text:
demonstrate the effectiveness and applicability of the proposed framework in relation to several well-practiced and state-of-the-art summarization methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {161–170},
numpages = {10}
}

@article{10.1109/TASLP.2017.2727684,
author = {Yee, Dianna and Kamkar-Parsi, Homayoun and Martin, Rainer and Puder, Henning and Yee, Dianna and Kamkar-Parsi, Homayoun and Martin, Rainer and Puder, Henning},
title = {A Noise Reduction Postfilter for Binaurally Linked Single-Microphone Hearing Aids Utilizing a Nearby External Microphone},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2727684},
doi = {10.1109/TASLP.2017.2727684},
abstract = {The use of a nearby external microphone for addressing front-back ambiguity in single-microphone hearing aid devices is investigated. Strategic placement of the external microphone is able to provide benefits from the body shielding back-directional noise and therefore information for discriminating between the frontal and back hemispheres. The scattering effects of the body are first analyzed to yield a placement strategy of the external microphone for maximizing the shielding effect of the body against back-directional noise sources while also optimizing for speech intelligibility. Assuming optimal placement, a frontal target source presence probability FTSPP estimator is derived. Using the FTSPP estimator, a more comprehensive noise estimator is proposed, which considers both stationary and nonstationary interferers. The performance of the proposed noise estimator is evaluated in its application for postfiltering the output of a binaural beamformer. The effect of postfiltering using the proposed noise estimator is to provide further reduction of directional noise from the lateral and back direction while preserving the frontal target signal. The resulting enhancement with the proposed noise estimator provides a better signal-to-noise ratio and improved objective speech quality and intelligibility of a frontal target speaker compared to the state of the art.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {5–18},
numpages = {14}
}

@article{10.1109/TASLP.2017.2761699,
author = {Andersen, Kristian Timm and Moonen, Marc and Andersen, Kristian Timm and Moonen, Marc},
title = {Robust Speech-Distortion Weighted Interframe Wiener Filters for Single-Channel Noise Reduction},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2761699},
doi = {10.1109/TASLP.2017.2761699},
abstract = {In this paper, speech-distortion weighted SDW interframe Wiener filters IFWFs are investigated for single-channel noise reduction in a filter bank structure. The filters utilize a parameter μ that explicitly sets a tradeoff between noise reduction and speech distortion and have traditionally been used in multichannel applications under the term SDW multichannel Wiener filter. The application of these SDW-IFWFs relies on the estimation of interframe correlation IFC coefficients, and it is shown that the IFC coefficients can be more robustly estimated using a secondary higher resolution filter bank HRFB. It is then shown how real-valued scalar gains, which are optimal in the primary filter bank, can be applied directly in the HRFB instead of the interframe filtering in the primary filter bank, which leads to a more robust noise reduction performance for any value of μ. Computing these gains is also cheaper since matrix inversions are avoided and the primary filter bank is not needed in the actual implementation. Experimental results are given that support the claims, where the proposed methods are compared to relevant reference methods using measures such as the segmental SNR and the objective PESQ.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {97–107},
numpages = {11}
}

@article{10.1109/TASLP.2017.2760243,
author = {Sahidullah, Md. and Thomsen, Dennis Alexander Lehmann and Hautamaki, Rosa Gonzalez and Kinnunen, Tomi and Tan, Zheng-Hua and Parts, Robert and Pitkanen, Martti and Sahidullah, Md and Thomsen, Dennis Alexander Lehmann and Gonzalez Hautamaki, Rosa and Kinnunen, Tomi and Zheng-Hua Tan and Parts, Robert and Pitkanen, Martti},
title = {Robust Voice Liveness Detection and Speaker Verification Using Throat Microphones},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2760243},
doi = {10.1109/TASLP.2017.2760243},
abstract = {While having a wide range of applications, automatic speaker verification ASV systems are vulnerable to spoofing attacks, in particular, replay attacks that are effective and easy to implement. Most prior work on detecting replay attacks uses audio from a single acoustic microphone only, leading to difficulties in detecting high-end replay attacks close to indistinguishable from live human speech. In this paper, we study the use of a special body-conducted sensor, throat microphone TM, for combined voice liveness detection VLD and ASV in order to improve both robustness and security of ASV against replay attacks. We first investigate the possibility and methods of attacking a TM-based ASV system, followed by a pilot data collection. Second, we study the use of spectral features for VLD using both single-channel and dual-channel ASV systems. We carry out speaker verification experiments using Gaussian mixture model with universal background model GMM-UBM and i-vector based systems on a dataset of 38 speakers collected by us. We have achieved considerable improvement in recognition accuracy, with the use of dual-microphone setup. In experiments with noisy test speech, the false acceptance rate FAR of the dual-microphone GMM-UBM based system for recorded speech reduces from 69.69% to 18.75%. The FAR of replay condition further drops to 0% when this dual-channel ASV system is integrated with the new dual-channel voice liveness detector.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {44–56},
numpages = {13}
}

@article{10.1109/TASLP.2017.2761547,
author = {Saito, Yuki and Takamichi, Shinnosuke and Saruwatari, Hiroshi and Saito, Yuki and Takamichi, Shinnosuke and Saruwatari, Hiroshi},
title = {Statistical Parametric Speech Synthesis Incorporating Generative Adversarial Networks},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2761547},
doi = {10.1109/TASLP.2017.2761547},
abstract = {A method for statistical parametric speech synthesis incorporating generative adversarial networks GANs is proposed. Although powerful deep neural networks techniques can be applied to artificially synthesize speech waveform, the synthetic speech quality is low compared with that of natural speech. One of the issues causing the quality degradation is an oversmoothing effect often observed in the generated speech parameters. A GAN introduced in this paper consists of two neural networks: a discriminator to distinguish natural and generated samples, and a generator to deceive the discriminator. In the proposed framework incorporating the GANs, the discriminator is trained to distinguish natural and generated speech parameters, while the acoustic models are trained to minimize the weighted sum of the conventional minimum generation loss and an adversarial loss for deceiving the discriminator. Since the objective of the GANs is to minimize the divergence i.e., distribution difference between the natural and generated speech parameters, the proposed method effectively alleviates the oversmoothing effect on the generated speech parameters. We evaluated the effectiveness for text-to-speech and voice conversion, and found that the proposed method can generate more natural spectral parameters and F0 than conventional minimum generation error training algorithm regardless of its hyperparameter settings. Furthermore, we investigated the effect of the divergence of various GANs, and found that a Wasserstein GAN minimizing the Earth-Mover's distance works the best in terms of improving the synthetic speech quality.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {84–96},
numpages = {13}
}

@article{10.1109/TASLP.2017.2765834,
author = {Chen, Zhehuai and Droppo, Jasha and Li, Jinyu and Xiong, Wayne and Zhehuai Chen and Droppo, Jasha and Jinyu Li and Xiong, Wayne},
title = {Progressive Joint Modeling in Unsupervised Single-Channel Overlapped Speech Recognition},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2765834},
doi = {10.1109/TASLP.2017.2765834},
abstract = {Unsupervised single-channel overlapped speech recognition is one of the hardest problems in automatic speech recognition ASR. Permutation invariant training PIT is a state of the art model-based approach, which applies a single neural network to solve this single-input, multiple-output modeling problem. We propose to advance the current state of the art by imposing a modular structure on the neural network, applying a progressive pretraining regimen, and improving the objective function with transfer learning and a discriminative training criterion. The modular structure splits the problem into three subtasks: frame-wise interpreting, utterance-level speaker tracing, and speech recognition. The pretraining regimen uses these modules to solve progressively harder tasks. Transfer learning leverages parallel clean speech to improve the training targets for the network. Our discriminative training formulation is a modification of standard formulations that also penalizes competing outputs of the system. Experiments are conducted on the artificial overlapped switchboard and hub5e-swb dataset. The proposed framework achieves over 30% relative improvement of word error rate over both a strong jointly trained system, PIT for ASR, and a separately optimized system, PIT for speech separation with clean speech ASR model. The improvement comes from better model generalization, training efficiency, and the sequence level linguistic knowledge integration.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {184–196},
numpages = {13}
}

@article{10.1109/TASLP.2017.2761546,
author = {Degottex, Gilles and Lanchantin, Pierre and Gales, Mark and Degottex, Gilles and Lanchantin, Pierre and Gales, Mark},
title = {A Log Domain Pulse Model for Parametric Speech Synthesis},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2761546},
doi = {10.1109/TASLP.2017.2761546},
abstract = {Most of the degradation in current Statistical Parametric Speech Synthesis SPSS results from the form of the vocoder. One of the main causes of degradation is the reconstruction of the noise. In this article, a new signal model is proposed that leads to a simple synthesizer, without the need for ad-hoc tuning of model parameters. The model is not based on the traditional additive linear source-filter model, it adopts a combination of speech components that are additive in the log domain. Also, the same representation for voiced and unvoiced segments is used, rather than relying on binary voicing decisions. This avoids voicing error discontinuities that can occur in many current vocoders. A simple binary mask is used to denote the presence of noise in the time-frequency domain, which is less sensitive to classification errors. Four experiments have been carried out to evaluate this new model. The first experiment examines the noise reconstruction issue. Three listening tests have also been carried out that demonstrate the advantages of this model: comparison with the STRAIGHT vocoder; the direct prediction of the binary noise mask by using a mixed output configuration; and partial improvements of creakiness using a mask correction mechanism.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {57–70},
numpages = {14}
}

@article{10.1109/TASLP.2017.2764271,
author = {Tang, Zhiyuan and Wang, Dong and Chen, Yixiang and Li, Lantian and Abel, Andrew and Zhiyuan Tang and Dong Wang and Yixiang Chen and Lantian Li and Abel, Andrew},
title = {Phonetic Temporal Neural Model for Language Identification},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2764271},
doi = {10.1109/TASLP.2017.2764271},
abstract = {Deep neural models, particularly the long short-term memory recurrent neural network LSTM-RNN model, have shown great potential for language identification LID. However, the use of phonetic information has been largely overlooked by most existing neural LID methods, although this information has been used very successfully in conventional phonetic LID systems. We present a phonetic temporal neural model for LID, which is an LSTM-RNN LID system that accepts phonetic features produced by a phone-discriminative DNN as the input, rather than raw acoustic features. This new model is similar to traditional phonetic LID methods, but the phonetic knowledge here is much richer: It is at the frame level and involves compacted information of all phones. Our experiments conducted on the Babel database and the AP16-OLR database demonstrate that the temporal phonetic neural approach is very effective, and significantly outperforms existing acoustic neural models. It also outperforms the conventional i-vector approach on short utterances and in noisy conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {134–144},
numpages = {11}
}

@article{10.1109/TASLP.2017.2766023,
author = {Jin, Ma and Song, Yan and McLoughlin, Ian and Dai, Li-Rong and Ma Jin and Yan Song and McLoughlin, Ian and Li-Rong Dai},
title = {LID-Senones and Their Statistics for Language Identification},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2766023},
doi = {10.1109/TASLP.2017.2766023},
abstract = {Recent research on end-to-end training structures for language identification has raised the possibility that intermediate language-sensitive feature units exist which are analogous to phonetically sensitive senones in automatic speech recognition systems. Termed language identification LID-senones, the statistics derived from these feature units have been shown to be beneficial in discriminating between languages, particularly for short utterances. This paper examines the evidence for the existence of LID-senones before designing and evaluating LID systems based on low- and high-level statistics of LID-senones with both generative and discriminative models. For the standard NIST LRE 2009 task on 23 languages, LID-senone-based systems are shown to outperform state-of-the-art deep neural network/i-vector methods both when LID-senones are used directly for classification and when LID-senone statistics are used for i-vector formation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {171–183},
numpages = {13}
}

@article{10.1109/TASLP.2017.2757601,
author = {Backstrom, Tom and Fischer, Johannes and Backstrom, Tom and Fischer, Johannes},
title = {Fast Randomization for Distributed Low-Bitrate Coding of Speech and Audio},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2757601},
doi = {10.1109/TASLP.2017.2757601},
abstract = {Efficient coding of speech and audio in a distributed system requires that quantization errors across nodes are uncorrelated. Yet, with conventional methods at low bitrates, quantization levels become increasingly sparse, which does not correspond to the distribution of the input signal and, importantly, also reduces coding efficiency in a distributed system. We have recently proposed a distributed speech and audio codec design, which applies quantization in a randomized domain such that quantization errors are randomly rotated in the output domain. Similar to dithering, this ensures that quantization errors across nodes are uncorrelated and coding efficiency is retained. In this paper, we improve this approach by proposing faster randomization methods, with a computational complexity of ON log N. The presented experiments demonstrate that the proposed randomizations yield uncorrelated signals, that perceptual quality is competitive, and that the complexity of the proposed methods is feasible for practical applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {19–30},
numpages = {12}
}

@article{10.1109/TASLP.2017.2765832,
author = {Ranjan, Shivesh and Hansen, John H. L. and Ranjan, Shivesh and Hansen, John H. L.},
title = {Curriculum Learning Based Approaches for Noise Robust Speaker Recognition},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2765832},
doi = {10.1109/TASLP.2017.2765832},
abstract = {Performance of speaker identification SID systems is known to degrade rapidly in the presence of mismatch such as noise and channel degradations. This study introduces a novel class of curriculum learning CL based algorithms for noise robust speaker recognition. We introduce CL-based approaches at two stages within a state-of-the-art speaker verification system: at the i-Vector extractor estimation and at the probabilistic linear discriminant PLDA back-end. Our proposed CL-based approaches operate by categorizing the available training data into progressively more challenging subsets using a suitable difficulty criterion. Next, the corresponding training algorithms are initialized with a subset that is closest to a clean noise-free set, and progressively moving to subsets that are more challenging for training as the algorithms progress. We evaluate the performance of our proposed approaches on the noisy and severely degraded data from the DARPA RATS SID task, and show consistent and significant improvement across multiple test sets over a baseline SID framework with a standard i-Vector extractor and multisession PLDA-based back-end. We also construct a very challenging evaluation set by adding noise to the NIST SRE 2010 C5 extended condition trials, where our proposed CL-based PLDA is shown to offer significant improvements over a traditional PLDA based back-end.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {197–210},
numpages = {14}
}

@article{10.1109/TASLP.2017.2763243,
author = {Liu, Bingquan and Xu, Zhen and Sun, Chengjie and Wang, Baoxun and Wang, Xiaolong and Wong, Derek F. and Zhang, Min and Bingquan Liu and Zhen Xu and Chengjie Sun and Baoxun Wang and Xiaolong Wang and Wong, Derek F. and Min Zhang},
title = {Content-Oriented User Modeling for Personalized Response Ranking in Chatbots},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2763243},
doi = {10.1109/TASLP.2017.2763243},
abstract = {Automatic chatbots also known as chat-agents have attracted much attention from both researching and industrial fields. Generally, the semantic relevance between users' queries and the corresponding responses is considered as the essential element for conversation modeling in both generation and ranking based chat systems. By contrast, it is a nontrivial task to adopt the users' information, such as preference, social role, etc., into conversational models reasonably, while users' profiles play a significant role in the procedure of conversations by providing the implicit contexts. This paper aims to address the personalized response ranking task by incorporating user profiles into the conversation model. In our approach, users' personalized representations are latently learned from the contents posted by them via a two-branch neural network. After that, a deep neural network architecture is further presented to learn the fusion representation of posts, responses, and personal information. In this way, the proposed model could understand conversations from the users' perspective; hence, the more appropriate responses are selected for a specified person. The experimental results on two datasets from social network services demonstrate that our approach is hopeful to represent users' personal information implicitly based on user generated contents, and it is promising to perform as an important component in chatbots to select the personalized responses for each user.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {122–133},
numpages = {12}
}

@article{10.1109/TASLP.2017.2762432,
author = {Chiang, Chen-Yu and Chen-Yu Chiang},
title = {Cross-Dialect Adaptation Framework for Constructing Prosodic Models for Chinese Dialect Text-to-Speech Systems},
year = {2018},
issue_date = {January 2018},
publisher = {IEEE Press},
volume = {26},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2762432},
doi = {10.1109/TASLP.2017.2762432},
abstract = {This paper presents an efficient cross-dialect adaptation framework for constructing prosodic models for Chinese dialect text-to-speech systems. In this framework, dialect prosodic models are adapted from an existing Mandarin speaking rate-dependent hierarchical prosodic model. The rationale of the framework is based on the cross-dialectal similarities between Mandarin and other Chinese dialects in terms of syntactic and prosodic structures. Two main problems are addressed in this study: One problem pertains to the use of cross-dialectal similarities in the design and adaptation of the dialect speaking rate-dependent hierarchical prosodic model. The other problem pertains to the data sparseness caused by the insufficiency of an adaptation corpus covering essential linguistic contexts and prosodic events as well as a wide speaking rate range. This problem is solved by employing the structural maximum a posteriori method that hierarchically organizes the dialect speaking rate-dependent hierarchical prosodic model parameters into decision trees to facilitate parameter estimations. The effectiveness of the proposed approach was evaluated by experiments on two Chinese dialects: Min and Hakka. Objective and subjective evaluations demonstrated that the prosodic features generated by the dialect speaking rate-dependent hierarchical prosodic models were quite natural in various speaking rates ranging from 3.3 to 6.7 syllables per second. These results confirm that the proposed cross-dialect adaptation framework is effective and promising.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {108–121},
numpages = {14}
}

@article{10.1109/TASLP.2017.2768838,
author = {Schultz, T. and Hueber, T. and Krusienski, D. J. and Brumberg, J. S.},
title = {Introduction to the Special Issue on Biosignal-Based Spoken Communication},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2768838},
doi = {10.1109/TASLP.2017.2768838},
abstract = {The papers in this special section focus on biosignal-based spoken communication. Speech production is a complex process resulting from human activities initiated in the brain, eventually leading to muscle activities that produce respiratory, laryngeal, and articulatory gestures which finally create acoustic signals. Traditional speech processing systems capture and interpret the acoustic signal of speech. However, speech is not only limited to acoustics - speech-related activities can be measured at each level of speech processing, including the central and peripheral nervous systems, muscular action potentials, and speech kinematics. Their measurement, obtained through recordings from variety of sensor technologies, results in speech-related "biosignals" that have been studied for decades to better understand the underlying mechanisms of human speech processing. However, there is more: speech-related biosignals have the potential to overcome limitations of traditional acoustic-based systems for spoken communication. Biosignals can be captured before the airborne acoustic signal and are thus less prone to environmental noise. Also, they do not rely on the production of audible speech - both features open up newtracks for "Biosignalbased Spoken Communication". Examples of these tracks include Brain-Computer Interfaces allowing for communication by directly decoding cortical brain activity into speech representations, and Silent-Speech Interfaces, which offer a way to communicate privately without disturbing bystanders and to restore spoken communication for people who lost their voice due to severe speech impairments. Furthermore, biosignals could provide valuable articulatory biofeedback to speakers about their own voice production for increasing articulatory awareness in speech therapy or language learning.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2254–2256},
numpages = {3}
}

@article{10.1109/TASLP.2017.2758999,
author = {Kim, Myungjong and Cao, Beiming and Mau, Ted and Wang, Jun},
title = {Speaker-Independent Silent Speech Recognition From Flesh-Point Articulatory Movements Using an LSTM Neural Network},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2758999},
doi = {10.1109/TASLP.2017.2758999},
abstract = {Silent speech recognition SSR converts nonaudio information such as articulatory movements into text. SSR has the potential to enable persons with laryngectomy to communicate through natural spoken expression. Current SSR systems have largely relied on speaker-dependent recognition models. The high degree of variability in articulatory patterns across different speakers has been a barrier for developing effective speaker-independent SSR approaches. Speaker-independent SSR approaches, however, are critical for reducing the amount of training data required from each speaker. In this paper, we investigate speaker-independent SSR from the movements of flesh points on tongue and lips with articulatory normalization methods that reduce the interspeaker variation. To minimize the across-speaker physiological differences of the articulators, we propose Procrustes matching-based articulatory normalization by removing locational, rotational, and scaling differences. To further normalize the articulatory data, we apply feature-space maximum likelihood linear regression and i-vector. In this paper, we adopt a bidirectional long short-term memory recurrent neural network BLSTM as an articulatory model to effectively model the articulatory movements with long-range articulatory history. A silent speech dataset with flesh-point articulatory movements was collected using an electromagnetic articulograph from 12 healthy and two laryngectomized English speakers. Experimental results showed the effectiveness of our speaker-independent SSR approaches on healthy as well as laryngectomy speakers. In addition, BLSTM outperformed the standard deep neural network. The best performance was obtained by the BLSTM with all the three normalization approaches combined.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2323–2336},
numpages = {14}
}

@article{10.1109/TASLP.2017.2753583,
author = {Tobing, Patrick Lumban and Kobayashi, Kazuhiro and Toda, Tomoki},
title = {Articulatory Controllable Speech Modification Based on Statistical Inversion and Production Mappings},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2753583},
doi = {10.1109/TASLP.2017.2753583},
abstract = {In this paper, we present an innovative way of utilizing the natural relationship between speech sounds and articulatory movements by developing an articulatory controllable speech modification system. Specifically, we employ statistical acoustic-to-articulatory inversion mapping and articulatory-to-acoustic production mapping based on a Gaussian mixture model, allowing flexible modification of the model parameters and the independence of the text input features. Modification of an input speech signal through manipulation of the unobserved articulatory movements is achievable through a sequence of inversion and production mappings. To ensure the naturalness of articulatory movement trajectories, we introduce a method for manipulating articulatory parameters by considering their intercorrelation. Moreover, to generate high-quality modified speech sounds, we avoid the use of vocoder-based excitation generation by presenting several implementations of direct waveform modification capable of directly filtering an input speech signal using the differences in spectral parameters. The experimental results demonstrate that: 1 higher accuracy in the estimation of spectral parameters is achieved by using sequential inversion and production mappings than for conventional production mapping using measured articulatory parameters, 2 the method for manipulating articulatory parameters by considering their intercorrelation makes it possible to generate more natural trajectories of modified articulatory movements; 3 the implementations of the direct waveform modification method significantly improve the quality of modified speech sounds, even under varying speaking conditions; and 4 the controllability of the system is ensured by its capability of producing modified vowel sounds through the manipulation of appropriate articulatory configurations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2337–2350},
numpages = {14}
}

@article{10.1109/TASLP.2017.2756440,
author = {Xiong, Wayne and Droppo, Jasha and Huang, Xuedong and Seide, Frank and Seltzer, Michael L. and Stolcke, Andreas and Yu, Dong and Zweig, Geoffrey},
title = {Toward Human Parity in Conversational Speech Recognition},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2756440},
doi = {10.1109/TASLP.2017.2756440},
abstract = {Conversational speech recognition has served as a flagship speech recognition task since the release of the Switchboard corpus in the 1990s. In this paper, we measure a human error rate on the widely used NIST 2000 test set for commercial bulk transcription. The error rate of professional transcribers is 5.9% for the Switchboard portion of the data, in which newly acquainted pairs of people discuss an assigned topic, and 11.3% for the CallHome portion, where friends and family members have open-ended conversations. In both cases, our automated system edges past the human benchmark, achieving error rates of 5.8% and 11.0%, respectively. The key to our system's performance is the use of various convolutional and long-short-term memory acoustic model architectures, combined with a novel spatial smoothing method and lattice-free discriminative acoustic training, multiple recurrent neural network language modeling approaches, and a systematic use of system combination. Comparing frequent errors in our human and machine transcripts, we find them to be remarkably similar, and highly correlated as a function of the speaker. Human subjects find it very difficult to tell which errorful transcriptions come from humans. Overall, this suggests that, given sufficient matched training data, conversational speech transcription engines are approximating human parity in both quantitative and qualitative terms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2410–2423},
numpages = {14}
}

@article{10.1109/TASLP.2017.2738445,
author = {Asaei, Afsaneh and Cernak, Milos and Bourlard, Herve},
title = {Perceptual Information Loss Due to Impaired Speech Production},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738445},
doi = {10.1109/TASLP.2017.2738445},
abstract = {Phonological classes define articulatory-free and articulatory-bound phone attributes. Deep neural network is used to estimate the probability of phonological classes from the speech signal. In theory, a unique combination of phone attributes form a phoneme identity. Probabilistic inference of phonological classes thus enables estimation of their compositional phoneme probabilities. A novel information theoretic framework is devised to quantify the information conveyed by each phone attribute, and assess the speech production quality for perception of phonemes. As a use case, we hypothesize that disruption in speech production leads to information loss in phone attributes, and thus confusion in phoneme identification. We quantify the amount of information loss due to dysarthric articulation recorded in the TORGO database. A novel information measure is formulated to evaluate the deviation from an ideal phone attribute production leading us to distinguish healthy production from pathological speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2433–2443},
numpages = {11}
}

@article{10.1109/TASLP.2017.2738568,
author = {Janke, Matthias and Diener, Lorenz},
title = {EMG-to-Speech: Direct Generation of Speech From Facial Electromyographic Signals},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738568},
doi = {10.1109/TASLP.2017.2738568},
abstract = {Silent speech interfaces are systems that enable speech communication even when an acoustic signal is unavailable. Over the last years, public interest in such interfaces has intensified. They provide solutions for some of the challenges faced by today's speech-driven technologies, such as robustness to noise and usability for people with speech impediments. In this paper, we provide an overview over our silent speech interface. It is based on facial surface electromyography EMG, which we use to record the electrical signals that control muscle contraction during speech production. These signals are then converted directly to an audible speech waveform, retaining important paralinguistic speech cues for information such as speaker identity and mood. This paper gives an overview over our state-of-the-art direct EMG-to-speech transformation system. This paper describes the characteristics of the speech EMG signal, introduces techniques for extracting relevant features, presents different EMG-to-speech mapping methods, and finally, presents an evaluation of the different methods for real-time capability and conversion quality.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2375–2385},
numpages = {11}
}

@article{10.1109/TASLP.2017.2759002,
author = {Borsky, Michal and Mehta, Daryush D. and Van Stan, Jarrad H. and Gudnason, Jon},
title = {Modal and Nonmodal Voice Quality Classification Using Acoustic and Electroglottographic Features},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2759002},
doi = {10.1109/TASLP.2017.2759002},
abstract = {The goal of this study was to investigate the performance of different feature types for voice quality classification using multiple classifiers. The study compared the COVAREP feature set; which included glottal source features, frequency warped cepstrum, and harmonic model features; against the mel-frequency cepstral coefficients MFCCs computed from the acoustic voice signal, acoustic-based glottal inverse filtered GIF waveform, and electroglottographic EGG waveform. Our hypothesis was that MFCCs can capture the perceived voice quality from either of these three voice signals. Experiments were carried out on recordings from 28 participants with normal vocal status who were prompted to sustain vowels with modal and nonmodal voice qualities. Recordings were rated by an expert listener using the Consensus Auditory-Perceptual Evaluation of Voice CAPE-V, and the ratings were transformed into a dichotomous label presence or absence for the prompted voice qualities of modal voice, breathiness, strain, and roughness. The classification was done using support vector machines, random forests, deep neural networks, and Gaussian mixture model classifiers, which were built as speaker independent using a leave-one-speaker-out strategy. The best classification accuracy of 79.97% was achieved for the full COVAREP set. The harmonic model features were the best performing subset, with 78.47% accuracy, and the static+dynamic MFCCs scored at 74.52%. A closer analysis showed that MFCC and dynamic MFCC features were able to classify modal, breathy, and strained voice quality dimensions from the acoustic and GIF waveforms. Reduced classification performance was exhibited by the EGG waveform.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2281–2291},
numpages = {11}
}

@article{10.1109/TASLP.2017.2755400,
author = {Chen, Fei and Wang, Lan and Chen, Hui and Peng, Gang},
title = {Investigations on Mandarin Aspiratory Animations Using an Airflow Model},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2755400},
doi = {10.1109/TASLP.2017.2755400},
abstract = {Various three-dimensional 3-D talking heads have been developed lately for language learning, with both external and internal articulatory movements being visualized to guide learning. Mandarin pronunciation animation is challenging due to its confusable stops and affricates with similar places of articulation. Until now, less attention has been paid to the biosignal information of aspiratory airflow, which is essential in distinguishing Mandarin consonants. This study fills a research gap by presenting the quantitative analyses of airflow, and then designing an airflow model for a 3-D pronunciation system. The airflow information was collected by Phonatory Aerodynamic System, so that confusable consonants in Mandarin could be discerned by mean airflow rate, peak airflow rate, airflow duration, and peak time. Based on the airflow parameters, an airflow model using the physical equation of fluid flow was proposed and solved, which was then combined and synchronized with the existing 3-D articulatory model. Therefore, the new multimodal system was implemented to synchronously exhibit the airflow motions and articulatory movements of uttering Mandarin syllables. Both an audio-visual perception test and a pronunciation training study were conducted to assess the effectiveness of our system. Perceptual results indicated that identification accuracy was improved for both native and nonnative groups with the help of airflow motions, while native perceivers exhibited higher accuracy due to long-term language experience. Moreover, our system helped Japanese learners of Mandarin enhance their production skills of Mandarin aspirated consonants, reflected by higher gain values of voice onset time after training.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2399–2409},
numpages = {11}
}

@article{10.1109/TASLP.2017.2752365,
author = {Schultz, Tanja and Wand, Michael and Hueber, Thomas and Krusienski, Dean J. and Herff, Christian and Brumberg, Jonathan S.},
title = {Biosignal-Based Spoken Communication: A Survey},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2752365},
doi = {10.1109/TASLP.2017.2752365},
abstract = {Speech is a complex process involving a wide range of biosignals, including but not limited to acoustics. These biosignals—stemming from the articulators, the articulator muscle activities, the neural pathways, and the brain itself—can be used to circumvent limitations of conventional speech processing in particular, and to gain insights into the process of speech production in general. Research on biosignal-based speech processing is a wide and very active field at the intersection of various disciplines, ranging from engineering, computer science, electronics and machine learning to medicine, neuroscience, physiology, and psychology. Consequently, a variety of methods and approaches have been used to investigate the common goal of creating biosignal-based speech processing devices for communication applications in everyday situations and for speech rehabilitation, as well as gaining a deeper understanding of spoken communication. This paper gives an overview of the various modalities, research approaches, and objectives for biosignal-based spoken communication.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2257–2271},
numpages = {15}
}

@article{10.1109/TASLP.2017.2757263,
author = {Gonzalez, Jose A. and Cheah, Lam A. and Gomez, Angel M. and Green, Phil D. and Gilbert, James M. and Ell, Stephen R. and Moore, Roger K. and Holdsworth, Ed},
title = {Direct Speech Reconstruction From Articulatory Sensor Data by Machine Learning},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2757263},
doi = {10.1109/TASLP.2017.2757263},
abstract = {This paper describes a technique that generates speech acoustics from articulator movements. Our motivation is to help people who can no longer speak following laryngectomy, a procedure that is carried out tens of thousands of times per year in the Western world. Our method for sensing articulator movement, permanent magnetic articulography, relies on small, unobtrusive magnets attached to the lips and tongue. Changes in magnetic field caused by magnet movements are sensed and form the input to a process that is trained to estimate speech acoustics. In the experiments reported here this “Direct Synthesis” technique is developed for normal speakers, with glued-on magnets, allowing us to train with parallel sensor and acoustic data. We describe three machine learning techniques for this task, based on Gaussian mixture models, deep neural networks, and recurrent neural networks RNNs. We evaluate our techniques with objective acoustic distortion measures and subjective listening tests over spoken sentences read from novels the CMU Arctic corpus. Our results show that the best performing technique is a bidirectional RNN BiRNN, which employs both past and future contexts to predict the acoustics from the sensor data. BiRNNs are not suitable for synthesis in real time but fixed-lag RNNs give similar results and, because they only look a little way into the future, overcome this problem. Listening tests show that the speech produced by this method has a natural quality that preserves the identity of the speaker. Furthermore, we obtain up to 92% intelligibility on the challenging CMU Arctic material. To our knowledge, these are the best results obtained for a silent-speech system without a restricted vocabulary and with an unobtrusive device that delivers audio in close to real time. This work promises to lead to a technology that truly will give people whose larynx has been removed their voices back.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2362–2374},
numpages = {13}
}

@article{10.1109/TASLP.2017.2738564,
author = {Dromey, Christopher and Black, Katherine M.},
title = {Effects of Laryngeal Activity on Articulation},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738564},
doi = {10.1109/TASLP.2017.2738564},
abstract = {This study examined the effects of three speech conditions voiced, whispered, and mouthed on global average measures of articulatory movement during sentence production. Participants were 20 adults ten males and ten females with no history of speech, language, or hearing disorders. They produced six target utterances in the three speaking conditions. Movements of the following articulators were recorded with an electromagnetic articulograph: Mid tongue, front of tongue, jaw, lower lip, and upper lip. The kinematic metrics were averages derived from movement strokes defined by minima in the speed record of each articulator. These measures revealed a number of significant changes between the voiced and mouthed conditions, with relatively few differences between voiced and whispered speech. Significant increases in sentence duration, articulatory stroke count, and stroke duration as well as significant decreases in peak stroke speed, stroke distance, and hull areas were found in the mouthed condition. These findings suggest that both laryngeal activation and auditory feedback play an important role in the production of normally articulated vocal tract movements, and that the absence of these may account for the significant changes in articulation between the voiced and mouthed conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2272–2280},
numpages = {9}
}

@article{10.1109/TASLP.2017.2758164,
author = {Rezazadeh Sereshkeh, Alborz and Trott, Robert and Bricout, Aurelien and Chau, Tom},
title = {EEG Classification of Covert Speech Using Regularized Neural Networks},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2758164},
doi = {10.1109/TASLP.2017.2758164},
abstract = {Communication using brain–computer interfaces BCIs can be non-intuitive, often requiring the performance of a conversation-irrelevant task such as hand motor imagery. In this paper, the reliability of electroencephalography EEG signals in discriminating between different covert speech tasks is investigated. Twelve participants, across two sessions each, were asked to perform multiple iterations of three differing mental tasks for 10 s each: unconstrained rest or the mental repetition of the words “yes” or “no.” A multilayer perceptron MLP artificial neural network ANN was used to classify all three pairwise combinations of “yes,” “no,” and rest trials and also for ternary classification. An average accuracy of 75.7% ± 9.6 was reached in the classification of covert speech trials versus rest, with all participants exceeding chance level 57.8%. The classification of “yes” versus “no” yielded an average accuracy of 63.2 ± 6.4 with ten participants surpassing chance level 57.8%. Finally, the ternary classification yielded an average accuracy of 54.1% ± 9.7 with all participants exceeding chance level 39.1%. The proposed MLP network provided significantly higher accuracies compared to some of the most common classification techniques in BCI. To our knowledge, this is the first report of using ANN for the classification of EEG covert speech across multiple sessions. Our findings support further study of covert speech as a BCI activation task, potentially leading to the development of more intuitive BCIs for communication.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2292–2300},
numpages = {9}
}

@article{10.1109/TASLP.2017.2738559,
author = {Grozdic, Dorde T. and Jovicic, Slobodan T.},
title = {Whispered Speech Recognition Using Deep Denoising Autoencoder and Inverse Filtering},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738559},
doi = {10.1109/TASLP.2017.2738559},
abstract = {Due to the profound differences between acoustic characteristics of neutral and whispered speech, the performance of traditional automatic speech recognition ASR systems trained on neutral speech degrades significantly when whisper is applied. In order to deeply analyze this mismatched train/test situation and to develop an efficient way for whisper recognition, this study first analyzes acoustic characteristics of whispered speech, addresses the problems of whispered speech recognition in mismatched conditions, and then proposes a new robust cepstral features and preprocessing approach based on deep denoising autoencoder DDAE that enhance whisper recognition. The experimental results confirm that Teager-energy-based cepstral features, especially TECCs, are more robust and better whisper descriptors than traditional Mel-frequency cepstral coefficients MFCC. Further detailed analysis of cepstral distances, distributions of cepstral coefficients, confusion matrices, and experiments with inverse filtering, prove that voicing in speech stimuli is the main cause of word misclassification in mismatched train/test scenarios. The new framework based on DDAE and TECC feature, significantly improves whisper recognition accuracy and outperforms traditional MFCC and GMM-HMM Gaussian mixture density—Hidden Markov model baseline, resulting in an absolute 31% improvement of whisper recognition accuracy. The achieved word recognition rate in neutral/whisper scenario is 92.81%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2313–2322},
numpages = {10}
}

@article{10.1109/TASLP.2017.2751420,
author = {Zhang, Biao and Xiong, Deyi and Su, Jinsong and Duan, Hong},
title = {A Context-Aware Recurrent Encoder for Neural Machine Translation},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2751420},
doi = {10.1109/TASLP.2017.2751420},
abstract = {Neural machine translation NMT heavily relies on its encoder to capture the underlying meaning of a source sentence so as to generate a faithful translation. However, most NMT encoders are built upon either unidirectional or bidirectional recurrent neural networks, which either do not deal with future context or simply concatenate the history and future context to form context-dependent word representations, implicitly assuming the independence of the two types of contextual information. In this paper, we propose a novel context-aware recurrent encoder CAEncoder, as an alternative to the widely-used bidirectional encoder, such that the future and history contexts can be fully incorporated into the learned source representations. Our CAEncoder involves a two-level hierarchy: The bottom level summarizes the history information, whereas the upper level assembles the summarized history and future context into source representations. Additionally, CAEncoder is as efficient as the bidirectional RNN encoder in terms of both training and decoding. Experiments on both Chinese–English and English–German translation tasks show that CAEncoder achieves significant improvements over the bidirectional RNN encoder on a widely-used NMT system. 1},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2424–2432},
numpages = {9}
}

@article{10.1109/TASLP.2017.2740000,
author = {Meltzner, Geoffrey S. and Heaton, James T. and Deng, Yunbin and De Luca, Gianluca and Roy, Serge H. and Kline, Joshua C.},
title = {Silent Speech Recognition as an Alternative Communication Device for Persons With Laryngectomy},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2740000},
doi = {10.1109/TASLP.2017.2740000},
abstract = {Each year thousands of individuals require surgical removal of the larynx voice box due to trauma or disease, and thereby require an alternative voice source or assistive device to verbally communicate. Although natural voice is lost after laryngectomy, most muscles controlling speech articulation remain intact. Surface electromyographic sEMG activity of speech musculature can be recorded from the neck and face, and used for automatic speech recognition to provide speech-to-text or synthesized speech as an alternative means of communication. This is true even when speech is mouthed or spoken in a silent subvocal manner, making it an appropriate communication platform after laryngectomy. In this study, eight individuals at least 6 months after total laryngectomy were recorded using eight sEMG sensors on their face 4 and neck 4 while reading phrases constructed from a 2500-word vocabulary. A unique set of phrases were used for training phoneme-based recognition models for each of the 39 commonly used phonemes in English, and the remaining phrases were used for testing word recognition of the models based on phoneme identification from running speech. Word error rates were on average 10.3% for the full eight-sensor set averaging 9.5% for the top four participants, and 13.6% when reducing the sensor set to four locations per individual n = 7. This study provides a compelling proof-of-concept for sEMG-based alaryngeal speech recognition, with the strong potential to further improve recognition performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2386–2398},
numpages = {13}
}

@article{10.1109/TASLP.2017.2750760,
author = {Ma, Ning and May, Tobias and Brown, Guy J.},
title = {Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localization of Multiple Sources in Reverberant Environments},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2750760},
doi = {10.1109/TASLP.2017.2750760},
abstract = {This paper presents a novel machine-hearing system that exploits deep neural networks DNNs and head movements for robust binaural localization of multiple sources in reverberant environments. DNNs are used to learn the relationship between the source azimuth and binaural cues, consisting of the complete cross-correlation function CCF and interaural level differences ILDs. In contrast to many previous binaural hearing systems, the proposed approach is not restricted to localization of sound sources in the frontal hemifield. Due to the similarity of binaural cues in the frontal and rear hemifields, front–back confusions often occur. To address this, a head movement strategy is incorporated in the localization model to help reduce the front–back errors. The proposed DNN system is compared to a Gaussian-mixture-model-based system that employs interaural time differences ITDs and ILDs as localization features. Our experiments show that the DNN is able to exploit information in the CCF that is not available in the ITD cue, which together with head movements substantially improves localization accuracies under challenging acoustic scenarios, in which multiple talkers and room reverberation are present.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2444–2453},
numpages = {10}
}

@article{10.1109/TASLP.2017.2751747,
author = {Sahraeian, Reza and Van Compernolle, Dirk},
title = {Crosslingual and Multilingual Speech Recognition Based on the Speech Manifold},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2751747},
doi = {10.1109/TASLP.2017.2751747},
abstract = {Speech signals are produced by the smooth and continuous movements of the human articulators. An articulatory representation of speech is considered to be a more compact, more universal, and language-independent speech feature space and can, therefore, improve crosslingual and multilingual speech recognition systems, especially when porting components from one language to another in low-resource scenarios. However, learning the acoustic-to-articulatory conversion has proven to be a very challenging task. In this paper, we utilize a manifold learning technique to derive a nonlinear feature transformation from the conventional filterbank feature space to an articulatory-like feature space. The coordinates in the resultant representation of which some have demonstrable phonological meaning are shown to be highly portable across languages. We propose a proper framework in terms of data selection and graph construction to train coordinates from multilingual data, which allows for training the coordinate space when we have abundant out-of-language data. Deep neural network DNN bottleneck features are demonstrated to exhibit a greater degree of language independence when using this representation than in the case of filterbank features as inputs. The usability of this representation is further demonstrated in a number of speech recognition experiments using DNNs in a variety of crosslingual and multilingual scenarios using the multilingual GlobalPhone dataset. Especially, speech recognition systems developed in low-resource settings profit from the improved portability across languages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2301–2312},
numpages = {12}
}

@article{10.1109/TASLP.2017.2756818,
author = {Steiner, Ingmar and Le Maguer, Sebastien and Hewer, Alexander},
title = {Synthesis of Tongue Motion and Acoustics From Text Using a Multimodal Articulatory Database},
year = {2017},
issue_date = {December 2017},
publisher = {IEEE Press},
volume = {25},
number = {12},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2756818},
doi = {10.1109/TASLP.2017.2756818},
abstract = {We present an end-to-end text-to-speech TTS synthesis system that generates audio and synchronized tongue motion directly from text. This is achieved by adapting a three-dimensional model of the tongue surface to an articulatory dataset and training a statistical parametric speech synthesis system directly on the tongue model parameters. We evaluate the model at every step by comparing the spatial coordinates of predicted articulatory movements against the reference data. The results indicate a global mean Euclidean distance of less than 2.8 mm, and our approach can be adapted to add an articulatory modality to conventional TTS applications without the need for extra data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = dec,
pages = {2351–2361},
numpages = {11}
}

@article{10.1109/TASLP.2017.2763038,
author = {Stefanakis, N. and Pavlidi, D. and Mouchtaris, A.},
title = {Corrections to "Perpendicular Cross-Spectra Fusion for Sound Source Localization With a Planar Microphone Array" [Sep 17 1821-1835]},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2763038},
doi = {10.1109/TASLP.2017.2763038},
abstract = {Presents corrections to the paper, "Perpendicular cross-spectra fusion for sound source localization with a planar microphone array," Stefanakis, N., et al, IEEE/ACMTrans. Audio, Speech, Lang. Process., vol. 25, no. 9, pp. 1821-1835, Sep. 2017.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2251},
numpages = {1}
}

@article{10.1109/TASLP.2017.2743620,
author = {Pal, Monisankha and Saha, Goutam and Pal, Monisankha and Saha, Goutam},
title = {Spectral Mapping Using Prior Re-Estimation of i-Vectors and System Fusion for Voice Conversion},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2743620},
doi = {10.1109/TASLP.2017.2743620},
abstract = {In this paper, we propose a new voice conversion VC method using i-vectors which consider low-dimensional representation of speech utterances. An attempt is made to restrict the i-vector variability in the intermediate computation of total variability T matrix by using a novel approach that uses modified-prior distribution of the intermediate i-vectors. This T-modification improves the speaker individuality conversion. For further improvement of conversion score and to keep a better balance between similarity and quality, band-wise spectrogram fusion between conventional joint density Gaussian mixture model JDGMM and i-vector based converted spectrograms is employed. The fused spectrogram retains more spectral details and leverages the complementary merits of each subsystem. Experiments in terms of objective and subjective evaluation are conducted extensively on CMU ARCTIC database. The results show that the proposed technique can produce a better trade-off between similarity and quality score than other state-of-the-art baseline VC methods. Furthermore, it works better than JDGMM in limited VC training data. The proposed VC performs moderately better both objective and subjective than mixture of factor analyzer based baseline VC. In addition, the proposed VC provides better quality converted speech as compared to maximum likelihood-GMM VC with dynamic feature constraint.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2071–2084},
numpages = {14}
}

@article{10.1109/TASLP.2017.2740002,
author = {Hayashi, Tomoki and Watanabe, Shinji and Toda, Tomoki and Hori, Takaaki and Le Roux, Jonathan and Takeda, Kazuya and Hayashi, Tomoki and Watanabe, Shinji and Toda, Tomoki and Hori, Takaaki and Le Roux, Jonathan and Takeda, Kazuya},
title = {Duration-Controlled LSTM for Polyphonic Sound Event Detection},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2740002},
doi = {10.1109/TASLP.2017.2740002},
abstract = {This paper presents a new hybrid approach called duration-controlled long short-term memory LSTM for polyphonic sound event detection SED. It builds upon a state-of-the-art SED method that performs frame-by-frame detection using a bidirectional LSTM recurrent neural network BLSTM, and incorporates a duration-controlled modeling technique based on a hidden semi-Markov model. The proposed approach makes it possible to model the duration of each sound event precisely and to perform sequence-by-sequence detection without having to resort to thresholding, as in conventional frame-by-frame methods. Furthermore, to effectively reduce sound event insertion errors, which often occur under noisy conditions, we also introduce a binary-mask-based postprocessing that relies on a sound activity detection network to identify segments with any sound event activity, an approach inspired by the well-known benefits of voice activity detection in speech recognition systems. We conduct an experiment using the DCASE2016 task 2 dataset to compare our proposed method with typical conventional methods, such as nonnegative matrix factorization and standard BLSTM. Our proposed method outperforms the conventional methods both in an event-based evaluation, achieving a 75.3% F1 score and a 44.2% error rate, and in a segment-based evaluation, achieving an 81.1% F1 score, and a 32.9% error rate, outperforming the best results reported in the DCASE2016 task 2 Challenge.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2059–2070},
numpages = {12}
}

@article{10.1109/TASLP.2017.2738438,
author = {Schwartz, Boaz and Gannot, Sharon and Habets, Emanuel A. P. and Schwartz, Boaz and Gannot, Sharon and Habets, Emanuel A. P.},
title = {Two Model-Based EM Algorithms for Blind Source Separation in Noisy Environments},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738438},
doi = {10.1109/TASLP.2017.2738438},
abstract = {The problem of blind separation of speech signals in the presence of noise using multiple microphones is addressed. Blind estimation of the acoustic parameters and the individual source signals is carried out by applying the expectation-maximization EM algorithm. Two models for the speech signals are used, namely an unknown deterministic signal model and a complex-Gaussian signal model. For the two alternatives, we define a statistical model and develop EM-based algorithms to jointly estimate the acoustic parameters and the speech signals. The resulting algorithms are then compared from both theoretical and performance perspectives. In both cases, the latent data differently defined for each alternative are estimated in the E-step, where in the M-step, the two algorithms estimate the acoustic transfer functions of each source and the noise covariance matrix. The algorithms differ in the way the clean speech signals are used in the EM scheme. When the clean signal is assumed deterministic unknown, only the a posteriori probabilities of the presence of each source are estimated in the E-step, whereas their time-frequency coefficients are the parameters that are estimated in the M-step using the minimum variance distortionless response beamformer. If the clean speech signals are modeled as complex Gaussian signals, their power spectral densities are estimated in the E-step using the multichannel Wiener filter output. The proposed algorithms were tested using reverberant noisy mixtures of two speech sources in different reverberation and noise conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2209–2222},
numpages = {14}
}

@article{10.1109/TASLP.2017.2750767,
author = {Ottosen, Emil Solsbaek and Dorfler, Monika and Ottosen, Emil Solsbaek and Dorfler, Monika},
title = {A Phase Vocoder Based on Nonstationary Gabor Frames},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2750767},
doi = {10.1109/TASLP.2017.2750767},
abstract = {We propose a new algorithm for time stretching music signals based on the theory of nonstationary Gabor frames NSGFs. The algorithm extends the techniques of the classical phase vocoder PV by incorporating adaptive time-frequency TF representations and adaptive phase locking. The adaptive TF representations imply good time resolution for the onsets of attack transients and good frequency resolution for the sinusoidal components. We estimate the phase values only at peak channels and the remaining phases are then locked to the values of the peaks in an adaptive manner. During attack transients we keep the stretch factor equal to one and we propose a new strategy for determining which channels are relevant for reinitializing the corresponding phase values. In contrast to previously published algorithms we use a non-uniform NSGF to obtain a low redundancy of the corresponding TF representation. We show that with just three times as many TF coefficients as signal samples, artifacts such as phasiness and transient smearing can be greatly reduced compared to the classical PV. The proposed algorithm is tested on both synthetic and real-world signals and compared with state-of-the-art algorithms in a reproducible manner.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2199–2208},
numpages = {10}
}

@article{10.1109/TASLP.2017.2746264,
author = {Song, Eunwoo and Soong, Frank K. and Kang, Hong-Goo and Eunwoo Song and Soong, Frank K. and Hong-Goo Kang},
title = {Effective Spectral and Excitation Modeling Techniques for LSTM-RNN-Based Speech Synthesis Systems},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2746264},
doi = {10.1109/TASLP.2017.2746264},
abstract = {In this paper, we report research results on modeling the parameters of an improved time-frequency trajectory excitation ITFTE and spectral envelopes of an LPC vocoder with a long short-term memory LSTM-based recurrent neural network RNN for high-quality text-to-speech TTS systems. The ITFTE vocoder has been shown to significantly improve the perceptual quality of statistical parameter-based TTS systems in our prior works. However, a simple feed-forward deep neural network DNN with a finite window length is inadequate to capture the time evolution of the ITFTE parameters. We propose to use the LSTM to exploit the time-varying nature of both trajectories of the excitation and filter parameters, where the LSTM is implemented to use the linguistic text input and to predict both ITFTE and LPC parameters holistically. In the case of LPC parameters, we further enhance the generated spectrum by applying LP bandwidth expansion and line spectral frequency-sharpening filters. These filters are not only beneficial for reducing unstable synthesis filter conditions but also advantageous toward minimizing the muffling problem in the generated spectrum. Experimental results have shown that the proposed LSTM-RNN system with the ITFTE vocoder significantly outperforms both similarly configured band aperiodicity-based systems and our best prior DNN-trainecounterpart, both objectively and subjectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2152–2161},
numpages = {10}
}

@article{10.1109/TASLP.2017.2748240,
author = {Sharma, Pulkit and Abrol, Vinayak and Sao, Anil Kumar and Sharma, Pulkit and Abrol, Vinayak and Sao, Anil Kumar},
title = {Deep-Sparse-Representation-Based Features for Speech Recognition},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2748240},
doi = {10.1109/TASLP.2017.2748240},
abstract = {Features derived using sparse representation SR-based approaches have been shown to yield promising results for speech recognition tasks. In most of the approaches, the SR corresponding to speech signal is estimated using a dictionary, which could be either exemplar based or learned. However, a single-level decomposition may not be suitable for the speech signal, as it contains complex hierarchical information about various hidden attributes. In this paper, we propose to use a multilevel decomposition having multiple layers, also known as the deep sparse representation DSR, to derive a feature representation for speech recognition. Instead of having a series of sparse layers, the proposed framework employs a dense layer between two sparse layers, which helps in efficient implementation. Our studies reveal that the representations obtained at different sparse layers of the proposed DSR model have complimentary information. Thus, the final feature representation is derived after concatenating the representations obtained at the sparse layers. This results in a more discriminative representation, and improves the speech recognition performance. Since the concatenation results in a high-dimensional feature, principal component analysis is used to reduce the dimension of the obtained feature. Experimental studies demonstrate that the proposed feature outperforms existing features for various speech recognition tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2162–2175},
numpages = {14}
}

@article{10.1109/TASLP.2017.2744264,
author = {Menzies, Dylan and Fazi, Filippo Maria and Menzies, Dylan and Fazi, Filippo Maria},
title = {Decoding and Compression of Channel and Scene Objects for Spatial Audio},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2744264},
doi = {10.1109/TASLP.2017.2744264},
abstract = {Sound fields can be encoded with a fixed number of signals, using microphones or panning functions. The sound field may later be reproduced approximately by decoding the signals to a loudspeaker array. The Stereo and Ambisonic systems provide examples. A framework is presented for addressing general questions about such encodings. The first problem considered is the conversion between encodings. The solution is applied to the decoding of scene encodings to a loudspeaker array. This is generalized to the decoding of subscenes where the resolution is focused in an angular window. Within an object-based audio framework such subscenes are useful for representing complex objects without using all the channels required for a full scene. The second problem considered is the compression of a scene encoding to a smaller encoding, from which the original can be reconstructed. The spatial distribution of compression error can be controlled.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2138–2151},
numpages = {14}
}

@article{10.1109/TASLP.2017.2743340,
author = {Muckenhirn, Hannah and Korshunov, Pavel and Magimai-Doss, Mathew and Marcel, Sebastien and Muckenhirn, Hannah and Korshunov, Pavel and Magimai-Doss, Mathew and Marcel, Sebastien},
title = {Long-Term Spectral Statistics for Voice Presentation Attack Detection},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2743340},
doi = {10.1109/TASLP.2017.2743340},
abstract = {Automatic speaker verification systems can be spoofed through recorded, synthetic, or voice converted speech of target speakers. To make these systems practically viable, the detection of such attacks, referred to as presentation attacks, is of paramount interest. In that direction, this paper investigates two aspects: 1 a novel approach to detect presentation attacks where, unlike conventional approaches, no speech signal modeling related assumptions are made, rather the attacks are detected by computing first-order and second-order spectral statistics and feeding them to a classifier, and 2 generalization of the presentation attack detection systems across databases. Our investigations on ASVspoof 2015 challenge database and AVspoof database show that, when compared to the approaches based on conventional short-term spectral features, the proposed approach with a linear discriminative classifier yields a better system, irrespective of whether the spoofed signal is replayed to the microphone or is directly injected into the system software process. Cross-database investigations show that neither the short-term spectral processing-based approaches nor the proposed approach yield systems which are able to generalize across databases or methods of attack. Thus, revealing the difficulty of the problem and the need for further resources and research.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2098–2111},
numpages = {14}
}

@article{10.1109/TASLP.2017.2744799,
author = {Hamilton, Brian and Bilbao, Stefan and Hamilton, Brian and Bilbao, Stefan},
title = {FDTD Methods for 3-D Room Acoustics Simulation With High-Order Accuracy in Space and Time},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2744799},
doi = {10.1109/TASLP.2017.2744799},
abstract = {Time-domain finite difference FDTD methods are popular tools for three-dimensional 3-D room acoustics modeling, but numerical dispersion is an inherent problem that can place limitations on the usable bandwidth of a given scheme. Compact explicit 27-point schemes and “large-star” schemes with high-order spatial differences offer improvements to the simplest scheme, but are ultimately limited by their second-order accuracy in time. In this paper, we use modified equation methods to derive FDTD schemes with high orders of accuracy in both space and time, resulting in significant improvements in numerical dispersion as compared to the aforementioned schemes. In comparison to such schemes, the high-order accurate schemes presented in this paper use significantly less memory and fewer operations when low error tolerances in numerical phase velocities are critical, leading to higher usable bandwidths for auralization purposes. Simulation results are also presented, demonstrating improved approximations to modal frequencies of a shoe-box room and free-space propagation of a bandlimited pulse.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2112–2124},
numpages = {13}
}

@article{10.1109/TASLP.2017.2756443,
author = {Giorgi, Bruno Di and Dixon, Simon and Zanoni, Massimiliano and Sarti, Augusto and Di Giorgi, Bruno and Dixon, Simon and Zanoni, Massimiliano and Sarti, Augusto},
title = {A Data-Driven Model of Tonal Chord Sequence Complexity},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2756443},
doi = {10.1109/TASLP.2017.2756443},
abstract = {We present a compound language model of tonal chord sequences, and evaluate its capability to estimate perceived harmonic complexity. In order to build the compound model, we trained three different models: prediction by partial matching, a hidden Markov model and a deep recurrent neural network on a novel large dataset containing half a million annotated chord sequences. We describe the training process and propose an interpretation of the harmonic patterns that are learned by the hidden states of these models. We use the compound model to generate new chord sequences and estimate their probability, which we then relate to perceived harmonic complexity. In order to collect subjective ratings of complexity, we devised a listening test comprising two different experiments. In the first, subjects choose the more complex chord sequence between two. In the second, subjects rate with a continuous scale the complexity of a single chord sequence. The results of both experiments show a strong relation between negative log probability, given by our language model, and the perceived complexity ratings. The relation is stronger for subjects with high musical sophistication index, acquired through the GoldMSI standard questionnaire. The analysis of the results also includes the preference ratings that have been collected along with the complexity ratings; a weak negative correlation emerged between preference and log probability.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2237–2250},
numpages = {14}
}

@article{10.1109/TASLP.2017.2747097,
author = {Yu, Chengzhu and Hansen, John H. L. and Chengzhu Yu and Hansen, John H. L.},
title = {Active Learning Based Constrained Clustering For Speaker Diarization},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2747097},
doi = {10.1109/TASLP.2017.2747097},
abstract = {Most speaker diarization research has focused on unsupervised scenarios, where no human supervision is available. However, in many real-world applications, a certain amount of human input could be expected, especially when minimal human supervision brings significant performance improvement. In this study, we propose an active learning based bottom-up speaker clustering algorithm to effectively improve speaker diarization performance with limited human input. Specifically, the proposed active learning based speaker clustering has two different stages: explore and constrained clustering. The explore stage is to quickly discover at least one sample for each speaker for boosting speaker clustering process with reliable initial speaker clusters. After discovering all, or a majority, of the involved speakers during explore stage, the constrained clustering is performed. Constrained clustering is similar to traditional bottom-up clustering process with an important difference that the clusters created during explore stage are restricted from merging with each other. Constrained clustering continues until only the clusters generated from the explore stage are left. Since the objective of active learning based speaker clustering algorithm is to provide good initial speaker models, performance saturates as soon as sufficient examples are ensured for each cluster. To further improve diarization performance with increasing human input, we propose a second method which actively select speech segments that account for the largest expected speaker error from existing cluster assignments for human evaluation and reassignment. The algorithms are evaluated on our recently created Apollo Mission Control Center dataset as well as augmented multiparty interaction meeting corpus. The results indicate that the proposed active learning algorithms are able to reduce diarization error rate significantly with a relatively small amount of human supervision.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2188–2198},
numpages = {11}
}

@article{10.1109/TASLP.2017.2747082,
author = {Mowlaee, Pejman and Blass, Martin and Kleijn, W. Bastiaan and Mowlaee, Pejman and Blass, Martin and Kleijn, W. Bastiaan},
title = {New Results in Modulation-Domain Single-Channel Speech Enhancement},
year = {2017},
issue_date = {November 2017},
publisher = {IEEE Press},
volume = {25},
number = {11},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2747082},
doi = {10.1109/TASLP.2017.2747082},
abstract = {We investigate single-channel speech enhancement using the double spectrum DS consisting of pitch-synchronous and modulation transforms. We first explore the fundamentals of the proposed DS domain and its advantageous properties for pitch estimation and speech presence probability estimation. We then propose speech enhancement methods based on adaptive weighting and Wiener filtering in the DS domain. We demonstrate the effectiveness of the proposed DS-based methods compared to the conventional benchmarks in the modulation or short-time Fourier transform domains. Our results show a good tradeoff between improved perceived quality and slight degradation in speech intelligibility is achieved by the proposed method across different signal-to-noise ratios and noise types.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = nov,
pages = {2125–2137},
numpages = {13}
}

@article{10.1109/TASLP.2017.2738698,
author = {Jo, Byeongho and Choi, Jung-Woo and Byeongho Jo and Jung-Woo Choi},
title = {Spherical Harmonic Smoothing for Localizing Coherent Sound Sources},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2738698},
doi = {10.1109/TASLP.2017.2738698},
abstract = {Correlated or coherent sources can cause the localization performance of subspace-based beamformers to deteriorate. To solve this problem, various smoothing techniques have been proposed for the localization of multiple coherent sound sources. A common principle of smoothing techniques is to increase the rank of a covariance matrix by constructing multiple subarrays in the space, time, or frequency domain. The construction of such subarrays, however, requires the satisfaction of strong assumptions regarding the microphone positions or the temporal/spectral structures of the signals. In this paper, we propose a spherical harmonic smoothing technique that can perform smoothing in terms of spherical harmonic coefficients only. Unlike other smoothing techniques, the proposed technique constructs subarrays of spherical harmonic coefficients and uses them to increase the number of linearly independent observations in a signal subspace. Subarray construction in the spherical harmonics domain enables the accurate localization of multiple coherent sources even at a single frequency. The proposed technique can be applied to an arbitrary microphone array as long as the spherical harmonic coefficients can be measured up to a finite order.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1969–1984},
numpages = {16}
}

@article{10.1109/TASLP.2017.2740004,
author = {Jokinen, Emma and Remes, Ulpu and Alku, Paavo and Jokinen, Emma and Remes, Ulpu and Alku, Paavo},
title = {Intelligibility Enhancement of Telephone Speech Using Gaussian Process Regression for Normal-to-Lombard Spectral Tilt Conversion},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2740004},
doi = {10.1109/TASLP.2017.2740004},
abstract = {Noise in the environment can decrease the quality and intelligibility of a telephone conversation. This study focuses on the intelligibility enhancement of narrowband telephone speech in a near-end noise scenario using a postprocessing method based on normal-to-Lombard spectral tilt conversion. The proposed technique uses nonparallel, conversational normal, and Lombard speech together with Gaussian process regression in order to mimic the flattening of the spectral tilt that occurs in the production of natural speech in noisy conditions. The performance of the proposed method was evaluated in comparison to two reference methods, a fixed high-pass filter, and a baseline spectral tilt conversion, as well as in comparison to unprocessed speech in terms of intelligibility and listening preference in noisy conditions and in terms of pressedness in silent conditions. The results indicate that while the proposed technique provides a similar benefit in terms of intelligibility as fixed high-pass filtering, it is also able to produce a notable increase in pressedness. This suggests that the developed processing of the spectral tilt can compete with fixed high-pass filtering in intelligibility enhancement, but it is also able to convert speech to become perceptually closer to natural Lombard speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1985–1996},
numpages = {12}
}

@article{10.1109/TASLP.2017.2740001,
author = {Li, Xiaofei and Girin, Laurent and Horaud, Radu and Gannot, Sharon and Xiaofei Li and Girin, Laurent and Horaud, Radu and Gannot, Sharon},
title = {Multiple-Speaker Localization Based on Direct-Path Features and Likelihood Maximization With Spatial Sparsity Regularization},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2740001},
doi = {10.1109/TASLP.2017.2740001},
abstract = {This paper addresses the problem of multiple-speaker localization in noisy and reverberant environments, using binaural recordings of an acoustic scene. A complex-valued Gaussian mixture model CGMM is adopted, whose components correspond to all the possible candidate source locations defined on a grid. After optimizing the CGMM-based objective function, given an observed set of complex-valued binaural features, both the number of sources and their locations are estimated by selecting the CGMM components with the largest weights. An entropy-based penalty term is added to the likelihood to impose sparsity over the set of CGMM component weights. This favors a small number of detected speakers with respect to the large number of initial candidate source locations. In addition, the direct-path relative transfer function DP-RTF is used to build robust binaural features. The DP-RTF, recently proposed for single-source localization, encodes interchannel information corresponding to the direct path of sound propagation and is thus robust to reverberations. In this paper, we extend the DP-RTF estimation to the case of multiple sources. In the short-time Fourier transform domain, a consistency test is proposed to check whether a set of consecutive frames is associated with the same source or not. Reliable DP-RTF features are selected from the frames that pass the consistency test to be used for source localization. Experiments carried out using both simulation data and real data recorded with a robotic head confirm the efficiency of the proposed multisource localization method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1997–2012},
numpages = {16}
}

@article{10.1109/TASLP.2017.2736067,
author = {Hafezi, Sina and Moore, Alastair H. and Naylor, Patrick A. and Hafezi, Sina and Moore, Alastair H. and Naylor, Patrick A.},
title = {Augmented Intensity Vectors for Direction of Arrival Estimation in the Spherical Harmonic Domain},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2736067},
doi = {10.1109/TASLP.2017.2736067},
abstract = {Pseudointensity vectors PIVs provide a means of direction of arrival DOA estimation for spherical microphone arrays using only the zeroth and the first-order spherical harmonics. An augmented intensity vector AIV is proposed which improves the accuracy of PIVs by exploiting higher order spherical harmonics. We compared DOA estimation using our proposed AIVs against PIVs, steered response power SRP and subspace methods where the number of sources, their angular separation, the reverberation time of the room and the sensor noise level are varied. The results show that the proposed approach outperforms the baseline methods and performs at least as accurately as the state-of-theart method with strong robustness to reverberation, sensor noise, and number of sources. In the single and multiple source scenarios tested, which include realistic levels of reverberation and noise, the proposed method had average error of 1.5° and 2°, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1956–1968},
numpages = {13}
}

@article{10.1109/TASLP.2017.2724203,
author = {Wang, Siying and Ewert, Sebastian and Dixon, Simon and Siying Wang and Ewert, Sebastian and Dixon, Simon},
title = {Identifying Missing and Extra Notes in Piano Recordings Using Score-Informed Dictionary Learning},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2724203},
doi = {10.1109/TASLP.2017.2724203},
abstract = {The goal of automatic music transcription AMT is to obtain a high-level symbolic representation of the notes played in a given audio recording. Despite being researched for several decades, current methods are still inadequate for many applications. To boost the accuracy in a music tutoring scenario, we exploit that the score to be played is specified and we only need to detect the differences to the actual performance. In contrast to previous work that uses score information for postprocessing, we employ the score to construct a transcription method that is tailored to the given audio recording. By adapting a score-informed dictionary learning technique as used for source separation, we learn for each score pitch a spectral pattern describing the energy distribution of associated notes in the recording. In this paper, we identify several systematic weaknesses in our previous approach and introduce three extensions to improve its performance. First, we extend our dictionary of spectral templates to a dictionary of variable-length spectrotemporal patterns. Second, we integrate the score information using soft rather than hard constraints, to better take into account that differences from the score indeed occur. Third, we introduce new regularizers to guide the learning process. Our experiments show that these extensions particularly improve the accuracy for identifying extra notes, while the accuracy for correct and missing notes remains at a similar level. The influence of each extension is demonstrated with further experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1877–1889},
numpages = {13}
}

@article{10.1109/TASLP.2017.2726762,
author = {Kolbaek, Morten and Yu, Dong and Tan, Zheng-Hua and Jensen, Jesper and Kolbaek, Morten and Dong Yu and Zheng-Hua Tan and Jensen, Jesper},
title = {Multitalker Speech Separation With Utterance-Level Permutation Invariant Training of Deep Recurrent Neural Networks},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2726762},
doi = {10.1109/TASLP.2017.2726762},
abstract = {In this paper, we propose the utterance-level permutation invariant training uPIT technique. uPIT is a practically applicable, end-to-end, deep-learning-based solution for speaker independent multitalker speech separation. Specifically, uPIT extends the recently proposed permutation invariant training PIT technique with an utterance-level cost function, hence eliminating the need for solving an additional permutation problem during inference, which is otherwise required by frame-level PIT. We achieve this using recurrent neural networks RNNs that, during training, minimize the utterance-level separation error, hence forcing separated frames belonging to the same speaker to be aligned to the same output stream. In practice, this allows RNNs, trained with uPIT, to separate multitalker mixed speech without any prior knowledge of signal duration, number of speakers, speaker identity, or gender. We evaluated uPIT on the WSJ0 and Danish two- and three-talker mixed-speech separation tasks and found that uPIT outperforms techniques based on nonnegative matrix factorization and computational auditory scene analysis, and compares favorably with deep clustering, and the deep attractor network. Furthermore, we found that models trained with uPIT generalize well to unseen speakers and languages. Finally, we found that a single model, trained with uPIT, can handle both two-speaker, and three-speaker speech mixtures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1901–1913},
numpages = {13}
}

@article{10.1109/TASLP.2017.2729024,
author = {Chung, Cheng-Tao and Tsai, Cheng-Yu and Liu, Chia-Hsiang and Lee, Lin-Shan and Cheng-Tao Chung and Cheng-Yu Tsai and Chia-Hsiang Liu and Lin-Shan Lee},
title = {Unsupervised Iterative Deep Learning of Speech Features and Acoustic Tokens with Applications to Spoken Term Detection},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2729024},
doi = {10.1109/TASLP.2017.2729024},
abstract = {In this paper, we aim to automatically discover high-quality frame-level speech features and acoustic tokens directly from unlabeled speech data. A multigranular acoustic tokenizer MAT was proposed for automatic discovery of multiple sets of acoustic tokens from the given corpus. Each acoustic token set is specified by a set of hyperparameters describing the model configuration. These different sets of acoustic tokens carry different characteristics for the given corpus and the language behind and, thus, can be mutually reinforced. The multiple sets of token labels are then used as the targets of a multitarget deep neural network MDNN trained on frame-level acoustic features. Bottleneck features extracted from the MDNN are then used as the feedback input to the MAT and the MDNN itself in the next iteration. The multigranular acoustic token sets and the frame-level speech features can be iteratively optimized in the iterative deep learning framework. We call this framework the MAT deep neural network. The results were evaluated using the metrics and corpora defined in the Zero Resource Speech Challenge organized at Interspeech 2015, and improved performance was obtained with a set of experiments of query-by-example spoken term detection on the same corpora. Visualization for the discovered tokens against the English phonemes was also shown.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1914–1928},
numpages = {15}
}

@article{10.1109/TASLP.2017.2723721,
author = {Tian, Xiaohai and Lee, Siu Wa and Wu, Zhizheng and Chng, Eng Siong and Li, Haizhou and Xiaohai Tian and Siu Wa Lee and Zhizheng Wu and Eng Siong Chng and Haizhou Li},
title = {An Exemplar-Based Approach to Frequency Warping for Voice Conversion},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2723721},
doi = {10.1109/TASLP.2017.2723721},
abstract = {The voice conversion's task is to modify a source speaker's voice to sound like that of a target speaker. A conversion method is considered successful when the produced speech sounds natural and similar to the target speaker. This paper presents a new voice conversion framework in which we combine frequency warping and exemplar-based method for voice conversion. Our method maintains high-resolution details during conversion by directly applying frequency warping on the high-resolution spectrum to represent the target. The warping function is generated by a sparse interpolation from a dictionary of exemplar warping functions. As the generated warping function is dependent only on a very small set of exemplars, we do away with the statistical averaging effects inherited from Gaussian mixture models. To compensate for the conversion error, we also apply residual exemplars into the conversion process. Both objective and subjective evaluations on the VOICES database validated the effectiveness of the proposed voice conversion framework. We observed a significant improvement in speech quality over the state-of-the-art parametric methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1863–1876},
numpages = {14}
}

@article{10.1109/TASLP.2017.2724198,
author = {Cumani, Sandro and Laface, Pietro and Cumani, Sandro and Laface, Pietro},
title = {Joint Estimation of PLDA and Nonlinear Transformations of Speaker Vectors},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2724198},
doi = {10.1109/TASLP.2017.2724198},
abstract = {The Gaussian probabilistic linear discriminant anal-ysis PLDA model assumes Gaussian distributed priors for the latent variables that represent the speaker and channel factors. Assuming that each training i-vector belongs to a different speaker, as is usually done in i-vector extraction, i-vectors generated by a PLDA model can be considered independent and identically distributed with Gaussian distribution. Thus, we have recently proposed to transform the development i-vectors so that their distribution becomes more Gaussian-like. This is obtained by means of a sequence of affine and nonlinear transformations whose parameters are trained by maximum likelihood ML estimation on the development set. The evaluation i-vectors are then subject to the same transformation. Although the i-vector “gaussianization” has shown to be effective, since the i-vectors extracted from segments of the same speaker are not independent, the original assumption is not satisfactory. In this work, we show that the model can be improved by properly exploiting the information about the speaker labels, which was ignored in the previous model. In particular, a more effective PLDA model can be obtained by jointly estimating the PLDA parameters and the parameters of the nonlinear transformation of the i-vectors. In other words, while the goal of the previous approach was to “gaussianize” the training i-vectors distribution, the objective of this work is to embed the estimation of the nonlinear i-vector transformation in the PLDA model estimation. We will thus refer to this model as the nonlinear PLDA model. We show that this new approach provides significant gain with respect to PLDA, and a small, yet consistent, improvement with respect to our former i-vector “gaussianization” approach, without further additional costs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1890–1900},
numpages = {11}
}

@article{10.1109/TASLP.2017.2744261,
author = {Baby, Deepak and Van hamme, Hugo and Baby, Deepak and Van hamme, Hugo},
title = {Joint Denoising and Dereverberation Using Exemplar-Based Sparse Representations and Decaying Norm Constraint},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2744261},
doi = {10.1109/TASLP.2017.2744261},
abstract = {Exemplar-based nonnegative models, where the noisy speech is decomposed as a sparse nonnegative linear combination of the speech and noise exemplars stored in a dictionary, have been successfully used for speech denoising. This paper extends this technique for the single-channel speech enhancement in noisy reverberant environments using a novel approximation of the noisy reverberant speech in the frequency domain and nonnegative matrix deconvolution. In the proposed model, the room impulse response RIR in the magnitude short-time Fourier transform domain is defined such that its decaying structure can also be estimated from the test data itself, whereas the existing models used a suboptimal binwise clamping procedure to impose such a decaying structure that does not hold in a typical RIR. This paper presents multiplicative updates for estimating the RIR, its decay, and the underlying anechoic speech and noise. The proposed model is evaluated on a synthetically created dataset created by convolving TIMIT recordings with RIRs measured from different rooms and varying speaker-and-microphone locations, and adding background noises taken from the CHiME corpus. Simulation results show that the proposed model results in a better RIR estimate over the existing model and improves various instrumental speech quality measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {2024–2035},
numpages = {12}
}

@article{10.1109/TASLP.2017.2735179,
author = {Arnela, Marc and Guasch, Oriol and Arnela, Marc and Guasch, Oriol},
title = {Finite Element Synthesis of Diphthongs Using Tuned Two-Dimensional Vocal Tracts},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2735179},
doi = {10.1109/TASLP.2017.2735179},
abstract = {Three-dimensional 3-D vocal tract acoustic modeling has the potential to generate high quality and natural voice sounds, but at the price of a large computational cost. Alternatively, 2-D models based on tuned vocal tracts have shown to provide similar results to the 3-D ones but with less computational demands. However, they are currently limited to the synthesis of static vowel sounds. In this paper, the tuned 2-D approach is extended by considering moving vocal tracts to generate dynamic vowel sounds, like diphthongs. Four tuning steps are followed to build a dynamic 2-D vocal tract model that can recover, to a large extent, the formant locations, bandwidths, and energies of a 3-D vocal tract with circular cross section, set in a spherical baffle representing the human head. Acoustic waves propagating through the time evolving vocal tract and radiating to free-field are simulated using the finite element method in the time-domain. As examples, the diphthongs [Ai] and [Au] have been generated using the tuning approach and compared, by means of objective and subjective evaluations, to those resulting from 3-D and conventional 2-D simulations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {2013–2023},
numpages = {11}
}

@article{10.1109/TASLP.2017.2730284,
author = {Antonello, Niccolo and De Sena, Enzo and Moonen, Marc and Naylor, Patrick A. and van Waterschoot, Toon and Antonello, Niccolo and De Sena, Enzo and Moonen, Marc and Naylor, Patrick A. and van Waterschoot, Toon},
title = {Room Impulse Response Interpolation Using a Sparse Spatio-Temporal Representation of the Sound Field},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2730284},
doi = {10.1109/TASLP.2017.2730284},
abstract = {Room Impulse Responses RIRs are typically measured using a set of microphones and a loudspeaker. When RIRs spanning a large volume are needed, many microphone measurements must be used to spatially sample the sound field. In order to reduce the number of microphone measurements, RIRs can be spatially interpolated. In the present study, RIR interpolation is formulated as an inverse problem. This inverse problem relies on a particular acoustic model capable of representing the measurements. Two different acoustic models are compared: the plane wave decomposition model and a novel time-domain model, which consists of a collection of equivalent sources creating spherical waves. These acoustic models can both approximate any reverberant sound field created by a far-field sound source. In order to produce an accurate RIR interpolation, sparsity regularization is employed when solving the inverse problem. In particular, by combining different acoustic models with different sparsity promoting regularizations, spatial sparsity, spatio-spectral sparsity, and spatio-temporal sparsity are compared. The inverse problem is solved using a matrix-free large-scale optimization algorithm. Simulations show that the best RIR interpolation is obtained when combining the novel time-domain acoustic model with the spatio-temporal sparsity regularization, outperforming the results of the plane wave decomposition model even when far fewer microphone measurements are available.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1929–1941},
numpages = {13}
}

@article{10.1109/TASLP.2017.2732162,
author = {Qian, Yanmin and Chen, Nanxin and Dinkel, Heinrich and Wu, Zhizheng and Yanmin Qian and Nanxin Chen and Dinkel, Heinrich and Zhizheng Wu},
title = {Deep Feature Engineering for Noise Robust Spoofing Detection},
year = {2017},
issue_date = {October 2017},
publisher = {IEEE Press},
volume = {25},
number = {10},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2732162},
doi = {10.1109/TASLP.2017.2732162},
abstract = {Spoofing detection for automatic speaker verification ASV aims to discriminate between genuine and spoofed speech. This topic has received increased attentions recently due to safety concerns with deploying an ASV system. While the performance of spoofing detection has improved significantly in clean condition in recent studies, the performance degrades dramatically in noisy conditions. To address this issue, in this paper, we propose to extract robust and discriminative deep features by using deep learning techniques for spoofing detection. In particular, we employ deep feedforward, recurrent, and convolutional neural networks to extract discriminative features. We also introduce multicondition training, noise-aware training, and annealed dropout training to make neural networks more robust against noise and to avoid overfitting to specific spoofing attacks and noise types. The proposed neural networks and training techniques are combined into a single framework for spoofing detection. Experimental evaluation is carried out on a noisy version of the standard ASVspoof 2015 corpus, including both additive noisy and reverberant scenarios. Experimental results confirm that the proposed system dramatically decreases averaged equal error rates from 19.1% and 22.6% to 3.2% and 5.1% for seen and unseen noisy conditions, respectively.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = oct,
pages = {1942–1955},
numpages = {14}
}

@article{10.1109/TASLP.2017.2717499,
author = {Patel, Vinal and Cheer, Jordan and George, Nithin V. and Patel, Vinal and Cheer, Jordan and George, Nithin V.},
title = {Modified Phase-Scheduled-Command FxLMS Algorithm for Active Sound Profiling},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2717499},
doi = {10.1109/TASLP.2017.2717499},
abstract = {Active sound profiling, or active noise equalization strategies have been proposed to achieve spectral shaping of a primary disturbance signal. The control algorithms proposed to achieve such spectral shaping have either suffered from poor robustness to plant modeling uncertainties or required high levels of control effort. To improve the robustness of active sound profiling to uncertainties in the plant model, while avoiding increased control effort, a modified phase-scheduled-command filtered-x least-mean-square algorithm is proposed in this paper. The new algorithm provides improved stability, while requiring the minimum control effort. This improvement is achieved by replacing the plant model with an intelligent adaptive-hysteresis switching mechanism to allow the necessary estimation of the disturbance signal phase. The improved performance and robustness of the proposed algorithm is demonstrated through a series of simulations using measured acoustic responses.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1799–1808},
numpages = {10}
}

@article{10.1109/TASLP.2017.2722103,
author = {Nakamura, Eita and Yoshii, Kazuyoshi and Dixon, Simon and Nakamura, Eita and Yoshii, Kazuyoshi and Dixon, Simon},
title = {Note Value Recognition for Piano Transcription Using Markov Random Fields},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2722103},
doi = {10.1109/TASLP.2017.2722103},
abstract = {This paper presents a statistical method for use in music transcription that can estimate score times of note onsets and offsets from polyphonic MIDI performance signals. Because performed note durations can deviate largely from score-indicated values, previous methods had the problem of not being able to accurately estimate offset score times or note values and, thus, could only output incomplete musical scores. Based on observations that the pitch context and onset score times are influential on the configuration of note values, we construct a context-tree model that provides prior distributions of note values using these features and combine it with a performance model in the framework of Markov random fields. Evaluation results show that our method reduces the average error rate by around 40 percent compared to existing/simple methods. We also confirmed that, in our model, the score model plays a more important role than the performance model, and it automatically captures the voice structure by unsupervised learning.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1846–1858},
numpages = {13}
}

@article{10.1109/TASLP.2017.2718843,
author = {Janod, Killian and Morchid, Mohamed and Dufour, Richard and Linares, Georges and De Mori, Renato and Janod, Killian and Morchid, Mohamed and Dufour, Richard and Linares, Georges and De Mori, Renato},
title = {Denoised Bottleneck Features From Deep Autoencoders for Telephone Conversation Analysis},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2718843},
doi = {10.1109/TASLP.2017.2718843},
abstract = {Automatic transcription of spoken documents is affected by automatic transcription errors that are especially frequent when speech is acquired in severe noisy conditions. Automatic speech recognition errors induce errors in the linguistic features used for a variety of natural language processing tasks. Recently, denoisng autoencoders DAE and stacked autoencoders SAE have been proposed with interesting results for acoustic feature denoising tasks. This paper deals with the recovery of corrupted linguistic features in spoken documents. Solutions based on DAEs and SAEs are considered and evaluated in a spoken conversation analysis task. In order to improve conversation theme classification accuracy, the possibility of combining abstractions obtained from manual and automatic transcription features is considered. As a result, two original representations of highly imperfect spoken documents are introduced. They are based on bottleneck features of a supervised autoencoder that takes advantage of both noisy and clean transcriptions to improve the robustness of error prone representations. Experimental results on a spoken conversation theme identification task show substantial accuracy improvements obtained with the proposed recovery of corrupted features.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1809–1820},
numpages = {12}
}

@article{10.1109/TASLP.2017.2702384,
author = {Abeser, Jakob and Schuller, Gerald and Abeser, Jakob and Schuller, Gerald},
title = {Instrument-Centered Music Transcription of Solo Bass Guitar Recordings},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2702384},
doi = {10.1109/TASLP.2017.2702384},
abstract = {This paper deals with the automatic transcription of solo bass guitar recordings with an additional estimation of playing techniques and fretboard positions used by the musician. Our goal is to first develop a system for a robust estimation of the note parameters pitch, onset, and duration score-level parameters. As a second step, we aim to automatically detect the applied plucking and expression style as well as the fret and string positions for each note instrument-level parameters. Our approach is to first apply a note onset detection followed by a tracking of the fundamental frequency contours based on a reassigned magnitude spectrogram. Then, we model the spectral envelope of each note and derive various timbre-related audio features. Using a support vector machine classifier, we automatically classify the instrument-level parameters for each detected note event. Our results show that the proposed system achieves accuracy values above 0.88 for the estimation of the plucking style, expression style, and string number for isolated note samples. As an additional contribution, we analyze the influence of the note duration characteristics in the classification performance. In a score-level evaluation on a novel public dataset of solo bass guitar tracks, our method outperforms three existing transcription algorithms for bass transcription in polyphonic music as well as a melody transcription algorithm for monophonic music.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1741–1750},
numpages = {10}
}

@article{10.1109/TASLP.2017.2716195,
author = {Liu, Lemao and Fujita, Atsushi and Utiyama, Masao and Finch, Andrew and Sumita, Eiichiro and Lemao Liu and Fujita, Atsushi and Utiyama, Masao and Finch, Andrew and Sumita, Eiichiro},
title = {Translation Quality Estimation Using Only Bilingual Corpora},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2716195},
doi = {10.1109/TASLP.2017.2716195},
abstract = {In computer-aided translation scenarios, quality estimation of machine translation hypotheses plays a critical role. Existing methods for word-level translation quality estimation TQE rely on the availability of manually annotated TQE training data obtained via direct annotation or postediting. However, due to the cost of human labor, such data are either limited in size or is only available for few tasks in practice. To avoid the reliance on such annotated TQE data, this paper proposes an approach to train word-level TQE models using bilingual corpora, which are typically used in machine translation training and is relatively easier to access. We formalize the training of our proposed method under the framework of maximum marginal likelihood estimation. To avoid degenerated solutions, we propose a novel regularized training objective whose optimization is achieved by an efficient approximation. Extensive experiments on both written and spoken language datasets empirically show that our approach yields comparable performance to the standard training on annotated data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1762–1772},
numpages = {11}
}

@article{10.1109/TASLP.2017.2716188,
author = {Bernardi, Giuliano and van Waterschoot, Toon and Wouters, Jan and Moonen, Marc and Bernardi, Giuliano and van Waterschoot, Toon and Wouters, Jan and Moonen, Marc},
title = {Adaptive Feedback Cancellation Using a Partitioned-Block Frequency-Domain Kalman Filter Approach With PEM-Based Signal Prewhitening},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2716188},
doi = {10.1109/TASLP.2017.2716188},
abstract = {Adaptive filtering based feedback cancellation is a widespread approach to acoustic feedback control. However, traditional adaptive filtering algorithms have to be modified in order to work satisfactorily in a closed-loop scenario. In particular, the undesired signal correlation between the loudspeaker signal and the source signal in a closed-loop scenario is one of the major problems to address when using adaptive filters for feedback cancellation. Slow convergence speed and limited tracking capabilities are other important limitations to be considered. Additionally, computationally expensive algorithms as well as long delays should be avoided, for instance, in hearing aid applications, because of power constraints, important to extend battery life, and real-time implementations requirements, respectively. We present an algorithm combining good decorrelation properties, by means of the prediction-error method based signal prewhitening, fast convergence, good tracking behavior, and low computational complexity by means of the frequency-domain Kalman filter, and low delay by means of a partitioned-block implementation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1784–1798},
numpages = {15}
}

@article{10.1109/TASLP.2017.2718733,
author = {Stefanakis, Nikolaos and Pavlidi, Despoina and Mouchtaris, Athanasios and Stefanakis, Nikolaos and Pavlidi, Despoina and Mouchtaris, Athanasios},
title = {Perpendicular Cross-Spectra Fusion for Sound Source Localization With a Planar Microphone Array},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2718733},
doi = {10.1109/TASLP.2017.2718733},
abstract = {Multiple sound source localization in reverberant environments stands as one of the most difficult challenges for many applications related to microphone array signal processing. In this paper, we describe perpendicular cross-spectra fusion PCSF, a new direction-of-arrival DOA estimation algorithm, which utilizes an analytic formula for direction estimation in the time-frequency TF domain. Inherent to this technique is the presence of multiple direction estimation subsystems which operate in parallel, producing a multiplicity of candidate DOAs at each TF point. We define a metric of coherence, based on the property of divergence of the different DOA estimators, for assessing the reliability of different signal portions, so that only TF bins with a high quality of directional information are exploited for local DOA estimation. The resulting collection of local DOAs is provided as input to a recently proposed histogram processing approach, which is based on matching pursuit. Results based on simulation and real recordings illustrate the advantages of PCSF compared to other DOA estimation techniques subject to the same histogram-based processing, in the context of real-time multiple source localization and counting; improved performance in reverberant conditions and high tolerance to diffuse and common mode noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1821–1835},
numpages = {15}
}

@article{10.1109/TASLP.2017.2721219,
author = {Yoshimura, Takenori and Hashimoto, Kei and Oura, Keiichiro and Nankaku, Yoshihiko and Tokuda, Keiichi and Yoshimura, Takenori and Hashimoto, Kei and Oura, Keiichiro and Nankaku, Yoshihiko and Tokuda, Keiichi},
title = {Simultaneous Optimization of Multiple Tree-Based Factor Analyzed HMM for Speech Synthesis},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2721219},
doi = {10.1109/TASLP.2017.2721219},
abstract = {This paper proposes a novel method to build multiple decision trees as a structure of factor analyzed hidden Markov model for speech synthesis. In the proposed method, the multiple decision trees grow simultaneously rather than sequentially to take into account the relationship between the trees. However, the simultaneous growing is computationally infeasible due to an exponential increase in the number of tree structures to be evaluated. To solve the problem, we further propose two computational complexity reduction algorithms that achieve a significant reduction in the computational time. Experimental results show that the proposed method outperforms the conventional one based on a single decision tree.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1836–1845},
numpages = {10}
}

@article{10.1109/TASLP.2017.2716178,
author = {Le Cornu, Thomas and Milner, Ben and Le Cornu, Thomas and Milner, Ben},
title = {Generating Intelligible Audio Speech From Visual Speech},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2716178},
doi = {10.1109/TASLP.2017.2716178},
abstract = {This paper is concerned with generating intelligible audio speech from a video of a person talking. Regression and classification methods are proposed first to estimate static spectral envelope features from active appearance model visual features. Two further methods are then developed to incorporate temporal information into the prediction: A feature-level method using multiple frames and a model-level method based on recurrent neural networks. Speech excitation information is not available from the visual signal, so methods to artificially generate aperiodicity and fundamental frequency are developed. These are combined within the STRAIGHT vocoder to produce a speech signal. The various systems are optimized through objective tests before applying subjective intelligibility tests that determine a word accuracy of 85% from a set of human listeners on the GRID audio-visual speech database. This compares favorably with a previous regression-based system that serves as a baseline, which achieved a word accuracy of 33%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = sep,
pages = {1751–1761},
numpages = {11}
}

@article{10.1109/TASLP.2017.2716443,
author = {Grais, Emad M. and Roma, Gerard and Simpson, Andrew J. R. and Plumbley, Mark D. and Grais, Emad M. and Roma, Gerard and Simpson, Andrew J. R. and Plumbley, Mark D.},
title = {Two-Stage Single-Channel Audio Source Separation Using Deep Neural Networks},
year = {2017},
issue_date = {September 2017},
publisher = {IEEE Press},
volume = {25},
number = {9},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2017.2716443},
doi = {10.1109/TASLP.2017.2716443},
abstract = {Most single channel audio source separation approaches produce separated sources accompanied by interference from other sources and other distortions. To tackle this problem, we propose to separate the sources in two stages. In the first stage, the sources are separated from the mixed signal. In the second stage, the interference between the separated sources and the distortions are reduced using deep neural networks DNNs. We propose two methods that use DNNs to improve the quality of the separated sources in the second stage. In the first method, each separated source is improved individually using its own trained DNN, while in the second method all the separated sources are improved together using a single DNN. To further improve the quality of the separated sources, the DNNs in the second stage

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extension: bib