},
title = {Personalizing Recurrent-Neural-Network-Based Language Model by Social Network},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2635445},
doi = {10.1109/TASLP.2016.2635445},
abstract = {With the popularity of mobile devices, personalized speech recognizers have become more attainable and are highly attractive. Since each mobile device is used primarily by a single user, it is possible to have a personalized recognizer that well matches the characteristics of the individual user. Although acoustic model personalization has been investigated for decades, much less work has been reported on personalizing language models, presumably because of the difficulties in collecting sufficient personalized corpora. In this paper, we propose a general framework for personalizing recurrent-neural-network-based language models RNNLMs using data collected from social networks, including the posts of many individual users and friend relationships among the users. Two major directions for this are model-based and feature-based RNNLM personalization. In model-based RNNLM personalization, the RNNLM parameters are fine-tuned to an individual user's wording patterns by incorporating social texts posted by the target user and his or her friends. For the feature-based approach, the RNNLM model parameters are fixed across users, but the RNNLM input features are instead augmented with personalized information. Both approaches not only drastically reduce the model perplexity, but also moderately reduce word error rates in n-best rescoring tests.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {519–530},
numpages = {12}
}

@article{10.1109/TASLP.2016.2639323,
author = {Tang, Zhiyuan and Li, Lantian and Wang, Dong and Vipperla, Ravichander and Zhiyuan Tang and Lantian Li and Dong Wang and Vipperla, Ravichander},
title = {Collaborative Joint Training With Multitask Recurrent Model for Speech and Speaker Recognition},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2639323},
doi = {10.1109/TASLP.2016.2639323},
abstract = {Automatic speech and speaker recognition are traditionally treated as two independent tasks and are studied separately. The human brain in contrast deciphers the linguistic content, and the speaker traits from the speech in a collaborative manner. This key observation motivates the work presented in this paper. A collaborative joint training approach based on multitask recurrent neural network models is proposed, where the output of one task is backpropagated to the other tasks. This is a general framework for learning collaborative tasks and fits well with the goal of joint learning of automatic speech and speaker recognition. Through a comprehensive study, it is shown that the multitask recurrent neural net models deliver improved performance on both automatic speech and speaker recognition tasks as compared to single-task systems. The strength of such multitask collaborative learning is analyzed, and the impact of various training configurations is investigated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {493–504},
numpages = {12}
}

@article{10.1109/TASLP.2017.2651406,
author = {Ming, Ji and Crookes, Danny and Ji Ming and Crookes, Danny},
title = {Speech Enhancement Based on Full-Sentence Correlation and Clean Speech Recognition},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2651406},
doi = {10.1109/TASLP.2017.2651406},
abstract = {Conventional speech enhancement methods, based on frame, multiframe, or segment estimation, require knowledge about the noise. This paper presents a new method that aims to reduce or effectively remove this requirement. It is shown that by using the zero-mean normalized correlation coefficient ZNCC as the comparison measure, and by extending the effective length of speech segment matching to sentence-long speech utterances, it is possible to obtain an accurate speech estimate from noise without requiring specific knowledge about the noise. The new method, thus, could be used to deal with unpredictable noise or noise without proper training data. This paper is focused on realizing and evaluating this potential. We propose a novel realization that integrates full-sentence speech correlation with clean speech recognition, formulated as a constrained maximization problem, to overcome the data sparsity problem. Then we propose an efficient implementation algorithm to solve this constrained maximization problem to produce speech sentence estimates. For evaluation, we build the new system on one training dataset and test it on two different test datasets across two databases, for a range of different noises including highly nonstationary ones. It is shown that the new approach, without any estimation of the noise, is able to significantly outperform conventional methods that use optimized noise tracking, in terms of various objective measures including automatic speech recognition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {531–543},
numpages = {13}
}

@article{10.1109/TASLP.2017.2651398,
author = {Girin, Laurent and Hueber, Thomas and Alameda-Pineda, Xavier and Girin, Laurent and Hueber, Thomas and Alameda-Pineda, Xavier},
title = {Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2651398},
doi = {10.1109/TASLP.2017.2651398},
abstract = {This paper addresses the adaptation of an acoustic-articulatory inversion model of a reference speaker to the voice of another source speaker, using a limited amount of audio-only data. In this study, the articulatory-acoustic relationship of the reference speaker is modeled by a Gaussian mixture model and inference of articulatory data from acoustic data is made by the associated Gaussian mixture regression GMR. To address speaker adaptation, we previously proposed a general framework called Cascaded-GMR C-GMR which decomposes the adaptation process into two consecutive steps: spectral conversion between source and reference speaker and acoustic-articulatory inversion of converted spectral trajectories. In particular, we proposed the integrated C-GMR technique IC-GMR in which both steps are tied together in the same probabilistic model. In this paper, we extend the C-GMR framework with another model called Joint-GMR J-GMR. Contrary to the IC-GMR, this model aims at exploiting all potential acoustic-articulatory relationships, including those between the source speaker's acoustics and the reference speaker's articulation. We present the full derivation of the exact expectation-maximization EM training algorithm for the J-GMR. It exploits the missing data methodology of machine learning to deal with limited adaptation data. We provide an extensive evaluation of the J-GMR on both synthetic acoustic-articulatory data and on the multispeaker MOCHA EMA database. We compare the J-GMR performance to other models of the C-GMR framework, notably the IC-GMR, and discuss their respective merits.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {662–673},
numpages = {12}
}

@article{10.1109/TASLP.2017.2651361,
author = {Sheikh, Imran and Fohr, Dominique and Illina, Irina and Linares, Georges and Sheikh, Imran and Fohr, Dominique and Illina, Irina and Linares, Georges},
title = {Modelling Semantic Context of OOV Words in Large Vocabulary Continuous Speech Recognition},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2651361},
doi = {10.1109/TASLP.2017.2651361},
abstract = {The diachronic nature of broadcast news data leads to the problem of out-of-vocabulary OOV words in large vocabulary continuous speech recognition LVCSR systems. Analysis of OOV words reveals that a majority of them are proper names PNs. However, PNs are important for automatic indexing of audio-video content and for obtaining reliable automatic transcriptions. In this paper, we focus on the problem of OOV PNs in diachronic audio documents. To enable the recovery of the PNs missed by the LVCSR system, relevant OOV PNs are retrieved by exploiting the semantic context of the LVCSR transcriptions. For retrieval of OOV PNs, we explore topic and semantic context derived from latent Dirichlet allocation LDA topic models, continuous word vector representations and the neural bag-of-words NBOW model which is capable of learning task specific word and context representations. We propose a neural bag-of-weighted words NBOW2 model which learns to assign higher weights to words that are important for retrieval of an OOV PN. With experiments on French broadcast news videos, we show that the NBOW and NBOW2 models outperform the methods based on raw embeddings from LDA and Skip-gram models. Combining the NBOW and NBOW2 models gives a faster convergence during training. Second pass speech recognition experiments, in which the LVCSR vocabulary and language model are updated with the retrieved OOV PNs, demonstrate the effectiveness of the proposed context models.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {598–610},
numpages = {13}
}

@article{10.1109/TASLP.2017.2651391,
author = {Vikram, C. M. and Prasanna, S. R. Mahadeva and Vikram, C. M. and Mahadeva Prasanna, S. R.},
title = {Epoch Extraction From Telephone Quality Speech Using Single Pole Filter},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2651391},
doi = {10.1109/TASLP.2017.2651391},
abstract = {Epoch extraction from speech involves the suppression of vocal tract resonances, either by linear prediction based inverse filtering or filtering at very low frequency. Degradations due to channel effect and significant attenuation of low frequency components <;300 Hz create challenges for the epoch extraction from telephone quality speech. An epoch extraction method is proposed that considers the vertical striations present in the time-frequency representation of voiced speech as the representative candidates for the epochs. Time-frequency representation with better localized vertical striations is estimated using single pole filter based filter bank. The time marginal of time-frequency representation is computed to locate the epochs. The proposed algorithm is evaluated on the database of five speakers, which provide simultaneous speech and electroglottographic recordings. Telephone quality speech is simulated using G.191 software tools. The identification rate of the state-of-the-art methods degrades substantially for the telephone quality speech whereas that of the proposed method remains the same, comparable to that of clean speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {624–636},
numpages = {13}
}

@article{10.1109/TASLP.2017.2651377,
author = {Sizov, Aleksandr and Lee, Kong Aik and Kinnunen, Tomi and Sizov, Aleksandr and Kong Aik Lee and Kinnunen, Tomi},
title = {Direct Optimization of the Detection Cost for I-Vector-Based Spoken Language Recognition},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2651377},
doi = {10.1109/TASLP.2017.2651377},
abstract = {We explore a method to boost discriminative capabilities of probabilistic linear discriminant analysis PLDA model without losing its generative advantages. We show a sequential projection and training steps leading to a classifier that operates in the original i-vector space but is discriminatively trained in a low-dimensional PLDA latent subspace. We use extended Baum-Welch technique to optimize the model with respect to two objective functions for discriminative training. One of them is the well-known maximum mutual information objective, while the other one is a new objective that we propose to approximate the language detection cost. We evaluate the performance on NIST language recognition evaluation LRE 2015 and our development dataset comprised of the utterances from previous LREs. We improve the detection cost by 10% and 6% relative compared to our fine-tuned generative and discriminative baselines, and by 10% over the best of our previously reported results. The proposed approximation method of the cost function and PLDA subspace training are applicable for a broad range of tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {588–597},
numpages = {10}
}

@article{10.1109/TASLP.2016.2639322,
author = {Bellur, Ashwin and Elhilali, Mounya and Bellur, Ashwin and Elhilali, Mounya},
title = {Feedback-Driven Sensory Mapping Adaptation for Robust Speech Activity Detection},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2639322},
doi = {10.1109/TASLP.2016.2639322},
abstract = {Parsing natural acoustic scenes using computational methodologies poses many challenges. Given the rich and complex nature of the acoustic environment, data mismatch between train and test conditions is a major hurdle in data-driven audio processing systems. In contrast, the brain exhibits a remarkable ability at segmenting acoustic scenes with relative ease. When tackling challenging listening conditions that are often faced in everyday life, the biological system relies on a number of principles that allow it to effortlessly parse its rich soundscape. In the current study, we leverage a key principle employed by the auditory system: its ability to adapt the neural representation of its sensory input in a high-dimensional space. We propose a framework that mimics this process in a computational model for robust speech activity detection. The system employs a 2-D Gabor filter bank whose parameters are returned offline to improve the separability between the feature representation of speech and nonspeech sounds. This retuning process, driven by feedback from statistical models of speech and nonspeech classes, attempts to minimize the misclassification risk of mismatched data, with respect to the original statistical models. We hypothesize that this risk minimization procedure results in an emphasis of unique speech and nonspeech modulations in the high-dimensional space. We show that such an adapted system is indeed robust to other novel conditions, with a marked reduction in equal error rates for a variety of databases with additive and convolutive noise distortions. We discuss the lessons learned from biology with regard to adapting to an ever-changing acoustic environment and the impact on building truly intelligent audio processing systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {481–492},
numpages = {12}
}

@article{10.1109/TASLP.2017.2655259,
author = {Cherkassky, Dani and Gannot, Sharon and Cherkassky, Dani and Gannot, Sharon},
title = {Blind Synchronization in Wireless Acoustic Sensor Networks},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2655259},
doi = {10.1109/TASLP.2017.2655259},
abstract = {The challenge of blindly resynchronizing the data acquisition processes in a wireless acoustic sensor network WASN is addressed in this paper. The sampling rate offset SRO is precisely modeled as a time scaling. The applicability of a wideband correlation processor for estimating the SRO, even in a reverberant and multiple source environment, is presented. An explicit expression for the ambiguity function, which in our case involves time scaling of the received signals, is derived by applying truncated band-limited interpolation. We then propose the recursive band-limited interpolation RBI algorithm for recursive SRO estimation. A complete resynchronization scheme utilizing the RBI algorithm, in parallel with the SRO compensation module, is presented. The resulting resynchronization method operates in the time domain in a sequential manner and is, thus, capable of tracking a potentially time-varying SRO. We compared the performance of the proposed RBI algorithm to other available methods in a simulation study. The importance of resynchronization in a beamforming application is demonstrated by both a simulation study and experiments with a real WASN. Finally, we present an experimental study evaluating the expected SRO level between typical data acquisition devices.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {651–661},
numpages = {11}
}

@article{10.1109/TASLP.2016.2644262,
author = {Li, Zhenghua and Chao, Jiayuan and Zhang, Min and Chen, Wenliang and Zhang, Meishan and Fu, Guohong and Zhenghua Li and Jiayuan Chao and Min Zhang and Wenliang Chen and Meishan Zhang and Guohong Fu},
title = {Coupled POS Tagging on Heterogeneous Annotations},
year = {2017},
issue_date = {March 2017},
publisher = {IEEE Press},
volume = {25},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2644262},
doi = {10.1109/TASLP.2016.2644262},
abstract = {The limited scale and genre coverage of labeled data greatly hinders the effectiveness of supervised models, especially when analyzing spoken languages, such as texts transcribed from speech and informal text including tweets and product comments in Internet. In order to effectively utilize multiple labeled datasets with heterogeneous annotations for the same task, this paper proposes a coupled sequence labeling model that can directly learn and infer two heterogeneous annotations simultaneously, using Chinese part-of-speech POS tagging as our case study. The key idea is to bundle two sets of POS tags together e.g., “[NN, n]n, and build a conditional random field CRF based tagging model in the enlarged space of bundled tags with the help of ambiguous labeling. To train our model on two nonoverlapping datasets that each has only one-side tags, we transform a one-side tag into a set of bundled tags by concatenating the tag with every possible tag at the missing side according to a predefined context-free tag-to-tag mapping function, thus producing ambiguous labeling as weak supervision. We design and investigate four different context-free tag-to-tag mapping functions, and find out that the coupled model achieves its best performance when each one-side tag is mapped to all tags at the other side namely complete mapping, indicating that the model can effectively learn the loose mapping between the two heterogeneous annotations, without the need of manually designed mapping rules. Moreover, we propose a context-aware online pruning strategy that can more accurately capture mapping relationships between annotations based on contextual evidences and thus effectively solve the severe inefficiency problem with our coupled model under complete mapping, making it comparable with the baseline CRF model. Experiments on benchmark datasets show that our coupled model significantly outperforms the state-of-the-art baselines on both one-side POS tagging and annotation conversion tasks. The codes and newly annotated data are released for research usage.1},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {557–571},
numpages = {15}
}

@article{10.1109/TASLP.2016.2633799,
author = {Ma, Yaping and Xiao, Yegui and Yaping Ma and Yegui Xiao},
title = {A New Strategy for Online Secondary-Path Modeling of Narrowband Active Noise Control},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2633799},
doi = {10.1109/TASLP.2016.2633799},
abstract = {Online secondary-path modeling SPM based on the use of random auxiliary noise is a very promising and attractive technique for implementing active noise control ANC. In this paper, a new strategy is proposed for the online SPM of narrowband ANC NANC. A bandpass filter bank consisting of IIR notch filters is applied to the residual noise to separate the uncancelled sinusoidal noise from other broadband noises due to the injected auxiliary noise and an additive noise. The extracted sinusoidal noise is used simultaneously to scale the auxiliary noise, to clean the desired signal for the SPM subsystem, and to update the controller. The benefits of this strategy are twofold. It can effectively reduce the contribution of injected auxiliary noise to the residual noise power. Meanwhile, it can also considerably raise the convergence rate of overall system as well as the accuracy of online SPM. Preliminary analysis is also provided to support the proposed strategy. Extensive simulations with synthetic/real secondary paths and noises are performed to demonstrate the superiority of the proposed SPM strategy over the conventional techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {420–434},
numpages = {15}
}

@article{10.1109/TASLP.2016.2633811,
author = {Samarasinghe, Prasanga N. and Abhayapala, Thushara D. and Chen, Hanchi and Samarasinghe, Prasanga N. and Abhayapala, Thushara D. and Hanchi Chen},
title = {Estimating the Direct-to-Reverberant Energy Ratio Using a Spherical Harmonics-Based Spatial Correlation Model},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2633811},
doi = {10.1109/TASLP.2016.2633811},
abstract = {The direct-to-reverberant ratio DRR, which describes the energy ratio between the direct and reverberant component of a soundfield, is an important parameter in many audio applications. In this paper, we present a multichannel algorithm, which utilizes the blind recordings of a spherical microphone array to estimate the DRR of interest. The algorithm is developed based on a spatial correlation model formulated in the spherical harmonics domain. This model expresses the cross correlation matrix of the recorded soundfield coefficients in terms of two spatial correlation matrices, one for direct sound and the other for reverberation. While the direct path arrives from the source, the reverberant path is considered to be a nondiffuse soundfield with varying directional gains. The direct and reverberant sound energies are estimated from the aforementioned spatial correlation model, which then leads to the DRR estimation. The practical feasibility of the proposed algorithm was evaluated using the speech corpus of the acoustic characterization of environments challenge. The experimental results revealed that the proposed method was able to effectively estimate the DRR of a large collection of reverberant speech recordings including various environmental noise types, room types and speakers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {310–319},
numpages = {10}
}

@article{10.1109/TASLP.2016.2637298,
author = {Cheer, Jordan and Daley, Stephen and Cheer, Jordan and Daley, Stephen},
title = {An Investigation of Delayless Subband Adaptive Filtering for Multi-Input Multi-Output Active Noise Control Applications},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2637298},
doi = {10.1109/TASLP.2016.2637298},
abstract = {The broadband control of noise and vibration using multi-input, multi-output MIMO active control systems has a potentially wide variety of applications. However, the performance of MIMO systems is often limited in practice by high computational demand and slow convergence speeds. In the somewhat simpler context of single-input, single-output broadband control, these problems have been overcome through a variety of methods including subband adaptive filtering. This paper presents an extension of the subband adaptive filtering technique to the MIMO active control problem and presents a comprehensive study of both the computational requirements and control performance. The implementation of the MIMO filtered-x LMS algorithm using subband adaptive filtering is described and the details of two specific implementations are presented. The computational demands of the two MIMO subband active control algorithms are then compared to that of the standard full-band algorithm. This comparison shows that as the number of subbands employed in the subband algorithms is increased, the computational demand is significantly reduced compared to the full-band implementation provided that a restructured analysis filter-bank is employed. An analysis of the convergence of the MIMO subband adaptive algorithm is then presented and this demonstrates that although the convergence of the control filter coefficients is dependent on the eigenvalue spread of the subband Hessian matrix, which reduces as the number of subbands is increased, the convergence of the cost function is limited for large numbers of subbands due to the simultaneous increase in the weight stacking distortion. The performance of the two MIMO subband algorithms and the standard full-band algorithm has then been assessed through a series of time-domain simulations of a practical active control system and it has been shown that the subband algorithms are able to achieve a significant increase in the convergence speed compared to the full-band implementation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {359–373},
numpages = {15}
}

@article{10.1109/TASLP.2016.2632528,
author = {Badawy, Dalia El and Duong, Ngoc Q. K. and Ozerov, Alexey and El Badawy, Dalia and Duong, Ngoc Q. K. and Ozerov, Alexey},
title = {On-the-Fly Audio Source Separation—A Novel User-Friendly Framework},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2632528},
doi = {10.1109/TASLP.2016.2632528},
abstract = {This paper addresses the challenging problem of single-channel audio source separation. We introduce a novel user-guided framework where source models that govern the separation process are learned on-the-fly from audio examples retrieved online. The user only provides the search keywords that describe the sources in the mixture. In this framework, the generic spectral characteristics of each source are modeled by a universal sound class model learned from the retrieved examples via nonnegative matrix factorization. We propose several group sparsity-inducing constraints in order to efficiently exploit a relevant subset of the universal model adapted to the mixture to be separated. We then derive the corresponding multiplicative update rules for parameter estimation. Separation results obtained from automated and user tests on mixtures containing various types of sounds confirm the effectiveness of the proposed framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {261–272},
numpages = {12}
}

@article{10.1109/TASLP.2016.2634123,
author = {Granell, Emilio and Martinez-Hinarejos, Carlos-D. and Granell, Emilio and Martinez-Hinarejos, Carlos-D},
title = {Multimodal Crowdsourcing for Transcribing Handwritten Documents},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2634123},
doi = {10.1109/TASLP.2016.2634123},
abstract = {Transcription of handwritten documents is an important research topic for multiple applications, such as document classification or information extraction. In the case of historical documents, their transcription allows to preserve cultural heritage because of the amount of historical data contained in those documents. The transcription process can employ state-of-the-art handwritten text recognition systems in order to obtain an initial transcription. This transcription is usually not good enough for the quality standards, but that may speed up the final transcription of the expert. In this framework, the use of collaborative transcription applications crowdsourcing has risen in the recent years, but these platforms are mainly limited by the use of non-mobile devices. Thus, the recruiting initiatives get reduced to a smaller set of potential volunteers. In this paper, an alternative that allows the use of mobile devices is presented. The proposal consists of using speech dictation of handwritten text lines. Then, by using multimodal combination of speech and handwritten text images, a draft transcription can be obtained, presenting more quality than that obtained by only using handwritten text recognition. The speech dictation platform is implemented as a mobile device application, which allows for a wider range of population for recruiting volunteers. A real acquisition on the contents of a Spanish historical handwritten book was obtained with the platform. This data was used to perform experiments on the behaviour of the proposed framework. Some experiments were performed to study how to optimise the collaborators effort in terms of number of collaborations, including how many lines and which lines should be selected for the speech dictation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {409–419},
numpages = {11}
}

@article{10.1109/TASLP.2016.2633803,
author = {Rehr, Robert and Gerkmann, Timo and Rehr, Robert and Gerkmann, Timo},
title = {An Analysis of Adaptive Recursive Smoothing with Applications to Noise PSD Estimation},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2633803},
doi = {10.1109/TASLP.2016.2633803},
abstract = {First-order recursive smoothing filters using a fixed smoothing constant are in general unbiased estimators of the mean of a random process. Due to their efficiency in terms of memory consumption and computational complexity, they are of high practical relevance and are also often used to track the first-order moment of nonstationary random processes. However, in single-channel speech-enhancement applications, e.g., for the estimation of the noise power spectral density, an adaptively changing smoothing factor is often employed. Here, the adaptivity is used to avoid speech leakage by raising the smoothing factor when speech is likely to be present. In this paper, we investigate the properties of adaptive first-order recursive smoothing factors applied to noise power spectral density estimators. We show that in contrast to a smoothing with fixed smoothing factors, adaptive smoothing is in general biased. We propose different methods to quantify and to compensate for the bias. We demonstrate that the proposed correction methods reduce the estimation error and increases the perceptual evaluation of speech quality scores in a speech enhancement framework.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {397–408},
numpages = {12}
}

@article{10.1109/TASLP.2016.2630305,
author = {Bell, Peter and Swietojanski, Pawel and Renals, Steve and Bell, Peter and Swietojanski, Pawel and Renals, Steve},
title = {Multitask Learning of Context-Dependent Targets in Deep Neural Network Acoustic Models},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2630305},
doi = {10.1109/TASLP.2016.2630305},
abstract = {This paper investigates the use of multitask learning to improve context-dependent deep neural network DNN acoustic models. The use of hybrid DNN systems with clustered triphone targets is now standard in automatic speech recognition. However, we suggest that using a single set of DNN targets in this manner may not be the most effective choice, since the targets are the result of a somewhat arbitrary clustering process that may not be optimal for discrimination. We propose to remedy this problem through the addition of secondary tasks predicting alternative content-dependent or context-independent targets. We present a comprehensive set of experiments on a lecture recognition task showing that DNNs trained through multitask learning in this manner give consistently improved performance compared to standard hybrid DNNs. The technique is evaluated across a range of data and output sizes. Improvements are seen when training uses the cross entropy criterion and also when sequence training is applied.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {238–247},
numpages = {10}
}

@article{10.1109/TASLP.2016.2632521,
author = {Zhao, Rui and Mao, Kezhi and Rui Zhao and Kezhi Mao},
title = {Topic-Aware Deep Compositional Models for Sentence Classification},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2632521},
doi = {10.1109/TASLP.2016.2632521},
abstract = {In recent years, deep compositional models have emerged as a popular technique for representation learning of sentence in computational linguistic and natural language processing. These models normally train various forms of neural networks on top of pretrained word embeddings using a task-specific corpus. However, most of these works neglect the multisense nature of words in the pretrained word embeddings. In this paper we introduce topic models to enrich the word embeddings for multisenses of words. The integration of the topic model with various semantic compositional processes leads to topic-aware convolutional neural network and topic-aware long short term memory networks. Different from previous multisense word embeddings models that assign multiple independent and sense-specific embeddings to each word, our proposed models are lightweight and have flexible frameworks that regard word sense as the composition of two parts: a general sense derived from a large corpus and a topic-specific sense derived from a task-specific corpus. In addition, our proposed models focus on semantic composition instead of word understanding. With the help of topic models, we can integrate the topic-specific sense at word-level before the composition and sentence-level after the composition. Comprehensive experiments on five public sentence classification datasets are conducted and the results show that our proposed topic-aware deep compositional models produce competitive or better performance than other text representation learning methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {248–260},
numpages = {13}
}

@article{10.1109/TASLP.2016.2633802,
author = {Remaggi, Luca and Jackson, Philip J. B. and Coleman, Philip and Wang, Wenwu and Remaggi, Luca and Jackson, Philip J. B. and Coleman, Philip and Wenwu Wang},
title = {Acoustic Reflector Localization: Novel Image Source Reversion and Direct Localization Methods},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2633802},
doi = {10.1109/TASLP.2016.2633802},
abstract = {Acoustic reflector localization is an important issue in audio signal processing, with direct applications in spatial audio, scene reconstruction, and source separation. Several methods have recently been proposed to estimate the 3-D positions of acoustic reflectors given room impulse responses RIRs. In this paper, we categorize these methods as “image-source reversion,” which localizes the image source before finding the reflector position, and “direct localization,” which localizes the reflector without intermediate steps. We present five new contributions. First, an onset detector, called the clustered dynamic programing projected phase-slope algorithm, is proposed to automatically extract the time of arrival for early reflections within the RIRs of a compact microphone array. Second, we propose an image-source reversion method that uses the RIRs from a single loudspeaker. It is constructed by combining an image source locator the image source direction and range ISDAR algorithm, and a reflector locator using the loudspeaker-image bisection LIB algorithm. Third, two variants of it, exploiting multiple loudspeakers, are proposed. Fourth, we present a direct localization method, the ellipsoid tangent sample consensus ETSAC, exploiting ellipsoid properties to localize the reflector. Finally, systematic experiments on simulated and measured RIRs are presented, comparing the proposed methods with the state-of-the-art. ETSAC generates errors lower than the alternative methods compared through our datasets. Nevertheless, the ISDAR-LIB combination performs well and has a run time 200 times faster than ETSAC.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {296–309},
numpages = {14}
}

@article{10.1109/TASLP.2016.2635029,
author = {Ahmed, Shakeel and Akhtar, Muhammad Tahir and Ahmed, Shakeel and Akhtar, Muhammad Tahir},
title = {Gain Scheduling of Auxiliary Noise and Variable Step-Size for Online Acoustic Feedback Cancellation in Narrow-Band Active Noise Control Systems},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2635029},
doi = {10.1109/TASLP.2016.2635029},
abstract = {In this paper, we investigate active noise control systems, which use auxiliary white Gaussian noise for online feedback path modeling and neutralization. Such systems comprise three adaptive filters: 1 active noise control filter, 2 linear prediction filter, and 3 feedback path modeling and neutralization filter. The injection of white Gaussian noise with large/small variance improves/degrades the convergence of feedback path modeling and neutralization filter but on the other hand degrades/improves the noise-reduction performance of active noise control systems. The focus of this paper is to have an adequate compromise between the convergence of feedback path modeling and neutralization filter and the noise-reduction-performance of active noise control systems. In this paper: 1 A tuning free gain scheduling of white Gaussian noise is used to improve the noise-reduction performance of active noise control systems, and 2 a variable step-size is used to compensate for the decrease in the convergence of feedback path modeling and neutralization filter due to gain scheduling. The proposed idea is explained for a single channel as well as for multichannel active noise control systems. Computer simulations have been carried out to demonstrate the performance of the proposed method for a single channel as well as for multichannel active noise control systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {333–343},
numpages = {11}
}

@article{10.1109/TASLP.2016.2635027,
author = {Schlecht, Sebastian J. and Habets, Emanuel A. P. and Schlecht, Sebastian J. and Habets, Emanuel A. P.},
title = {Feedback Delay Networks: Echo Density and Mixing Time},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2635027},
doi = {10.1109/TASLP.2016.2635027},
abstract = {Feedback delay networks FDNs are frequently used to generate artificial reverberation. This paper discusses the temporal features of impulse responses produced by FDNs, i.e., the number of echoes per time unit and its evolution over time. This so-called echo density is related to known measures of mixing time and their psychoacoustic correlates such as auditive perception of the room size. It is shown that the echo density of FDNs follows a polynomial function, whereby the polynomial coefficients can be derived from the lengths of the delays for which an explicit method is given. The mixing time of impulse responses can be predicted from the echo density, and conversely, a desired mixing time can be achieved by a derived mean delay length. A Monte Carlo simulation confirms the accuracy of the derived relation of mixing time and delay lengths.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {374–383},
numpages = {10}
}

@article{10.1109/TASLP.2016.2631338,
author = {Belloch, Jose A. and Gonzalez, Alberto and Quintana-Orti, Enrique S. and Ferrer, Miguel and Valimaki, Vesa and Belloch, Jose A. and Gonzalez, Alberto and Quintana-Orti, Enrique S. and Ferrer, Miguel and Valimaki, Vesa},
title = {GPU-Based Dynamic Wave Field Synthesis Using Fractional Delay Filters and Room Compensation},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2631338},
doi = {10.1109/TASLP.2016.2631338},
abstract = {Wave field synthesis WFS is a multichannel audio reproduction method, of a considerable computational cost that renders an accurate spatial sound field using a large number of loudspeakers to emulate virtual sound sources. The moving of sound source locations can be improved by using fractional delay filters, and room reflections can be compensated by using an inverse filter bank that corrects the room effects at selected points within the listening area. However, both the fractional delay filters and the room compensation filters further increase the computational requirements of the WFS system. This paper analyzes the performance of a WFS system composed of 96 loudspeakers which integrates both strategies. In order to deal with the large computational complexity, we explore the use of a graphics processing unit GPU as a massive signal co-processor to increase the capabilities of the WFS system. The performance of the method as well as the benefits of the GPU acceleration are demonstrated by considering different sizes of room compensation filters and fractional delay filters of order 9. The results show that a 96-speaker WFS system that is efficiently implemented on a state-of-art GPU can synthesize the movements of 94 sound sources in real time and, at the same time, can manage 9216 room compensation filters having more than 4000 coefficients each.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {435–447},
numpages = {13}
}

@article{10.1109/TASLP.2016.2635022,
author = {Abel, Johannes and Kaniewska, Magdalena and Guillaume, Cyril and Tirry, Wouter and Fingscheidt, Tim and Abel, Johannes and Kaniewska, Magdalena and Guillaume, Cyril and Tirry, Wouter and Fingscheidt, Tim},
title = {An Instrumental Quality Measure for Artificially Bandwidth-Extended Speech Signals},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2635022},
doi = {10.1109/TASLP.2016.2635022},
abstract = {Various studies have shown that the instrumental measures wideband PESQ and POLQA are not reliably predicting speech quality for artificial speech bandwidth extension ABE test conditions, as this has never been their scope. Based on data from a coordinated subjective listening test with 12 ABE variants developed by 6 different institutions, conducted in 4 languages, we propose in this work a novel instrumental quality measure that is specifically suited for narrowband-to-wideband ABE test conditions. In particular, our contributions are fourfold: First, we propose quality indicators particularly being able to detect ABE-related distortions. Second, we investigate the combination of perceptually and nonperceptually motivated distortion-related statistics. Third, we propose a support-vector-machine-based high-performance MOS predictor for ABE speech quality assessment, finally, we present the training process based on the subjective listening test data. A k-fold cross-validation test on 1 disjoint languages, 2 disjoint speakers, and 3 disjoint ABE solutions proves the superiority of our proposed measure in the ITU-T-recommended categories accuracy, consistency, and linearity compared to both, wideband PESQ and POLQA.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {384–396},
numpages = {13}
}

@article{10.1109/TASLP.2016.2634118,
author = {Elvander, Filip and Sward, Johan and Jakobsson, Andreas and Elvander, Filip and Sward, Johan and Jakobsson, Andreas},
title = {Online Estimation of Multiple Harmonic Signals},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2634118},
doi = {10.1109/TASLP.2016.2634118},
abstract = {In this paper, we propose a time-recursive multipitch estimation algorithm using a sparse reconstruction framework, assuming that only a few pitches from a large set of candidates are active at each time instant. The proposed algorithm does not require any training data, and instead utilizes a sparse recursive least-squares formulation augmented by an adaptive penalty term specifically designed to enforce a pitch structure on the solution. The amplitudes of the active pitches are also recursively updated, allowing for a smooth and more accurate representation. When evaluated on a set of ten music pieces, the proposed method is shown to outperform other general purpose multipitch estimators in either accuracy or computational speed, although not being able to yield performance as good as the state-of-the art methods, which are being optimally tuned and specifically trained on the present instruments. However, the method is able to outperform such a technique when used without optimal tuning, or when applied to instruments not included in the training data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {273–284},
numpages = {12}
}

@article{10.1109/TASLP.2016.2633806,
author = {Markovich-Golan, Shmulik and Gannot, Sharon and Kellermann, Walter and Markovich-Golan, Shmulik and Gannot, Sharon and Kellermann, Walter},
title = {Combined LCMV-TRINICON Beamforming for Separating Multiple Speech Sources in Noisy and Reverberant Environments},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2633806},
doi = {10.1109/TASLP.2016.2633806},
abstract = {The problem of source separation using an array of microphones in reverberant and noisy conditions is addressed. We consider applying the well-known linearly constrained minimum variance LCMV beamformer BF for extracting individual speakers. Constraints are defined using relative transfer functions RTFs for the sources, which are ratios of acoustic transfer functions ATFs between any microphone and a reference microphone. The latter are usually estimated by methods that rely on single-talk time segments where only a single source is active and on reliable knowledge of the source activity. Two novel algorithms for estimation of RTFs using the “Triple N” ICA for convolutive mixtures TRINICON framework are proposed, not resorting to the usually unavailable source activity pattern. The first algorithm estimates the RTFs of the sources by applying multiple two-channel geometrically constrained GC TRINICON units, where approximate direction of arrival information for the sources is utilized for ensuring convergence to the desired solution. The GC-TRINICON is applied to all microphone pairs using a common reference microphone. In the second algorithm, we propose to estimate RTFs iteratively using GC-TRINICON, where instead of using a fixed reference microphone as before, we suggest to use the output signals of LCMV-BFs from the previous iteration as spatially processed references with improved signal-to-interference-and-noise ratio. For both algorithms, a simple detection of noise-only time segments is required for estimating the covariance matrix of noise and interference. We conduct an experimental study in which the performance of the proposed methods is confirmed and compared to corresponding supervised methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {320–332},
numpages = {13}
}

@article{10.1109/TASLP.2016.2601222,
author = {Chen, Hanchi and Abhayapala, Thushara Dheemantha and Samarasinghe, Prasanga N. and Zhang, Wen and Hanchi Chen and Abhayapala, Thushara Dheemantha and Samarasinghe, Prasanga N. and Wen Zhang},
title = {Direct-to-Reverberant Energy Ratio Estimation Using a First-Order Microphone},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2601222},
doi = {10.1109/TASLP.2016.2601222},
abstract = {The direct-to-reverberant ratio DRR is an important characterization of a reverberant environment. This paper presents a novel blind DRR estimation method based on the coherence function between the sound pressure and particle velocity at a point. First, a general expression of coherence function and DRR is derived in the spherical harmonic domain, without imposing assumptions on the reverberation. In this paper, DRR is expressed in terms of the coherence function as well as two parameters that are related to statistical characteristics of the reverberant environment. Then, a method to estimate the values of these two parameters using a microphone system capable of capturing first-order spherical harmonics is proposed, under three assumptions which are more realistic than the diffuse field model. Furthermore, a theoretical analysis on the use of plane wave model for direct path signal and its effect on DRR estimation is presented, and a rule of thumb is provided for determining whether the point source model should be used for the direct path signal. Finally, the ACE challenge dataset is used to validate the proposed DRR estimation method. The results show that the average full band estimation error is within 2 dB, with no clear trend of bias.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {226–237},
numpages = {12}
}

@article{10.1109/TASLP.2016.2635031,
author = {Sargent, Gabriel and Bimbot, Frederic and Vincent, Emmanuel and Sargent, Gabriel and Bimbot, Frederic and Vincent, Emmanuel},
title = {Estimating the Structural Segmentation of Popular Music Pieces Under Regularity Constraints},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2635031},
doi = {10.1109/TASLP.2016.2635031},
abstract = {Music structure estimation has recently emerged as a central topic within the field of music information retrieval. Indeed, as music is a highly structured information stream, knowledge of how a music piece is organized represents a key challenge to enhance the management and exploitation of large music collections. This paper focuses on the benefits that can be expected from a regularity constraint on the structural segmentation of popular music pieces. Specifically, here, we study how a constraint that favors structural segments of comparable size provides a better conditioning of the boundary estimation process. First, we propose a formulation of the structural segmentation task as an optimization process, which separates the contribution from the audio features and the one from the constraint. We illustrate how the corresponding cost function can be minimized using a Viterbi algorithm. We present briefly its implementation and results in three systems designed for and submitted to the MIREX 2010, 2011, and 2012 evaluation campaigns. Then, we explore the benefits of the regularity constraint as an efficient mean for combining the outputs of a selection of systems presented at MIREX between 2010 and 2015, yielding a level of performance competitive to that of the state-of-the-art on the “MIREX10” dataset 100 J-Pop songs from the RWC database.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {344–358},
numpages = {15}
}

@article{10.1109/TASLP.2016.2633812,
author = {Renkens, Vincent and Van hamme, Hugo and Renkens, Vincent and Van hamme, Hugo},
title = {Weakly Supervised Learning of Hidden Markov Models for Spoken Language Acquisition},
year = {2017},
issue_date = {February 2017},
publisher = {IEEE Press},
volume = {25},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2633812},
doi = {10.1109/TASLP.2016.2633812},
abstract = {In this paper, a spoken command and control interface that acquires spoken language through demonstrations from the user is discussed. The user can train the system by uttering a command and subsequently demonstrating the required action through an alternative interface. From the demonstration, a bag of semantic concepts representation that represents which semantic concepts are present in the demonstration is extracted. In the previous work, we have proposed a method for learning words for these concepts by linking the bag of semantic concepts representation to a bag of features representation of the acoustics. In this method, the order in which the words occur is lost. However, in many cases, the order in which the words occur is important to be able to determine the correct action. In this paper, the vocabulary acquisition based on nonnegative matrix factorization is jointly trained with a hidden Markov model HMM, making it possible to use the bag of concepts representation as a weak supervision for HMM learning. This model can better utilize the timing information to improve the results and the order in which the words occur is retained making it possible to learn vocabulary and grammar. The proposed system is tested on several command and control tasks and it is shown that for unimpaired speech the resulting system outperforms the system solely based on vocabulary acquisition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {285–295},
numpages = {11}
}

@article{10.1109/TASLP.2016.2623559,
author = {Wu, Bo and Li, Kehuang and Yang, Minglei and Lee, Chin-Hui and Bo Wu and Kehuang Li and Minglei Yang and Chin-Hui Lee and Wu, Bo and Yang, Minglei and Lee, Chin-Hui and Li, Kehuang},
title = {A Reverberation-Time-Aware Approach to Speech Dereverberation Based on Deep Neural Networks},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2623559},
doi = {10.1109/TASLP.2016.2623559},
abstract = {A reverberation-time-aware deep-neural-network DNN-based speech dereverberation framework is proposed to handle a wide range of reverberation times. There are three key steps in designing a robust system. First, in contrast to sigmoid activation and min-max normalization in state-of-the-art algorithms, a linear activation function at the output layer and global mean-variance normalization of target features are adopted to learn the complicated nonlinear mapping function from reverberant to anechoic speech and to improve the restoration of the low-frequency and intermediate-frequency contents. Next, two key design parameters, namely, frame shift size in speech framing and acoustic context window size at the DNN input, are investigated to show that RT60-dependent parameters are needed in the DNN training stage in order to optimize the system performance in diverse reverberant environments. Finally, the reverberation time is estimated to select the proper frame shift and context window sizes for feature extraction before feeding the log-power spectrum features to the trained DNNs for speech dereverberation. Our experimental results indicate that the proposed framework outperforms the conventional DNNs without taking the reverberation time into account, while achieving a performance only slightly worse than the oracle cases with known reverberation times even for extremely weak and severe reverberant conditions. It also generalizes well to unseen room sizes, loudspeaker and microphone positions, and recorded room impulse responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {102–111},
numpages = {10}
}

@article{10.1109/TASLP.2016.2623563,
author = {Xing, Hua and Hansen, John H. L. and Hua Xing and Hansen, John H. L. and Xing, Hua and Hansen, John H. L.},
title = {Single Sideband Frequency Offset Estimation and Correction for Quality Enhancement and Speaker Recognition},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2623563},
doi = {10.1109/TASLP.2016.2623563},
abstract = {Communication system mismatch represents a major influence in the losses of both speech quality and speaker recognition system performance. Although microphone and handset differences have been considered for speaker recognition e.g., NIST SRE, nonlinear communication system differences, such as modulation/demodulation Mod/DeMod carrier mismatch, have yet to be explored. While such mismatch was common in traditional analog communications, today, with the diversity and blending of communication technologies, it is reconsidered as a major distortion. This paper is focused on estimating and correcting the frequency-shift distortion resulting from Mod/DeMod carrier frequency mismatch in high-frequency single sideband HF-SSB speech. To overcome the drawbacks of existing solutions, a two-step algorithm is proposed to improve estimation performance. In the first step, the offset of speech is scaled to a small frequency interval, which eliminates or reduces the nonuniqueness issue due to the periodicity within the spectrum; the second step performs fine tuning within the estimated predetermined uniqueness interval UI. For the first time, a statistical framework is developed for UI detection, where an innovative acoustic feature is proposed to represent alternative frequency shifts. Additionally, in the estimation process, statistical techniques such as GMM-SVM, i-Vector, and deep neural networks are applied in the first step to improve the estimation accuracy. An evaluation using DARPA RATS HF-SSB data shows that the proposed algorithm achieves a significant improvement in the estimation performance up to +35.6% improvement in accuracy, speech quality measurement up to +27.3% relative improvement in the PESQ score, and speaker verification up to +59.9% relative improvement in equal error rate.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {124–136},
numpages = {13}
}

@article{10.1109/TASLP.2016.2620600,
author = {Al-Tmeme, Ahmed and Woo, Wai Lok and Dlay, Satnam Singh and Gao, Bin and Al-Tmeme, Ahmed and Wai Lok Woo and Dlay, Satnam Singh and Bin Gao and Woo, Wai Lok and Dlay, Satnam Singh and Al-Tmeme, Ahmed and Gao, Bin},
title = {Underdetermined Convolutive Source Separation Using GEM-MU With Variational Approximated Optimum Model Order NMF2D},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2620600},
doi = {10.1109/TASLP.2016.2620600},
abstract = {An unsupervised machine learning algorithm based on nonnegative matrix factor Two-dimensional deconvolution NMF2D with approximated optimum model order is proposed. The proposed algorithm adapted under the hybrid framework that combines the generalized EM algorithm with multiplicative update. As the number of parameters in the NMF2D grows exponentially the number of frequency basis increases linearly, the issues of model-order fitness, initialization, and parameters estimation become ever more critical. This paper proposes a variational Bayesian method to optimize the number of components in the NMF2D by using the Gamma-Exponential process as the observation-latent model. In addition, it is shown that the proposed Gamma-Exponential process can be used to initialize the NMF2D parameters. Finally, the paper investigates the issue and advantages of using different window length. Experimental results for the synthetic convolutive mixtures and live recordings verify the competence of the proposed algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {35–49},
numpages = {15}
}

@article{10.1109/TASLP.2016.2626965,
author = {Chen, Hongjie and Xie, Lei and Leung, Cheung-Chi and Lu, Xiaoming and Ma, Bin and Li, Haizhou and Hongjie Chen and Lei Xie and Cheung-Chi Leung and Xiaoming Lu and Bin Ma and Haizhou Li and Leung, Cheung-Chi and Li, Haizhou and Lu, Xiaoming and Ma, Bin and Chen, Hongjie and Xie, Lei},
title = {Modeling Latent Topics and Temporal Distance for Story Segmentation of Broadcast News},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2626965},
doi = {10.1109/TASLP.2016.2626965},
abstract = {This paper studies a strategy to model latent topics and temporal distance of text blocks for story segmentation, that we call graph regularization in topic modeling or GRTM. We propose two novel approaches that consider both temporal distance and lexical similarity of text blocks, collectively referred to as data proximity, in learning latent topic representation, where a graph regularizer is involved to derive the latent topic representation while preserving data proximity. In the first approach, we extend the idea of Laplacian probabilistic latent semantic analysis LapPLSA by introducing a distance penalty function in the affinity matrix of a graph for latent topic estimation. The estimated latent topic distributions are used to replace the traditional term-frequency vectors as the data representation of the text blocks and to measure the cohesive strength between them. In the second approach, we perform Laplacian eigenmaps, which makes use of the graph regularizer for dimensionality reduction, on latent topic distributions estimated by conventional topic modeling. We conduct the experiments on the automatic speech recognition transcripts of the TDT2 English broadcast news corpus. The experiments show the proposed strategy outperforms the conventional techniques. LapPLSA performs the best with the highest F1-measure of 0.816. The effects of the penalty constant in the distance penalty function, the number of latent topics, and the size of training data on the segmentation performances are also studied.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {112–123},
numpages = {12}
}

@article{10.1109/TASLP.2016.2621669,
author = {Huang, Zhen and Siniscalchi, Sabato Marco and Lee, Chin-Hui and Zhen Huang and Siniscalchi, Sabato Marco and Chin-Hui Lee and Lee, Chin-Hui and Huang, Zhen and Siniscalchi, Sabato Marco},
title = {Bayesian Unsupervised Batch and Online Speaker Adaptation of Activation Function Parameters in Deep Models for Automatic Speech Recognition},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2621669},
doi = {10.1109/TASLP.2016.2621669},
abstract = {We present a Bayesian framework to obtain maximum a posteriori MAP estimation of a small set of hidden activation function parameters in context-dependent-deep neural network-hidden markov model CD-DNN-HMM-based automatic speech recognition ASR systems. When applied to speaker adaptation, we aim at transfer learning from a well-trained deep model for a “general” usage to a “personalized” model geared toward a particular talker by using a collection of speaker-specific data. To make the framework applicable to practical situations, we perform adaptation in an unsupervised manner assuming that the transcriptions of the adaptation utterances are not readily available to the ASR system. We conduct a series of comprehensive batch adaptation experiments on the Switchboard ASR task and show that the proposed approach is effective even with CD-DNN-HMM built with discriminative sequential training. Indeed, MAP speaker adaptation reduces the word error rate WER to 20.1% from an initial 21.9% on the full NIST 2000 Hub5 benchmark test set. Moreover, MAP speaker adaptation compares favorably with other techniques evaluated on the same speech tasks. We also demonstrate its complementarity to other approaches by applying MAP adaptation to CD-DNN-HMM trained with speaker adaptive features generated through constrained maximum likelihood linear regression and further reduces the WER to 18.6%. Leveraging upon the intrinsic recursive nature in Bayesian adaptation and mitigating possible system constraints on batch learning, we also proposed an incremental approach to unsupervised online speaker adaptation by simultaneously updating the hyperparameters of the approximate posterior densities and the DNN parameters sequentially. The advantage of such a sequential learning algorithm over a batch method is not necessarily in the final performance, but in computational efficiency and reduced storage needs, without having to wait for all the data to be processed. So far, the experimental results are promising.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {64–75},
numpages = {12}
}

@article{10.1109/TASLP.2016.2632307,
author = {Han, Yoonchang and Kim, Jaehun and Lee, Kyogu and Yoonchang Han and Jaehun Kim and Kyogu Lee},
title = {Deep Convolutional Neural Networks for Predominant Instrument Recognition in Polyphonic Music},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2632307},
doi = {10.1109/TASLP.2016.2632307},
abstract = {Identifying musical instruments in polyphonic music recordings is a challenging but important problem in the field of music information retrieval. It enables music search by instrument, helps recognize musical genres, or can make music transcription easier and more accurate. In this paper, we present a convolutional neural network framework for predominant instrument recognition in real-world polyphonic music. We train our network from fixed-length music excerpts with a single-labeled predominant instrument and estimate an arbitrary number of predominant instruments from an audio signal with a variable length. To obtain the audio-excerpt-wise result, we aggregate multiple outputs from sliding windows over the test audio. In doing so, we investigated two different aggregation methods: one takes the class-wise average followed by normalization, and the other perform temporally local class-wise max-pooling on the output probability prior to averaging and normalization steps to minimize the effect of averaging process suppresses the activation of sporadically appearing instruments. In addition, we conducted extensive experiments on several important factors that affect the performance, including analysis window size, identification threshold, and activation functions for neural networks to find the optimal set of parameters. Our analysis on the instrument-wise performance found that the onset type is a critical factor for recall and precision of each instrument. Using a dataset of 10k audio excerpts from 11 instruments for evaluation, we found that convolutional neural networks are more robust than conventional methods that exploit spectral features and source separation with support vector machines. Experimental results showed that the proposed convolutional network architecture obtained an F1 measure of 0.619 for micro and 0.513 for macro, respectively, achieving 23.1% and 18.8% in performance improvement compared with the state-of-the-art algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {208–221},
numpages = {14}
}

@article{10.1109/TASLP.2016.2628641,
author = {Kolbk, Morten and Tan, Zheng-Hua and Jensen, Jesper and Kolbk, Morten and Zheng-Hua Tan and Jensen, Jesper},
title = {Speech Intelligibility Potential of General and Specialized Deep Neural Network Based Speech Enhancement Systems},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2628641},
doi = {10.1109/TASLP.2016.2628641},
abstract = {In this paper, we study aspects of single microphone speech enhancement SE based on deep neural networks DNNs. Specifically, we explore the generalizability capabilities of state-of-the-art DNN-based SE systems with respect to the background noise type, the gender of the target speaker, and the signal-to-noise ratio SNR. Furthermore, we investigate how specialized DNN-based SE systems, which have been trained to be either noise type specific, speaker specific or SNR specific, perform relative to DNN based SE systems that have been trained to be noise type general, speaker general, and SNR general. Finally, we compare how a DNN-based SE system trained to be noise type general, speaker general, and SNR general performs relative to a state-of-the-art short-time spectral amplitude minimum mean square error STSA-MMSE based SE algorithm. We show that DNN-based SE systems, when trained specifically to handle certain speakers, noise types and SNRs, are capable of achieving large improvements in estimated speech quality SQ and speech intelligibility SI, when tested in matched conditions. Furthermore, we show that improvements in estimated SQ and SI can be achieved by a DNN-based SE system when exposed to unseen speakers, genders and noise types, given a large number of speakers and noise types have been used in the training of the system. In addition, we show that a DNN-based SE system that has been trained using a large number of speakers and a wide range of noise types outperforms a state-of-the-art STSA-MMSE based SE method, when tested using a range of unseen speakers and noise types. Finally, a listening test using several DNN-based SE systems tested in unseen speaker conditions show that these systems can improve SI for some SNR and noise type configurations but degrade SI for others.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {153–167},
numpages = {15}
}

@article{10.1109/TASLP.2016.2623565,
author = {Durand, Simon and Bello, Juan Pablo and David, Bertrand and Richard, Gael and Durand, Simon and Bello, Juan Pablo and David, Bertrand and Richard, Gael and David, Bertrand and Richard, Gael and Durand, Simon and Bello, Juan Pablo},
title = {Robust Downbeat Tracking Using an Ensemble of Convolutional Networks},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2623565},
doi = {10.1109/TASLP.2016.2623565},
abstract = {In this paper, we present a novel state-of-the-art system for automatic downbeat tracking from music signals. The audio signal is first segmented in frames which are synchronized at the tatum level of the music. We then extract different kind of features based on harmony, melody, rhythm, and bass content to feed convolutional neural networks that are adapted to take advantage of the characteristics of each feature. This ensemble of neural networks is combined to obtain one downbeat likelihood per tatum. The downbeat sequence is finally decoded with a flexible and efficient temporal model which takes advantage of the assumed metrical continuity of a song. We then perform an evaluation of our system on a large base of nine datasets, compare its performance to four other published algorithms and obtain a significant increase of 16.8% points compared to the second-best system, for altogether a moderate cost in test and training. The influence of each step of the method is studied to show its strengths and shortcomings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {76–89},
numpages = {14}
}

@article{10.1109/TASLP.2016.2621659,
author = {Hasegawa-Johnson, Mark A. and Jyothi, Preethi and McCloy, Daniel and Mirbagheri, Majid and Liberto, Giovanni M. di and Das, Amit and Ekin, Bradley and Liu, Chunxi and Manohar, Vimal and Tang, Hao and Lalor, Edmund C. and Chen, Nancy F. and Hager, Paul and Kekona, Tyler and Sloan, Rose and Lee, Adrian K. C. and Hasegawa-Johnson, Mark A. and Jyothi, Preethi and McCloy, Daniel and Mirbagheri, Majid and Di Liberto, Giovanni M. and Das, Amit and Ekin, Bradley and Chunxi Liu and Manohar, Vimal and Hao Tang and Lalor, Edmund C. and Chen, Nancy F. and Hager, Paul and Kekona, Tyler and Sloan, Rose and Lee, Adrian K. C. and Chen, Nancy F. and Manohar, Vimal and McCloy, Daniel and Liu, Chunxi and Hager, Paul and Lee, Adrian K. C. and Mirbagheri, Majid and Sloan, Rose and Kekona, Tyler and Lalor, Edmund C. and Tang, Hao and Ekin, Bradley and Liberto, Giovanni M. di and Das, Amit and Hasegawa-Johnson, Mark A. and Jyothi, Preethi},
title = {ASR for Under-Resourced Languages From Probabilistic Transcription},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2621659},
doi = {10.1109/TASLP.2016.2621659},
abstract = {In many under-resourced languages it is possible to find text, and it is possible to find speech, but transcribed speech suitable for training automatic speech recognition ASR is unavailable. In the absence of native transcripts, this paper proposes the use of a probabilistic transcript: A probability mass function over possible phonetic transcripts of the waveform. Three sources of probabilistic transcripts are demonstrated. First, self-training is a well-established semisupervised learning technique, in which a cross-lingual ASR first labels unlabeled speech, and is then adapted using the same labels. Second, mismatched crowdsourcing is a recent technique in which nonspeakers of the language are asked to write what they hear, and their nonsense transcripts are decoded using noisy channel models of second-language speech perception. Third, EEG distribution coding is a new technique in which nonspeakers of the language listen to it, and their electrocortical response signals are interpreted to indicate probabilities. ASR was trained in four languages without native transcripts. Adaptation using mismatched crowdsourcing significantly outperformed self-training, and both significantly outperformed a cross-lingual baseline. Both EEG distribution coding and text-derived phone language models were shown to improve the quality of probabilistic transcripts derived from mismatched crowdsourcing.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {50–63},
numpages = {14}
}

@article{10.1109/TASLP.2016.2625459,
author = {Chen, Zhehuai and Zhuang, Yimeng and Qian, Yanmin and Yu, Kai and Zhehuai Chen and Yimeng Zhuang and Yanmin Qian and Kai Yu and Yu, Kai and Zhuang, Yimeng and Chen, Zhehuai and Qian, Yanmin},
title = {Phone Synchronous Speech Recognition With CTC Lattices},
year = {2017},
issue_date = {January 2017},
publisher = {IEEE Press},
volume = {25},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2625459},
doi = {10.1109/TASLP.2016.2625459},
abstract = {Connectionist temporal classification CTC has recently shown improved performance and efficiency in automatic speech recognition. One popular decoding implementation is to use a CTC model to predict the phone posteriors at each frame and then perform Viterbi beam search on a modified WFST network. This is still within the traditional frame synchronous decoding framework. In this paper, the peaky posterior property of CTC is carefully investigated and it is found that ignoring blank frames will not introduce additional search errors. Based on this phenomenon, a novel phone synchronous decoding framework is proposed by removing tremendous search redundancy due to blank frames, which results in significant search speed up. The framework naturally leads to an extremely compact phone-level acoustic space representation: CTC lattice. With CTC lattice, efficient and effective modular speech recognition approaches, second pass rescoring for large vocabulary continuous speech recognition LVCSR, and phone-based keyword spotting KWS, are also proposed in this paper. Experiments showed that phone synchronous decoding can achieve 3-4 times search speed up without performance degradation compared to frame synchronous decoding. Modular LVCSR with CTC lattice can achieve further WER improvement. KWS with CTC lattice not only achieved significant equal error rate improvement, but also greatly reduced the KWS model size and increased the search speed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {90–

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extension: bib