text:
sources. The effectiveness of these applications is proven through simulations as well as real data experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jun,
pages = {1054–1067},
numpages = {14},
keywords = {acoustic measurements, acoustic signal detection, array signal processing}
}

@article{10.1109/TASLP.2015.2409780,
author = {Hendriks, Richard C. and Crespo, Jo\~{a}o B. and Jensen, Jesper and Taal, Cees H.},
title = {Optimal Near-End Speech Intelligibility Improvement Incorporating Additive Noise and Late Reverberation under an Approximation of the Short-Time SII},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409780},
doi = {10.1109/TASLP.2015.2409780},
abstract = {The presence of environmental additive noise in the vicinity of the user typically degrades the speech intelligibility of speech processing applications. This intelligibility loss can be compensated by properly preprocessing the speech signal prior to playout, often referred to as near-end speech enhancement. Although the majority of such algorithms focus primarily on the presence of additive noise, reverberation can also severely degrade intelligibility. In this paper we investigate how late reverberation and additive noise can be jointly taken into account in the near-end speech enhancement process. For this effort we use a recently presented approximation of the speech intelligibility index under a power constraint, which we optimize for speech degraded by both additive noise and late reverberation. The algorithm results in time-frequency dependent amplification factors that depend on both the additive noise power spectral density as well as the late reverberation energy. These amplification factors redistribute speech energy across frequency and perform a dynamic range compression. Experimental results using both instrumental intelligibility measures as well as intelligibility listening tests show that the proposed approach improves speech intelligibility over state-of-the-art reference methods when speech signals are degraded simultaneously by additive noise and reverberation. Speech intelligibility improvements in the order of 20% are observed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {851–862},
numpages = {12},
keywords = {additive noise, late reverberation, speech intelligibility, approximated speech intelligibility index (SII)}
}

@article{10.1109/TASLP.2015.2412466,
author = {Chien, Jen-Tzung},
title = {Laplace Group Sensing for Acoustic Models},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2412466},
doi = {10.1109/TASLP.2015.2412466},
abstract = {This paper presents the group sparse learning for acoustic models where a sequence of acoustic features is driven by Markov chain and each feature vector is represented by groups of basis vectors. The group of common bases represents the features across Markov states within a regression class. The group of individual basis compensates the intra-state residual information. Laplace distribution is used as the sparse prior of sensing weights for group basis representation. Laplace parameter serves as regularization parameter or automatic relevance determination which controls the selection of relevant bases for acoustic modeling. The groups of regularization parameters and basis vectors are estimated from training data by maximizing the marginal likelihood over sensing weights which is implemented by Laplace approximation using the Hessian matrix and the maximum a posteriori parameters. Model uncertainty is compensated through full Bayesian treatment. The connection of Laplace group sensing to lasso regularization is illustrated. Experiments on noisy speech recognition show the robustness of group sparse acoustic models in presence of different noise types and SNRs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {909–922},
numpages = {14},
keywords = {speech recognition, Laplace distribution, acoustic model, basis representation, group sparsity}
}

@article{10.1109/TASLP.2015.2409779,
author = {Mu, Hao and Gan, Woon-Seng and Tan, Ee-Leng},
title = {An Objective Analysis Method for Perceptual Quality of a Virtual Bass System},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409779},
doi = {10.1109/TASLP.2015.2409779},
abstract = {Due to the physical size and frequency response constraints of miniaturized and flat panel loudspeakers, low frequency reproduction from these loudspeakers is generally limited and unsatisfactory. The virtual bass system (VBS) enhances the bass performance of such loudspeakers by tricking the human brain to perceive the fundamental frequency from its higher harmonics. Problematically, the additional harmonics from VBS can also introduce perceptual distortion and reduce the audio quality. Therefore, a reliable method to assess the quality of VBS-enhanced signals is necessary in the designing of VBS. Since subjective experiments are often time-consuming and may be inconsistent, it is desirable to develop an objective assessment method for VBS. Earlier studies only utilized some simple objective metrics, which generally do not consider the human auditory model and are unable to accurately predict the perceptual quality of VBS. In this paper, we introduce a perceptual quality-assessment method for VBS based on the model output variables (MOVs) of the ITU Recommendation ITU-R BS.1387. Suitable combinations of MOVs are selected to derive perceptual quality metrics that correlate closely to the subjective quality. A verification experiment is presented to justify the accuracy of the metrics.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {840–850},
numpages = {11},
keywords = {perceptual distortion, bass enhancement, audio quality evaluation, psychoacoustic signal processing, phase vocoder}
}

@article{10.1109/TASLP.2015.2409774,
author = {Wei, Ying and Wang, Yinfeng},
title = {Design of Low Complexity Adjustable Filter Bank for Personalized Hearing Aid Solutions},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409774},
doi = {10.1109/TASLP.2015.2409774},
abstract = {The emerging demand for personalized hearing aids requires the filter bank of a hearing aid system to be capable of decomposing the sound waves in accordance with the characteristic of the patient's hearing loss. In this paper, an efficient adjustable filter bank is proposed to achieve this goal. By careful design, the number of the subbands as well as the location of the subbands can be easily adjusted by changing a 4-bit control signal. The proposed filter bank has extremely low complexity due to the adoption of fractional interpolation and the technique of symmetric and complementary filters. Only one prototype filter is needed for each of the stages, the multiple passbands generation stage and masking stage. We show, by means of examples, that the proposed filter bank can meet different needs of hearing loss cases with acceptable delay.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {923–931},
numpages = {9},
keywords = {hearing aids, adjustable, fractional interpolation, filter bank}
}

@article{10.1109/TASLP.2015.2412462,
author = {Breebaart, Jeroen},
title = {Evaluation of Statistical Inference Tests Applied to Subjective Audio Quality Data with Small Sample Size},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2412462},
doi = {10.1109/TASLP.2015.2412462},
abstract = {Monte-Carlo simulations of statistical inference tests were performed to assess type 1 (false rejection) and type 2 (false non-rejection) error rates associated with subjective audio quality data as a function of sample size. Samples were generated by randomly drawing data from large-scale subjective audio quality tests. Null hypotheses were simulated by equalizing population means followed by pooling. The Null hypothesis rejection rates were determined for a parametric t test, as well as a non-parametric (permutation) test and compared to rejection rates based on analytical expressions and empirical distributions of the sample means and medians. The results indicated that pairwise comparisons are beneficial for high power and to obtain type I error rates that are close to the nominal value of 5%. The pairwise inferences can be realized by a parametric, pairwise t test or by a non-parametric permutation test, provided that for the latter, only pairwise permutations are executed. Although the observations from this study cannot be generalized for arbitrary data sets, the results do indicate that a pairwise, non-parametric resampling test is an interesting candidate for the statistical analysis of subjective quality data due to the absence of any requirements on data distributions and its relatively accurate performance in terms of Null hypothesis rejection rates.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {887–897},
numpages = {11},
keywords = {statistical analysis, audio compression, audio coding, Monte Carlo methods}
}

@article{10.1109/TASLP.2015.2409737,
author = {Krebs, Florian and Holzapfel, Andre and Cemgil, Ali Taylan and Widmer, Gerhard},
title = {Inferring Metrical Structure in Music Using Particle Filters},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409737},
doi = {10.1109/TASLP.2015.2409737},
abstract = {In this paper, we propose a new state-of-the-art particle filter (PF) system to infer the metrical structure of musical audio signals. The new inference method is designed to overcome the problem of PFs in multi-modal probability distributions, which arise due to tempo and phase ambiguities in musical rhythm representations. We compare the new method with a hidden Markov model (HMM) system and several other PF schemes in terms of performance, speed and scalability on several audio datasets. We demonstrate that using the proposed system the computational complexity can be reduced drastically in comparison to the HMM while maintaining the same order of beat tracking accuracy. Therefore, for the first time, the proposed system allows fast meter inference in a high-dimensional state space, spanned by the three components of tempo, type of rhythm, and position in a metric cycle.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {817–827},
numpages = {11},
keywords = {downbeat tracking, bayesian modeling, particle filters (PFs), beat tracking, approximate inference}
}

@article{10.1109/TASLP.2015.2410140,
author = {Perez-Carrillo, Alfonso and Wanderley, Marcelo M.},
title = {Indirect Acquisition of Violin Instrumental Controls from Audio Signal with Hidden Markov Models},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2410140},
doi = {10.1109/TASLP.2015.2410140},
abstract = {Acquisition of instrumental gestures in musical performances is a field of increasing interest with applications in different areas ranging from acoustics and sound synthesis to motor learning or artistic performances. Direct acquisition approaches are based on measurements with sensors attached on the instrument or the performer, a process that usually involves the use of expensive sensing systems and complex setups that are generally intrusive in practice. An alternative is the indirect acquisition without sensors based on analysis of the audio signal. This paper reports a novel indirect acquisition method for the estimation of continuous violin controls from audio-signal analysis based on the training of statistical models with a database of previously recorded violin performances. The database contains synchronized streams of audio features and instrumental controls. Audio signal was captured with a vibration transducer built into the violin bridge, and instrumental controls were measured with sensors. The controls include bowing parameters (played string, bowing velocity, bowing force, and bowing distance to the bridge) as well as fingering position. Once the model is trained for a specific violin, we can perform indirect acquisition from analysis of the signal captured with its embedded transducer without the need for the sensors any more. The statistical methods used are Hidden Markov Models (HMM) with observation distributions parameterized as Multivariate Gaussian Mixtures (GM). HMMs provide a means for note recognition and following and parameter prediction is based on GM regression. Results show that the presented method constitutes an accurate, non-intrusive and low-cost alternative for instrumental control acquisition of a previously calibrated violin and recording device.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {932–940},
numpages = {9},
keywords = {indirect acquisition, musical gesture, information retrieval, violin instrumental controls}
}

@article{10.1109/TASLP.2015.2410139,
author = {Berkun, Reuven and Cohen, Israel and Benesty, Jacob},
title = {Combined Beamformers for Robust Broadband Regularized Superdirective Beamforming},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2410139},
doi = {10.1109/TASLP.2015.2410139},
abstract = {Superdirective fixed beamformers are known to attain high directivity factors, but are extremely sensitive to uncorrelated noise and slight errors in the array elements, which are modeled by the beamformer white noise gain measure. The delay-and-sum beamformer, on the other hand, manages to maximize the white noise gain, but suffers from a very low directivity factor. In this paper, we discuss the design of a broadband beamformer which controls both the directivity factor and the white noise gain. We combine a regularized version of the superdirective beamformer together with the delay-and-sum beamformer to create a robust regularized superdirective beamformer. We derive analytic closed-form expressions of the beamformer gain responses, and extend them to derive a beamformer with full control of the desired white noise gain or the directivity factor. The proposed approach offers a simple and robust broadband beamformer with controllable characteristics, shown here through persuasive simulation results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {877–886},
numpages = {10},
keywords = {robust superdirective beamformer, delay-and-sum beamformer, superdirective beamformer, microphone arrays, white noise gain, beamforming, directivity factor, supergain}
}

@article{10.1109/TASLP.2015.2412464,
author = {Zivanovic, Miroslav},
title = {Harmonic Bandwidth Companding for Separation of Overlapping Harmonics in Pitched Signals},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2412464},
doi = {10.1109/TASLP.2015.2412464},
abstract = {We present a novel approach to separating pitched signals from single-channel mixtures by harmonic bandwidth companding. Recalling that in a short analysis window the harmonic waveform envelope conveys information about approximate amplitude and frequency time variations, we show that proper linear waveform envelope smoothing reduces frequency spread around the harmonics and thus compresses the harmonic bandwidth. Such an action largely relaxes worst-case separation scenarios, which are caused by large proportions of overlapped harmonics. The distortion in separated signals due to harmonic bandwidth compression is compensated by an inverse system--the harmonic bandwidth expander. The benefit of such an approach is that explicit classification in overlapped and non-overlapped harmonics is no longer needed. Moreover, the underlying signal model is linear-in-parameters which allows for an efficient estimation via linear least-squares. The results show that harmonic bandwidth companding significantly outperforms two state-of-the-art separation methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {898–908},
numpages = {11},
keywords = {harmonic bandwidth, non-stationary signals, companding, signal separation}
}

@article{10.1109/TASLP.2015.2409785,
author = {Abdelaziz, Ahmed Hussen and Zeiler, Steffen and Kolossa, Dorothea},
title = {Learning Dynamic Stream Weights for Coupled-HMM-Based Audio-Visual Speech Recognition},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409785},
doi = {10.1109/TASLP.2015.2409785},
abstract = {With the increasing use of multimedia data in communication technologies, the idea of employing visual information in automatic speech recognition (ASR) has recently gathered momentum. In conjunction with the acoustical information, the visual data enhances the recognition performance and improves the robustness of ASR systems in noisy and reverberant environments. In audio-visual systems, dynamic weighting of audio and video streams according to their instantaneous confidence is essential for reliably and systematically achieving high performance. In this paper, we present a complete framework that allows blind estimation of dynamic stream weights for audio-visual speech recognition based on coupled hidden Markov models (CHMMs). As a stream weight estimator, we consider using multilayer perceptrons and logistic functions to map multidimensional reliability measure features to audiovisual stream weights. Training the parameters of the stream weight estimator requires numerous input-output tuples of reliability measure features and their corresponding stream weights. We estimate these stream weights based on oracle knowledge using an expectation maximization algorithm. We define 31-dimensional feature vectors that combine model-based and signal-based reliability measures as inputs to the stream weight estimator. During decoding, the trained stream weight estimator is used to blindly estimate stream weights. The entire framework is evaluated using the Grid audio-visual corpus and compared to state-of-the-art stream weight estimation strategies. The proposed framework significantly enhances the performance of the audio-visual ASR system in all examined test conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {863–876},
numpages = {14},
keywords = {stream weight, audio-visual speech recognition, logistic regression, reliability measure, coupled hidden Markov model, multilayer perceptron}
}

@article{10.1109/TASLP.2015.2409778,
author = {Cho, Janghoon and Yoo, Chang D.},
title = {Underdetermined Convolutive BSS: Bayes Risk Minimization Based on a Mixture of Super-Gaussian Posterior Approximation},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409778},
doi = {10.1109/TASLP.2015.2409778},
abstract = {This paper considers the underdetermined blind source separation (BSS) of convolutively mixed super-Gaussian signals that include speech, audio, and various other sparse signals. Here, the separation is performed in three steps. In the first and second steps, the mixing matrix and the sources at each time-frequency location are estimated by minimizing the Bayes risk (or the posterior risk) with squared loss. In the final third step, the permutation alignment is conducted by considering the correlation between adjacent spectral bins as in many conventional algorithms. To overcome any computationally intractable integrations involving a complex-valued super-Gaussian source prior, the posterior distribution of the sources is approximated as a mixture of super-Gaussians. The posterior means of the mixing matrix and the sources are obtained with Metropolis-Hastings within Gibbs sampling and the weighted sum of individual super-Gaussians, respectively. Overall, this approximation leads to a separation that is computationally lighter than and as accurate as the algorithm without the approximation. The simulation results of the synthetically generated data in a virtual room with reverberation show that the estimates of the mixing matrix in the first step and the sources in the second step are more accurate than the estimates from the state-of-the-art algorithms in terms of the mixing error ratio (MER) and the signal-to-distortion ratio (SDR). The experiment was also conducted with recorded data in a real room environment using a public benchmark dataset. Results show that the proposed algorithm gives a better performance compared to the state-of-the-art algorithms in terms of the SDR.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {828–839},
numpages = {12},
keywords = {cocktail party problem, underdetermined convolutive mixture, Bayesian estimation, blind source separation (BSS)}
}

@article{10.1109/TASLP.2015.2414818,
author = {Mansikkaniemi, Andr\'{e} and Kurimo, Mikko},
title = {Adaptation of Morph-Based Speech Recognition for Foreign Names and Acronyms},
year = {2015},
issue_date = {May 2015},
publisher = {IEEE Press},
volume = {23},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2414818},
doi = {10.1109/TASLP.2015.2414818},
abstract = {In this paper, we improve morph-based speech recognition system by focusing adaptation efforts on acronyms (ACRs) and foreign proper names (FPNs). An unsupervised language model (LM) adaptation framework based on two-pass decoding is used. Vocabulary adaptation is applied alongside unsupervised LM adaptation. The aim is to improve both language and pronunciation modeling for FPNs and ACRs. A smart selection algorithm is used to find the most likely topically related foreign words and acronyms from in-domain text. New pronunciation rules are generated for the selected words. Different kinds of morpheme adaptation operations are also evaluated on the ACR and FPN candidate words, to ensure optimal results are gained from pronunciation adaptation. Statistically significant improvements in average word error rate (WER), and term error rate (TER), are achieved using a combination of unsupervised LM adaptation with vocabulary adaptation focused on ACRs and FPNs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {941–950},
numpages = {10},
keywords = {foreign word detection, morph-based speech recognition, out-of-vocabulary (OOV) recognition, unsupervised language model (LM) adaptation}
}

@article{10.1109/TASLP.2015.2404035,
author = {Aneeja, G. and Yegnanarayana, B.},
title = {Single Frequency Filtering Approach for Discriminating Speech and Nonspeech},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2404035},
doi = {10.1109/TASLP.2015.2404035},
abstract = {In this paper, a signal processing approach is proposed for speech/nonspeech discrimination. The approach is based on single frequency filtering (SFF), where the amplitude envelope of the signal is obtained at each frequency with high temporal and spectral resolution. This high resolution property helps to exploit the resulting high signal-to-noise ratio (SNR) regions in time and frequency. The variance of the spectral information across frequency is higher for speech and lower for many types of noises. The mean and variance of the noise-compensated weighted envelopes are computed across frequency at each time instant. Decision logic is applied to the feature derived from the mean and variance values on varieties of degradations, including NTIMIT, CTIMIT and distance speech, besides degradation due to standard noise types. In all cases, the proposed method gives significantly better performance than the standard Adaptive Multi-rate VAD2 (AMR2) method. AMR2 method is chosen for comparison, as the method adapts itself for different degradations, and is seen to give good performance over different SNR situations. The proposed method does not use training data to derive the characteristics of speech or noise, nor makes any assumption on the nonspeech beginning. The SFF method appears promising in other applications of speech processing, such as pitch extraction and speech enhancement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {705–717},
numpages = {13},
keywords = {single frequency filtering (SFF), temporal variance, spectral variance, voice activity detection (VAD), speech/nonspeech discrimination, weighted component envelope}
}

@article{10.5555/2876386.2876401,
author = {Huang, Hao and Xu, Haihua and Wang, Xianhui and Silamu, Wushour},
title = {Maximum F1-Score Discriminative Training Criterion for Automatic Mispronunciation Detection},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
abstract = {We carry out an in-depth investigation on a newly proposed Maximum F1-score Criterion (MFC) discriminative training objective function for Goodness of Pronunciation (GOP) based automatic mispronunciation detection that makes use of Gaussian Mixture Model-hidden Markov model (GMM-HMM) as acoustic models. The formulation of MFC seeks to directly optimize F1-score by converting the non-differentiable F1-score function into a continuous objective function to facilitate optimization. We present model-space training algorithm according to MFC using extended Baum-Welch form like update equations based on the weak-sense auxiliary function method. We then present MFC based feature-space discriminative training. We train a matrix projecting from posteriors of Gaussians to a normal size feature space, and add the projected features to traditional spectral features. Mispronunciation detection experiments show MFC based model-space training and feature-space training are effective in improving F1-score and other commonly used evaluation metrics. It is also shown MFC training in both the feature-space and model-space outperforms either model-space training or feature-space training alone, and is about 11.6% better than the maximum likelihood (ML) trained baseline in terms of F1-score. Further, we review and compare mispronunciation detection results with the use of MFC and some traditional training criteria that minimize word error rate in speech recognition. The experimental analysis and comparison provide useful insight into the correlations between F1-score maximization and optimization of these training criteria.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {787–797},
numpages = {11},
keywords = {automatic mispronunciation detection, computer-assisted language learning, discriminative training, feature extraction, F1-score}
}

@article{10.1109/TASLP.2015.2403619,
author = {Kim, Myung Jong and Kim, Younggwan and Kim, Hoirin},
title = {Automatic Intelligibility Assessment of Dysarthric Speech Using Phonologically-Structured Sparse Linear Model},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2403619},
doi = {10.1109/TASLP.2015.2403619},
abstract = {This paper presents a new method for automatically assessing the speech intelligibility of patients with dysarthria, which is a motor speech disorder impeding the physical production of speech. The proposed method consists of two main steps: feature representation and prediction. In the feature representation step, the speech utterance is converted into a phone sequence using an automatic speech recognition technique and is then aligned with a canonical phone sequence from a pronunciation dictionary using a weighted finite state transducer to capture the pronunciation mappings such as match, substitution, and deletion. The histograms of the pronunciation mappings on a pre-defined word set are used for features. Next, in the prediction step, a structured sparse linear model incorporated with phonological knowledge that simultaneously addresses phonologically structured sparse feature selection and intelligibility prediction is proposed. Evaluation of the proposed method on a database of 109 speakers consisting of 94 dysarthric and 15 control speakers yielded a root mean square error of 8.14 compared to subjectively rated scores in the range of 0 to 100. This is a promising performance in which the system can be successfully applied to help speech therapists in diagnosing the degree of speech disorder.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {694–704},
numpages = {11},
keywords = {structured sparse model, Dysarthria, pronunciation confusion network, weighted finite state transducer (WFST), speech intelligibility assessment}
}

@article{10.1109/TASLP.2014.2385478,
author = {Chen, Langzhou and Braunschweiler, Norbert and Gales, Mark J. F.},
title = {Speaker and Expression Factorization for Audiobook Data: Expressiveness and Transplantation},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2385478},
doi = {10.1109/TASLP.2014.2385478},
abstract = {Expressive synthesis from text is a challenging problem. There are two issues. First, read text is often highly expressive to convey the emotion and scenario in the text. Second, since the expressive training speech is not always available for different speakers, it is necessary to develop methods to share the expressive information over speakers. This paper investigates the approach of using very expressive, highly diverse audio book data from multiple speakers to build an expressive speech synthesis system. Both of two problems are addressed by considering a factorized framework where speaker and emotion are modeled in separate sub-spaces of a cluster adaptive training (CAT) parametric speech synthesis system. The sub-spaces for the expressive state of a speaker and the characteristics of the speaker are jointly trained using a set of audiobooks. In this work, the expressive speech synthesis system works in two distinct modes. In the first mode, the expressive information is given by audio data and the adaptation method is used to extract the expressive information in the audio data. In the second mode, the input of the synthesis system is plain text and a full expressive synthesis system is examined where the expressive state is predicted from the text. In both modes, the expressive information is shared and transplanted over different speakers. Experimental results show that in both modes, the expressive speech synthesis method proposed in this work significantly improves the expressiveness of the synthetic speech for different speakers. Finally, this paper also examines whether it is possible to predict the expressive states from text for multiple speakers using a single model, or whether the prediction process needs to be speaker specific.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {605–618},
numpages = {14},
keywords = {expressive speech synthesis, cluster adaptive training, audiobook, neural network, hidden Markov model, factorization}
}

@article{10.1109/TASLP.2015.2405481,
author = {Dov, David and Talmon, Ronen and Cohen, Israel},
title = {Audio-Visual Voice Activity Detection Using Diffusion Maps},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2405481},
doi = {10.1109/TASLP.2015.2405481},
abstract = {The performance of traditional voice activity detectors significantly deteriorates in the presence of highly nonstationary noise and transient interferences. One solution is to incorporate a video signal which is invariant to the acoustic environment. Although several voice activity detectors based on the video signal were recently presented, merely few detectors which are based on both the audio and the video signals exist in the literature to date. In this paper, we present an audio-visual voice activity detector and show that the incorporation of both audio and video signals is highly beneficial for voice activity detection. The algorithm is based on a supervised learning procedure, and a labeled training data set is considered. The algorithm comprises a feature extraction procedure, where the features are designed to separate speech from nonspeech frames. Diffusion maps is applied separately and similarly to the features of each modality and builds a low dimensional representation. Using the new representation, we propose a measure for voice activity which is based on a supervised learning procedure and the variability between adjacent frames in time. The measures of the two modalities are merged to provide voice activity detection based on both the audio and the video signals. Experimental results demonstrate the improved performance of the proposed algorithm compared to state-of-the-art detectors.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {732–745},
numpages = {14},
keywords = {audio-visual speech processing, diffusion maps, voice activity detection}
}

@article{10.5555/2876386.2876391,
author = {Kitamura, Daichi and Saruwatari, Hiroshi and Kameoka, Hirokazu and Takahashi, Yu and Kondo, Kazunobu and Nakamura, Satoshi},
title = {Multichannel Signal Separation Combining Directional Clustering and Nonnegative Matrix Factorization with Spectrogram Restoration},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
abstract = {In this paper, to address problems in multichannel music signal separation, we propose a new hybrid method that combines directional clustering and advanced nonnegative matrix factorization (NMF). The aims of multichannel music signal separation technology is to extract a specific target signal from observed multichannel signals that contain multiple instrumental sounds. In previous studies, various methods using NMF have been proposed, but many problems remain including poor separation accuracy and lack of robustness. To solve these problems, we propose a new supervised NMF (SNMF) with spectrogram restoration and a hybrid method that concatenates the proposed SNMF after directional clustering. Via the extrapolation of supervised spectral bases, the proposed SNMF attempts both target signal separation and reconstruction of the lost target components, which are generated by preceding directional clustering. In addition, we experimentally reveal the trade-off between separation and extrapolation abilities and propose a new scheme for adaptive divergence, where the optimal divergence can be automatically changed in each time frame according to the local spatial conditions. The results of an evaluation experiment show that our proposed hybrid method outperforms the conventional music signal separation methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {654–669},
numpages = {16},
keywords = {music signal processing, multichannel signal separation, nonnegative matrix factorization (NMF), spectrogram restoration}
}

@article{10.1109/TASLP.2015.2409735,
author = {Wang, Chung-Che and Jang, Jyh-Shing Roger},
title = {Improving Query-by-Singing/Humming by Combining Melody and Lyric Information},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2409735},
doi = {10.1109/TASLP.2015.2409735},
abstract = {This paper proposes a novel method for improving query-by-singing/humming systems by using both melody and lyric information. First, singing/humming discrimination is performed to distinguish between singing and humming queries, which is achieved by considering the similarity between acoustic models. For the humming queries, a pitch-only melody recognition method that was ranked first among the MIREX (Music Information Retrieval Evaluation eXchange) query-by-singing/humming task submissions is applied. For the singing queries, a lyric similarity is computed using speech recognition techniques; the computed similarity is subsequently combined with the melody distance to exploit additional information in the lyrics. Several methods for combining melody distance and lyric similarity are investigated. Under the optimal experimental settings, the proposed query-by-singing/humming system achieves 51.19% error rate reduction for the top-10 retrieved results, indicating the feasibility of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {798–806},
numpages = {9},
keywords = {singing voice recognition, combined melody distance and lyric similarity, singing/humming discrimination (SHD), query-by-singing/humming (QBSH)}
}

@article{10.1109/TASLP.2015.2405476,
author = {De Sena, Enzo and Antonello, Niccol\`{o} and Moonen, Marc and Van Waterschoot, Toon},
title = {On the Modeling of Rectangular Geometries in Room Acoustic Simulations},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2405476},
doi = {10.1109/TASLP.2015.2405476},
abstract = {This paper is concerned with an acoustical phenomenon called sweeping echo, which manifests itself in a room impulse response as a distinctive, continuous pitch increase. In this paper, it is shown that sweeping echoes are present (although to greatly varying degrees) in all perfectly rectangular rooms. The theoretical analysis is based on the rigid-wall image solution of the wave equation. Sweeping echoes are found to be caused by the orderly time-alignment of high-order reflections arriving from directions close to the three axial directions. While sweeping echoes have been previously observed in real rooms with a geometry very similar to the rectangular model (e.g., a squash court), they are not perceived in commonly encountered rooms. Room acoustic simulators such as the image method (IM) and finite difference timedomain (FDTD) correctly predict the presence of this phenomenon, which means that rectangular geometries should be used with caution when the objective is to model commonly encountered rooms. Small out-of-square asymmetries in the room geometry are shown to reduce the phenomenon significantly. Randomization of the image sources' position is shown to remove sweeping echoes without the need to model an asymmetrical geometry explicitly. Finally, the performance of three speech and audio processing algorithms is shown to be sensitive to strong sweeping echoes, thus highlighting the need to avoid their occurrence.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {774–786},
numpages = {13},
keywords = {acoustic propagation, image method, room acoustics, acoustic signal processing, computer simulation, acoustics, finite difference methods}
}

@article{10.1109/TASLP.2015.2403613,
author = {Blanco, Eduardo and Moldovan, Dan},
title = {A Semantic Logic-Based Approach to Determine Textual Similarity},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2403613},
doi = {10.1109/TASLP.2015.2403613},
abstract = {This paper presents a semantic logic-based approach to determine textual similarity. Three logic form transformations taking into account semantic structure of sentences are proposed. Logic proofs are obtained using an adapted resolution step that drops predicates when a proof cannot be found with standard resolution. Features are extracted from proofs and combined using supervised machine learning to obtain the final similarity scores. Experimental results show that taking into account semantic relations to determine textual similarity yields performance improvements with respect to both baselines and third-party state-of-the-art systems. Specific sentence pairs that benefit from considering semantic relations are discussed. Detailed results provide empirical evidence that either proof direction offers a strong baseline although considering both is beneficial, and that ignoring concepts that are not an argument of a semantic relation is not sound.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {683–693},
numpages = {11},
keywords = {semantic relations, logic prover, textual similarity}
}

@article{10.1109/TASLP.2015.2401426,
author = {Mai, Van-Khanh and Pastor, Dominique and A\"{\i}ssa-El-Bey, Abdeldjalil and Le-Bidan, Rapha\"{e}l},
title = {Robust Estimation of Non-Stationary Noise Power Spectrum for Speech Enhancement},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2401426},
doi = {10.1109/TASLP.2015.2401426},
abstract = {We propose a novel method for noise power spectrum estimation in speech enhancement. This method called extended-DATE (E-DATE) extends the -dimensional amplitude trimmed estimator (DATE), originally introduced for additive white gaussian noise power spectrum estimation in "Robust estimation of noise standard deviation in presence of signals with unknown distributions and occurrences" (D. Pastor and F. Socheleau, IEEE Trans. Signal Processing, vol. 60, no. 4, pp. 1545-1555, Apr. 2012) to the more challenging scenario of non-stationary noise. The key idea is that, in each frequency bin and within a sufficiently short time period, the noise instantaneous power spectrum can be considered as approximately constant and estimated as the variance of a complex gaussian noise process possibly observed in the presence of the signal of interest. The proposed method relies on the fact that the Short-Time Fourier Transform (STFT) of noisy speech signals is sparse in the sense that transformed speech signals can be represented by a relatively small number of coefficients with large amplitudes in the time-frequency domain. The E-DATE estimator is robust in that it does not require prior information about the signal probability distribution except for the weak-sparseness property. In comparison to other state-of-the-art methods, the E-DATE is found to require the smallest number of parameters (only two). The performance of the proposed estimator has been evaluated in combination with noise reduction and compared to alternative methods. This evaluation involves objective as well as pseudo-subjective criteria.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {670–682},
numpages = {13},
keywords = {speech enhancement, robust statistics, noise power spectrum estimation, noise reduction}
}

@article{10.1109/TASLP.2015.2396681,
author = {Hu, Ying and Liu, Guizhong},
title = {Separation of Singing Voice Using Nonnegative Matrix Partial Co-Factorization for Singer Identification},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2396681},
doi = {10.1109/TASLP.2015.2396681},
abstract = {In order to improve the performance of singer identification, we propose a system to separate singing voice from music accompaniment for monaural recordings. Our system consists of two key stages. The first stage exploits the nonnegative matrix partial co-factorization (NMPCF), which is a joint matrix decomposition integrating prior knowledge of singing voice and pure accompaniment to separate the mixture signal into singing voice portion and accompaniment portion. In the second stage, based on the separated singing voice obtained by the first stage, the pitches of singing voice are first estimated and then the harmonic components of singing voice can be distinguished. For a frame, the distinguished harmonic components are regarded as reliable while other frequency components unreliable, thus the spectrum is incomplete. With those harmonic components, the complete spectrums of singing voice can be reconstructed by a missing feature method, spectrum reconstruction, obtaining a refined signal with more clean singing voice. Experimental results demonstrate that, from the point view of source separation, the singing voice refinement can further improve ΔSNR in contrast with the singing voice separation using NMPCF, while for the point view of singer identification, the singing voice separated by NMPCF is more appropriate than the refined singing voice.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {643–653},
numpages = {11},
keywords = {singer identification, spectrum reconstruction, nonnegative matrix partial co-factorization (NMPCF), singing voice separation}
}

@article{10.1109/TASLP.2015.2405475,
author = {Deleforge, Antoine and Horaud, Radu and Schechner, Yoav Y. and Girin, Laurent},
title = {Co-Localization of Audio Sources in Images Using Binaural Features and Locally-Linear Regression},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2405475},
doi = {10.1109/TASLP.2015.2405475},
abstract = {This paper addresses the problem of localizing audio sources using binaural measurements. We propose a supervised formulation that simultaneously localizes multiple sources at different locations. The approach is intrinsically efficient because, contrary to prior work, it relies neither on source separation, nor on monaural segregation. The method starts with a training stage that establishes a locally linear Gaussian regression model between the directional coordinates of all the sources and the auditory features extracted from binaural measurements. While fixed-length wide-spectrum sounds (white noise) are used for training to reliably estimate the model parameters, we show that the testing (localization) can be extended to variable-length sparse-spectrum sounds (such as speech), thus enabling a wide range of realistic applications. Indeed, we demonstrate that the method can be used for audio-visual fusion, namely to map speech signals onto images and hence to spatially align the audio and visual modalities, thus enabling to discriminate between speaking and non-speaking faces. We release a novel corpus of real-room recordings that allow quantitative evaluation of the co-localization method in the presence of one or two sound sources. Experiments demonstrate increased accuracy and speed relative to several state-of-the-art methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {718–731},
numpages = {14},
keywords = {mixture model, binaural hearing, regression, audio-visual fusion, sound-source localization, supervised learning}
}

@article{10.5555/2876386.2876388,
author = {Zhou, Xinjie and Wan, Xiaojun and Xiao, Jianguo},
title = {CLOpinionMiner: Opinion Target Extraction in a Cross-Language Scenario},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
abstract = {Opinion target extraction is a subtask of opinion mining which is very useful in many applications. The problem has usually been solved by training a sequence labeler on manually labeled data. However, the labeled training datasets are imbalanced in different languages, and the lack of labeled corpus in a language limits the research progress on opinion target extraction in this language. In order to address the above problem, we propose a novel system called CLOpinionMiner which investigates leveraging the rich labeled data in a source language for opinion target extraction in a different target language. In this study, we focus on English-to-Chinese cross-language opinion target extraction. Based on the English dataset, our method produces two Chinese training datasets with different features. Two labeling models for Chinese opinion target extraction are trained based on Conditional Random Fields (CRF). After that, we use a monolingual co-training algorithm to improve the performance of both models by leveraging the enormous unlabeled Chinese review texts on the web. Experimental results show the effectiveness of our proposed approach.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {619–630},
numpages = {12},
keywords = {cross-language information extraction, opinion mining, opinion target extraction}
}

@article{10.1109/TASLP.2015.2401513,
author = {Mamun, Nursadul and Jassim, Wissam A. and Zilany, Muhammad S. A.},
title = {Prediction of Speech Intelligibility Using a Neurogram Orthogonal Polynomial Measure (NOPM)},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2401513},
doi = {10.1109/TASLP.2015.2401513},
abstract = {Sensorineural hearing loss (SNHL) is an increasingly prevalent condition, resulting from damage to the inner ear and causing a reduction in speech intelligibility. This paper proposes a new speech intelligibility prediction metric, the neurogram orthogonal polynomial measure (NOPM). This metric applies orthogonal moments to the auditory neurogram to predict speech intelligibility for listeners with and without hearing loss. The model simulates the responses of auditory-nerve fibers to speech signals under quiet and noisy conditions. Neurograms were created using a physiologically based computational model of the auditory periphery. A well-known orthogonal polynomial measure, Krawtchouk moments, was applied to extract features from the auditory neurogram. The predicted intelligibility scores were compared to subjective results, and NOPM showed a good fit with the subjective scores for normal listeners and also for listeners with hearing loss. The proposed metric has a realistic and wider dynamic range than corresponding existing metrics, such as mean structural similarity index measure and neurogram similarity index measure, and the predicted scores are also well-separated as a function of hearing loss. The application of this metric could be extended for assessing hearing-aid and speech-enhancement algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {760–773},
numpages = {14},
keywords = {orthogonal moment, neurogram, auditory-nerve model, speech intelligibility, sensorineural hearing loss}
}

@article{10.1109/TASLP.2015.2392944,
author = {Zhou, Pan and Jiang, Hui and Dai, Li-Rong and Hu, Yu and Liu, Qing-Feng},
title = {State-Clustering Based Multiple Deep Neural Networks Modeling Approach for Speech Recognition},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2392944},
doi = {10.1109/TASLP.2015.2392944},
abstract = {The hybrid deep neural network (DNN) and hidden Markov model (HMM) has recently achieved dramatic performance gains in automatic speech recognition (ASR). The DNN-based acoustic model is very powerful but its learning process is extremely time-consuming. In this paper, we propose a novel DNN-based acoustic modeling framework for speech recognition, where the posterior probabilities of HMM states are computed from multiple DNNs (mDNN), instead of a single large DNN, for the purpose of parallel training towards faster turnaround. In the proposed mDNN method all tied HMM states are first grouped into several disjoint clusters based on data-driven methods. Next, several hierarchically structured DNNs are trained separately in parallel for these clusters using multiple computing units (e.g. GPUs). In decoding, the posterior probabilities of HMM states can be calculated by combining outputs from multiple DNNs. In this work, we have shown that the training procedure of the mDNN under popular criteria, including both frame-level cross-entropy and sequence-level discriminative training, can be parallelized efficiently to yield significant speedup. The training speedup is mainly attributed to the fact that multiple DNNs are parallelized over multiple GPUs and each DNN is smaller in size and trained by only a subset of training data. We have evaluated the proposed mDNN method on a 64-hour Mandarin transcription task and the 320-hour Switchboard task. Compared to the conventional DNN, a 4-cluster mDNN model with similar size can yield comparable recognition performance in Switchboard (only about 2% performance degradation) with a greater than 7 times speed improvement in CE training and a 2.9 times improvement in sequence training, when 4 GPUs are used.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {631–642},
numpages = {12},
keywords = {data partition, parallel training, model parallelism, cross entropy training, speech recognition, multiple DNNs (mDNN), deep neural networks (DNN), sequence training, state clustering}
}

@article{10.1109/TASLP.2015.2405482,
author = {Habibi, Maryam and Popescu-Belis, Andrei},
title = {Keyword Extraction and Clustering for Document Recommendation in Conversations},
year = {2015},
issue_date = {April 2015},
publisher = {IEEE Press},
volume = {23},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2405482},
doi = {10.1109/TASLP.2015.2405482},
abstract = {This paper addresses the problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words, which are potentially related to several topics; moreover, using an automatic speech recognition (ASR) system introduces errors among them. Therefore, it is difficult to infer precisely the information needs of the conversation participants. We first propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward function which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise. Then, we propose a method to derive multiple topically separated queries from this keyword set, in order to maximize the chances of making at least one relevant recommendation when using these queries to search over the English Wikipedia. The proposed methods are evaluated in terms of relevance with respect to conversation fragments from the Fisher, AMI, and ELEA conversational corpora, rated by several human judges. The scores show that our proposal improves over previous methods that consider only word frequency or topic similarity, and represents a promising solution for a document recommender system to be used in conversations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {746–759},
numpages = {14},
keywords = {document recommendation, meeting analysis, keyword extraction, topic modeling, information retrieval}
}

@article{10.1109/TASLP.2015.2405131,
author = {Li, Haizhou and Federico, Marcello and He, Xiaodong and Meng, Helen and Trancoso, Isabel},
title = {Introduction to the Special Section on Continuous Space and Related Methods in Natural Language Processing},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2405131},
doi = {10.1109/TASLP.2015.2405131},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {427–430},
numpages = {4}
}

@article{10.1109/TASLP.2015.2389034,
author = {Zeng, Xiaodong and Wong, Derek F. and Chao, Lidia S. and Trancoso, Isabel},
title = {Graph-Based Lexicon Regularization for PCFG with Latent Annotations},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2389034},
doi = {10.1109/TASLP.2015.2389034},
abstract = {This paper aims at learning a better probabilistic context-free grammar with latent annotations (PCFG-LA) by using a graph propagation (GP) technique. We propose leveraging the GP to regularize the lexical model of the grammar. The proposed approach constructs k-nearest neighbor (k-NN) similarity graphs over words with identical pre-terminal (part-of-speech) tags, for propagating the probabilities of latent annotations given the words. The graphs demonstrate the relationship between words in syntactic and semantic levels, estimated by using a neural word representation method based on Recursive autoencoder (RAE). We modify the conventional PCFG-LA parameter estimation algorithm, expectation maximization (EM), by incorporating a GP process subsequent to the M-step. The GP encourages the smoothness among the graph vertices, where different words under similar syntactic and semantic environments should have approximate posterior distributions of nonterminal subcategories. The proposed PCFG-LA learning approach was evaluated together with a hierarchical split-and-merge training strategy, on parsing tasks for English, Chinese and Portuguese. The empirical results reveal two crucial findings: 1) regularizing the lexicons with GP results in positive effects to parsing accuracy; and 2) learning with unlabeled data can also expand the PCFG-LA lexicons.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {441–450},
numpages = {10},
keywords = {graph propagation, natural language processing, syntax parsing, neural word representation}
}

@article{10.1109/TASLP.2014.2365359,
author = {Chen, Wenliang and Zhang, Min and Zhang, Yue},
title = {Distributed Feature Representations for Dependency Parsing},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2365359},
doi = {10.1109/TASLP.2014.2365359},
abstract = {This paper presents an approach to automatically learning distributed representations for features to address the feature sparseness problem for dependency parsing. Borrowing terminologies from word embeddings, we call the feature representation feature embeddings. In our approach, the feature embeddings are inferred from large amounts of auto-parsed data. First, the sentences in raw data are parsed by a baseline system and we obtain dependency trees. Then, we represent each model feature using the surrounding features on the dependency trees. Based on the representation of surrounding context, we proposed two learning methods to infer feature embeddings. Finally, based on feature embeddings, we present a set of new features for graph-based dependency parsing models. The new parsers can not only make full use of well-established hand-designed features but also benefit from the hidden-class representations of features. Experiments on the standard Chinese and English data sets show that the new parser achieves significant performance improvements over a strong baseline.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {451–460},
numpages = {10},
keywords = {natural language processing, feature embeddings, semi-supervised approach, dependency parsing}
}

@article{10.5555/2817174.2817183,
author = {Rashwan, Mohsen A. A. and Al Sallab, Ahmad A. and Raafat, Hazem M. and Rafea, Ahmed},
title = {Deep Learning Framework with Confused Sub-Set Resolution Architecture for Automatic Arabic Diacritization},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
abstract = {The Arabic language belongs to a group of languages that require diacritization over their characters. Modern Standard Arabic (MSA) transcripts omit the diacritics, which are essential for many machine learning tasks like Text-To-Speech (TTS) systems. In this work Arabic diacritics restoration is tackled under a deep learning framework that includes the Confused Sub-set Resolution (CSR) method to improve the classification accuracy, in addition to an Arabic Part-of-Speech (PoS) tagging framework using deep neural nets. Special focus is given to syntactic diacritization, which still suffers low accuracy as indicated in prior works. Evaluation is done versus state-of-the-art systems reported in literature, with quite challenging datasets collected from different domains. Standard datasets like the LDC Arabic Tree Bank are used in addition to custom ones we have made available online to allow other researchers to replicate these results. Results show significant improvement of the proposed techniques over other approaches, reducing the syntactic classification error to 9.9% and morphological classification error to 3% compared to 12.7% and 3.8% of the best reported results in literature, improving the error by 22% over the best reported systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {505–516},
numpages = {12},
keywords = {deep networks, arabic diacritization, part-of-speech (PoS) tagging, classifier design}
}

@article{10.5555/2817174.2817185,
author = {Mesnil, Gr\'{e}goire and Dauphin, Yann and Yao, Kaisheng and Bengio, Yoshua and Deng, Li and Hakkani-Tur, Dilek and He, Xiaodong and Heck, Larry and Tur, Gokhan and Yu, Dong and Zweig, Geoffrey},
title = {Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
abstract = {Semantic slot filling is one of the most challenging problems in spoken language understanding (SLU). In this paper, we propose to use recurrent neural networks (RNNs) for this task, and present several novel architectures designed to efficiently model past and future temporal dependencies. Specifically, we implemented and compared several important RNN architectures, including Elman, Jordan, and hybrid variants. To facilitate reproducibility, we implemented these networks with the publicly available Theano neural network toolkit and completed experiments on the well-known airline travel information system (ATIS) benchmark. In addition, we compared the approaches on two custom SLU data sets from the entertainment and movies domains. Our results show that the RNN-based models outperform the conditional random field (CRF) baseline by 2% in absolute error reduction on the ATIS benchmark. We improve the state-of-the-art by 0.5% in the Entertainment domain, and 6.7% for the movies domain.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {530–539},
numpages = {10},
keywords = {word embedding, recurrent neural network (RNN), slot filling, spoken language understanding (SLU)}
}

@article{10.1109/TASLP.2015.2389622,
author = {Adel, Heike and Vu, Ngoc Thang and Kirchhoff, Katrin and Telaar, Dominic and Schultz, Tanja},
title = {Syntactic and Semantic Features for Code-Switching Factored Language Models},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2389622},
doi = {10.1109/TASLP.2015.2389622},
abstract = {This paper presents our latest investigations on different features for factored language models for Code-Switching speech and their effect on automatic speech recognition (ASR) performance. We focus on syntactic and semantic features which can be extracted from Code-Switching text data and integrate them into factored language models. Different possible factors, such as words, part-of-speech tags, Brown word clusters, open class words and clusters of open class word embeddings are explored. The experimental results reveal that Brown word clusters, part-of-speech tags and open-class words are the most effective at reducing the perplexity of factored language models on the Mandarin-English Code-Switching corpus SEAME. In ASR experiments, the model containing Brown word clusters and part-of-speech tags and the model also including clusters of open class word embeddings yield the best mixed error rate results. In summary, the best language model can significantly reduce the perplexity on the SEAME evaluation set by up to 10.8% relative and the mixed error rate by up to 3.4% relative.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {431–440},
numpages = {10},
keywords = {recurrent neural networks, automatic speech recognition (ASR), natural language processing}
}

@article{10.1109/TASLP.2014.2379593,
author = {Hutchinson, Brian and Ostendorf, Mari and Fazel, Maryam},
title = {A Sparse plus Low-Rank Exponential Language Model for Limited Resource Scenarios},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2379593},
doi = {10.1109/TASLP.2014.2379593},
abstract = {This paper describes a new exponential language model that decomposes the model parameters into one or more low-rank matrices that learn regularities in the training data and one or more sparse matrices that learn exceptions (e.g., keywords). The low-rank matrices induce continuous-space representations of words and histories. The sparse matrices learn multiword lexical items and topic/domain idiosyncrasies. This model generalizes the standard l1-regularized exponential language model, and has an efficient accelerated first-order training algorithm. Language modeling experiments show that the approach is useful in scenarios with limited training data, including low resource languages and domain adaptation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {494–504},
numpages = {11},
keywords = {sparse, language model, exponential, low-hyphen, log bilinear}
}

@article{10.1109/TASLP.2014.2379589,
author = {Nakashika, Toru and Takiguchi, Tetsuya and Ariki, Yasuo},
title = {Voice Conversion Using RNN Pre-Trained by Recurrent Temporal Restricted Boltzmann Machines},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2379589},
doi = {10.1109/TASLP.2014.2379589},
abstract = {This paper presents a voice conversion (VC) method that utilizes the recently proposed probabilistic models called recurrent temporal restricted Boltzmann machines (RTRBMs). One RTRBM is used for each speaker, with the goal of capturing high-order temporal dependencies in an acoustic sequence. Our algorithm starts from the separate training of one RTRBM for a source speaker and another for a target speaker using speaker-dependent training data. Because each RTRBM attempts to discover abstractions to maximally express the training data at each time step, as well as the temporal dependencies in the training data, we expect that the models represent the linguistic-related latent features in high-order spaces. In our approach, we convert (match) features of emphasis for the source speaker to those of the target speaker using a neural network (NN), so that the entire network (consisting of the two RTRBMs and the NN) acts as a deep recurrent NN and can be fine-tuned. Using VC experiments, we confirm the high performance of our method, especially in terms of objective criteria, relative to conventional VC methods such as approaches based on Gaussian mixture models and on NNs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {580–587},
numpages = {8},
keywords = {speaker specific features, voice conversion, deep Learning, recurrent neural network, recurrent temporal restricted Boltzmann machine (RTRBM)}
}

@article{10.1109/TASLP.2014.2387389,
author = {Obin, Nicolas and Lanchantin, Pierre},
title = {Symbolic Modeling of Prosody: From Linguistics to Statistics},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387389},
doi = {10.1109/TASLP.2014.2387389},
abstract = {The assignment of prosodic events (accent and phrasing) from the text is crucial in text-to-speech synthesis systems. This paper addresses the combination of linguistic and metric constraints for the assignment of prosodic events in text-to-speech synthesis. First, a linguistic processing chain is used to provide a rich linguistic description of a text. Then, a novel statistical representation based on a hierarchical HMM (HHMM) is used to model the prosodic structure of a text: the root layer represents the text, each intermediate layer a sequence of intermediate phrases, the pre-terminal layer the sequence of accents, and the terminal layer the sequence of linguistic contexts. For each intermediate layer, a segmental HMM and information fusion are used to fuse the linguistic and metric constraints for the segmentation of a text into phrases. A set of experiments conducted on multi-speaker databases with various speaking styles reports that: the rich linguistic representation improves drastically the assignment of prosodic events, and the fusion of linguistic and metric constraints significantly improves over standard methods for the segmentation of a text into phrases. These constitute substantial advances that can be further used to model the speech prosody of a speaker, a speaking style, and emotions for text-to-speech synthesis.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {588–599},
numpages = {12},
keywords = {prosodic events, hierarchical HMMs, segmental HMMs, speaking style, speech prosody, surface/deep syntactic parsing, Dempster-Shafer fusion, text-to-speech synthesis}
}

@article{10.1109/TASLP.2015.2395254,
author = {Xiong, Deyi and Zhang, Min and Wang, Xing},
title = {Topic-Based Coherence Modeling for Statistical Machine Translation},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2395254},
doi = {10.1109/TASLP.2015.2395254},
abstract = {Coherence that ties sentences of a text into a meaningfully connected structure is of great importance to text generation and translation. In this paper, we propose topic-based coherence models to produce coherence for document translation, in terms of the continuity of sentence topics in a text. We automatically extract a coherence chain for each source text to be translated. Based on the extracted source coherence chain, we adopt a maximum entropy classifier to predict the target coherence chain that defines a linear topic structure for the target document. We build two topic-based coherence models on the predicted target coherence chain: 1) a word level coherence model that helps the decoder select coherent word translations and 2) a phrase level coherence model that guides the decoder to select coherent phrase translations. We integrate the two models into a state-of-the-art phrase-based machine translation system. Experiments on large-scale training data show that our coherence models achieve substantial improvements over both the baseline and models that are built on either document topics or sentence topics obtained under the assumption of direct topic correspondence between the source and target side. Additionally, further evaluations on translation outputs suggest that target translations generated by our coherence models are more coherent and similar to reference translations than those generated by the baseline.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {483–493},
numpages = {11},
keywords = {coherence chain, topic modeling, text coherence, text analysis, natural, statistical machine translation (SMT), language processing}
}

@article{10.1109/TASLP.2015.2389618,
author = {McLoughlin, Ian and Zhang, Haomin and Xie, Zhipeng and Song, Yan and Xiao, Wei},
title = {Robust Sound Event Classification Using Deep Neural Networks},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2389618},
doi = {10.1109/TASLP.2015.2389618},
abstract = {The automatic recognition of sound events by computers is an important aspect of emerging applications such as automated surveillance, machine hearing and auditory scene understanding. Recent advances in machine learning, as well as in computational models of the human auditory system, have contributed to advances in this increasingly popular research field. Robust sound event classification, the ability to recognise sounds under real-world noisy conditions, is an especially challenging task. Classification methods translated from the speech recognition domain, using features such as mel-frequency cepstral coefficients, have been shown to perform reasonably well for the sound event classification task, although spectrogram-based or auditory image analysis techniques reportedly achieve superior performance in noise. This paper outlines a sound event classification framework that compares auditory image front end features with spectrogram image-based front end features, using support vector machine and deep neural network classifiers. Performance is evaluated on a standard robust classification task in different levels of corrupting noise, and with several system enhancements, and shown to compare very well with current state-of-the-art classification techniques.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {540–552},
numpages = {13},
keywords = {auditory event detection, machine hearing}
}

@article{10.5555/2817174.2876374,
author = {Fu, Ruiji and Guo, Jiang and Qin, Bing and Che, Wanxiang and Wang, Haifeng and Liu, Ting},
title = {Learning Semantic Hierarchies: A Continuous Vector Space Approach},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
abstract = {Semantic hierarchy construction aims to build structures of concepts linked by hypernym-hyponym ("is-a") relations. A major challenge for this task is the automatic discovery of such relations. This paper proposes a novel and effective method for the construction of semantic hierarchies based on continuous vector representation of words, named word embeddings, which can be used to measure the semantic relationship between words. We identify whether a candidate word pair has hypernym-hyponym relation by using the word-embedding-based semantic projections between words and their hypernyms. Our result, an F-score of 73.74%, outperforms the state-of-the-art methods on a manually labeled test dataset. Moreover, combining our method with a previous manually built hierarchy extension method can further improve F-score to 80.29%.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {461–471},
numpages = {11},
keywords = {piecewise linear projections, word embedding, semantic hierarchy}
}

@article{10.1109/TASLP.2014.2387413,
author = {Wang, Yow-Bang and Lee, Lin-shan},
title = {Supervised Detection and Unsupervised Discovery of Pronunciation Error Patterns for Computer-Assisted Language Learning},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387413},
doi = {10.1109/TASLP.2014.2387413},
abstract = {Pronunciation error patterns (EPs) are patterns of mispronunciation frequently produced by language learners, and are usually different for different pairs of target and native languages. Accurate information of EPs can offer helpful feedbacks to the learners to improve their language skills. However, the major difficulty of EP detection comes from the fact that EPs are intrinsically similar to their corresponding canonical pronunciation, and different EPs corresponding to same canonical pronunciation are also intrinsically similar to each other. As a result, distinguishing EPs from their corresponding canonical pronunciation and between different EPs of the same phoneme is a difficult task-perhaps even more difficult than distinguishing between different phonemes in one language. On the other hand, the cost of deriving all EPs for each pair of target and native languages is high, usually requiring extensive expert knowledge or high-quality annotated data. Unsupervised EP discovery from a corpus of learner recordings would thus be an attractive addition to the field. In this paper, we propose new frameworks for both supervised EP detection and unsupervised EP discovery. For supervised EP detection, we use hierarchical multi-layer perceptrons (MLPs) as the EP classifiers to be integrated with the baseline using HMM/GMM in a two-pass Viterbi decoding architecture. Experimental results show that the new framework enhances the power of EP diagnosis. For unsupervised EP discovery we propose the first known framework, using the hierarchical agglomerative clustering (HAC) algorithm to explore sub-segmental variation within phoneme segments and produce fixed-length segment-level feature vectors in order to distinguish different EPs. We tested K-means (assuming a known number of EPs) and the Gaussian mixture model with the minimum description length principle (estimating an unknown number of EPs) for EP discovery. Preliminary experiments offered very encouraging results, although there is still a long way to go to approach the performance of human experts. We also propose to use the universal phoneme posteriorgram (UPP), derived from an MLP trained on corpora of mixed languages, as frame-level features in both supervised detection and unsupervised discovery of EPs. Experimental results show that using UPP not only achieves the best performance, but also is useful in analyzing the mispronunciation produced by language learners.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {564–579},
numpages = {16},
keywords = {error pattern detection, universal phoneme posteriorgram, computer-assisted language learning, error pattern discovery, computer-aided pronunciation training}
}

@article{10.1109/TASLP.2015.2400218,
author = {Sundermeyer, Martin and Ney, Hermann and Schl\"{u}ter, Ralf},
title = {From Feedforward to Recurrent LSTM Neural Networks for Language Modeling},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2400218},
doi = {10.1109/TASLP.2015.2400218},
abstract = {Language models have traditionally been estimated based on relative frequencies, using count statistics that can be extracted from huge amounts of text data. More recently, it has been found that neural networks are particularly powerful at estimating probability distributions over word sequences, giving substantial improvements over state-of-the-art count models. However, the performance of neural network language models strongly depends on their architectural structure. This paper compares count models to feedforward, recurrent, and long short-term memory (LSTM) neural network variants on two large-vocabulary speech recognition tasks. We evaluate the models in terms of perplexity and word error rate, experimentally validating the strong correlation of the two quantities, which we find to hold regardless of the underlying type of the language model. Furthermore, neural networks incur an increased computational complexity compared to count models, and they differently model context dependences, often exceeding the number of words that are taken into account by count based approaches. These differences require efficient search methods for neural networks, and we analyze the potential improvements that can be obtained when applying advanced algorithms to the rescoring of word lattices on large-scale setups.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {517–529},
numpages = {13},
keywords = {recurrent neural network (RNN), language modeling, Kneser-Ney smoothing, feedforward neural network, long short-term memory (LSTM)}
}

@article{10.1109/TASLP.2015.2405751,
author = {Banchs, Rafael E. and D'Haro, Luis F. and Li, Haizhou},
title = {Adequacy-Fluency Metrics: Evaluating MT in the Continuous Space Model Framework},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2405751},
doi = {10.1109/TASLP.2015.2405751},
abstract = {This work extends and evaluates a two-dimensional automatic evaluation metric for machine translation, which is designed to operate at the sentence level. The metric is based on the concepts of adequacy and fluency, aiming at decoupling both semantic and syntactic components of the translation process to provide a more balanced view on translation quality. These two elements are independently evaluated by using continuous space and n-gram language modeling frameworks, respectively. Two different implementations are evaluated: a monolingual version that fully operates on the target language side, and a cross-language version that has the main advantage of not requiring reference translations. Both implementations are evaluated by comparing their performance with state-of-the-art automatic metrics over a dataset involving five different European languages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {472–482},
numpages = {11},
keywords = {machine translation, system evaluation, natural language processing}
}

@article{10.1109/TASLP.2015.2393393,
author = {Zahoransky, Du\v{s}an and Polasek, Ivan},
title = {Text Search of Surnames in Some Slavic and Other Morphologically Rich Languages Using Rule Based Phonetic Algorithms},
year = {2015},
issue_date = {March 2015},
publisher = {IEEE Press},
volume = {23},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2393393},
doi = {10.1109/TASLP.2015.2393393},
abstract = {Surnames play a key role as person natural identifiers, essentially in present information systems. This paper deals with the topic of optimizing a phonetic search algorithm as a string matching of surnames usable for communications service providers, person registries, social networks or genealogy databases. It describes a proposed solution for the phonetic searching of Slovak and (territorial) neighboring languages (Czech, Polish, Ukrainian, Russian, German, Hungarian, Jewish) surnames. This solution was designed to improve search precision and recall when searching for people by their surnames originating in these languages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {553–563},
numpages = {11},
keywords = {algorithms, natural languages, information retrieval}
}

@article{10.1109/TASLP.2014.2387385,
author = {Hua, Guang and Goh, Jonathan and Thing, Vrizlynn L. L.},
title = {Time-Spread Echo-Based Audio Watermarking with Optimized Imperceptibility and Robustness},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387385},
doi = {10.1109/TASLP.2014.2387385},
abstract = {We present a time-spread echo-based audio watermarking scheme with optimized imperceptibility and robustness. Specifically, convex optimization based finite-impulse-response (FIR) filter design is utilized to obtain the optimal echo filter coefficients. The desired power spectrum of the echo filter is shaped by the proposed maximum power spectral margin (MPSM) and the absolute threshold of hearing (ATH) of human auditory system (HAS) to ensure the optimal imperceptibility. Meanwhile, the auto-correlation function of the echo filter coefficients is specified as the constraint in the problem formulation, which controls the robustness in terms of watermark detection. In this way, a joint optimization of imperceptibility and robustness can be quantitatively performed. As a result, the proposed watermarking scheme is superior to existing solutions such as the ones based on pseudo noise (PN) sequence or modified pseudo noise (MPN) sequence. Note that the designed echo kernel is also highly secure in that only with the same filter coefficients can one successfully detect the watermark. Experimental results are provided to evaluate the imperceptibility and robustness of the proposed watermarking scheme.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {227–239},
numpages = {13},
keywords = {time-spread echo, FIR filter design, convex optimization, audio watermarking}
}

@article{10.1109/TASLP.2014.2381881,
author = {Zhang, Mengqiu and Kennedy, Rodney A. and Abhayapala, Thushara D.},
title = {Empirical Determination of Frequency Representation in Spherical Harmonics-Based HRTF Functional Modeling},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2381881},
doi = {10.1109/TASLP.2014.2381881},
abstract = {This paper considers a functional modeling of a head-related transfer function (HRTF) where the spatial-portion is constrained to be expanded using spherical harmonics and the frequency-portion is expanded in terms of standard closed-form orthonormal functions, which may be user selected. We derive an objective evaluation metric to compare the relative energy efficiencies of candidate functional models using empirical HRTF database measurements and robust estimation techniques. Among four sets of closed-form orthonormal functions the complex exponentials are identified as the most efficient to represent the frequency-portion in the spherical harmonics-based HRTF functional model. The proposed model is evaluated across three HRTF data sets: 1) CIPIC database, 2) the MIT KEMAR (Knowles Electronics Mannequin for Acoustics Research) database, and 3) the ANU KEMAR database.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {351–360},
numpages = {10},
keywords = {functional modeling, dimensionality, series expansion theory, head-related transfer function (HRTF)}
}

@article{10.1109/TASLP.2014.2387414,
author = {Hirayama, Naoki and Yoshino, Koichiro and Itoyama, Katsutoshi and Mori, Shinsuke and Okuno, Hiroshi G.},
title = {Automatic Speech Recognition for Mixed Dialect Utterances by Mixing Dialect Language Models},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387414},
doi = {10.1109/TASLP.2014.2387414},
abstract = {This paper presents an automatic speech recognition (ASR) system that accepts a mixture of various kinds of dialects. The system recognizes dialect utterances on the basis of the statistical simulation of vocabulary transformation and combinations of several dialect models. Previous dialect ASR systems were based on handcrafted dictionaries for several dialects, which involved costly processes. The proposed system statistically trains transformation rules between a common language and dialects, and simulates a dialect corpus for ASR on the basis of a machine translation technique. The rules are trained with small sets of parallel corpora to make up for the lack of linguistic resources on dialects. The proposed system also accepts mixed dialect utterances that contain a variety of vocabularies. In fact, spoken language is not a single dialect but a mixed dialect that is affected by the circumstances of speakers' backgrounds (e.g., native dialects of their parents or where they live). We addressed two methods to combine several dialects appropriately for each speaker. The first was recognition with language models of mixed dialects with automatically estimated weights that maximized the recognition likelihood. This method performed the best, but calculation was very expensive because it conducted grid searches of combinations of dialect mixing proportions that maximized the recognition likelihood. The second was integration of results of recognition from each single dialect language model. The improvements with this model were slightly smaller than those with the first method. Its calculation cost was, however, inexpensive and it worked in real-time on general workstations. Both methods achieved higher recognition accuracies for all speakers than those with the single dialect models and the common language model, and we could choose a suitable model for use in ASR that took into consideration the computational costs and recognition accuracies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {373–382},
numpages = {10},
keywords = {mixture of dialects, corpus simulation, speech recognition}
}

@article{10.1109/TASLP.2014.2331102,
author = {Molina, Emilio and Tard\'{o}n, Lorenzo J. and Barbancho, Ana M. and Barbancho, Isabel},
title = {SiPTH: Singing Transcription Based on Hysteresis Defined on the Pitch-Time Curve},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2331102},
doi = {10.1109/TASLP.2014.2331102},
abstract = {In this paper, we present a method for monophonic singing transcription based on hysteresis defined on the pitch-time curve. This method is designed to perform note segmentation even when the pitch evolution during the same note behaves unstably, as in the case of untrained singers. The selected approach estimates the regions in which the chroma is stable, these regions are classified as voiced or unvoiced according to a decision tree classifier using two descriptors based on aperiodicity and power. Then, a note segmentation stage based on pitch intervals of the sung signal is carried out. To this end, a dynamic averaging of the pitch curve is performed after the beginning of a note is detected in order to roughly estimate the pitch. Deviations of the actual pitch curve with respect to this average are measured to determine the next note change according to a hysteresis process defined on the pitchtime curve. Finally, each note is labeled using three single values: rounded pitch (to semitones), duration and volume. Also, a complete evaluation methodology that includes the definition of different relevant types of errors, measures and a method for the computation of the evaluation measures are presented. The proposed system improves significantly the performance of the baseline approach, and attains results similar to previous approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {252–263},
numpages = {12},
keywords = {acoustic signal processing, singing voice analysis, singing transcription, pitch, fundamental frequency}
}

@article{10.1109/TASLP.2014.2372342,
author = {Schwartz, Boaz and Gannot, Sharon and Habets, Emanu\"{e}l A. P.},
title = {Online Speech Dereverberation Using Kalman Filter and EM Algorithm},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2372342},
doi = {10.1109/TASLP.2014.2372342},
abstract = {Speech signals recorded in a room are commonly degraded by reverberation. In most cases, both the speech signal and the acoustic system of the room are unknown and time-varying. In this paper, a scenario with a single desired sound source and slowly time-varying and spatially-white noise is considered, and a multi-microphone algorithm that simultaneously estimates the clean speech signal and the time-varying acoustic system is proposed. The recursive expectation-maximization scheme is employed to obtain both the clean speech signal and the acoustic system in an online manner. In the expectation step, the Kalman filter is applied to extract a new sample of the clean signal, and in the maximization step, the system estimate is updated according to the output of the Kalman filter. Experimental results show that the proposed method is able to significantly reduce reverberation and increase the speech quality. Moreover, the tracking ability of the algorithm was validated in practical scenarios using human speakers moving in a natural manner.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {394–406},
numpages = {13},
keywords = {dereverberation, recursive parameter estimation, convolution in STFT, recursive expectation-maximization}
}

@article{10.1109/TASLP.2014.2381882,
author = {Bi\c{c}ici, Ergun and Yuret, Deniz},
title = {Optimizing Instance Selection for Statistical Machine Translation with Feature Decay Algorithms},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2381882},
doi = {10.1109/TASLP.2014.2381882},
abstract = {We introduce FDA5 for efficient parameterization, optimization, and implementation of feature decay algorithms (FDA), a class of instance selection algorithms that use feature decay. FDA increase the diversity of the selected training set by devaluing features (i.e., n-grams) that have already been included. FDA5 decides which instances to select based on three functions used for initializing and decaying feature values and scaling sentence scores controlled with five parameters. We present optimization techniques that allow FDA5 to adapt these functions to in-domain and out-of-domain translation tasks for different language pairs. In a transductive learning setting, selection of training instances relevant to the test set can improve the final translation quality. In machine translation experiments performed on the 2 million sentence English-German section of the Europarl corpus, we show that a subset of the training set selected by FDA5 can gain up to 3.22 BLEU points compared to a randomly selected subset of the same size, can gain up to 0.41 BLEU points compared to using all of the available training data using only 15% of it, and can reach within 0.5 BLEU points to the full training set result by using only 2.7% of the full training data. FDA5 peaks at around 8M words or 15% of the full training set. In an active learning setting, FDA5 minimizes the human effort by identifying the most informative sentences for translation and FDA gains up to 0.45 BLEU points using 3/5 of the available training data compared to using all of it and 1.12 BLEU points compared to random training set. In translation tasks involving English and Turkish, a morphologically rich language, FDA5 can gain up to 11.52 BLEU points compared to a randomly selected subset of the same size, can achieve the same BLEU score using as little as 4% of the data compared to random instance selection, and can exceed the full dataset result by 0.78 BLEU points. FDA5 is able to reduce the time to build a statistical machine translation system to about half with 1M words using only 3% of the space for the phrase table and 8% of the overall space when compared with a baseline system using all of the training data available yet still obtain only 0.58 BLEU points difference with the baseline system in out-of-domain translation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {339–350},
numpages = {12},
keywords = {transductive learning, information retrieval, domain adaptation, machine translation, instance selection}
}

@article{10.1109/TASLP.2014.2372335,
author = {Schwartz, Ofer and Gannot, Sharon and Habets, Emanu\"{e}l A. P.},
title = {Multi-Microphone Speech Dereverberation and Noise Reduction Using Relative Early Transfer Functions},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2372335},
doi = {10.1109/TASLP.2014.2372335},
abstract = {In speech communication systems, the microphone signals are degraded by reverberation and ambient noise. The reverberant speech can be separated into two components, namely, an early speech component that includes the direct path and some early reflections, and a late reverberant component that includes all the late reflections. In this paper, a novel algorithm to simultaneously suppress early reflections, late reverberation and ambient noise is presented. A multi-microphone minimum mean square error estimator is used to obtain a spatially filtered vaersion of the early speech component. The estimator constructed as a minimum variance distortionless response (MVDR) beam-former (BF) followed by a postfilter (PF). Three unique design features characterize the proposed method. First, the MVDR BF is implemented in a special structure, named the nonorthogonal generalized sidelobe canceller (NO-GSC). Compared with the more conventional orthogonal GSC structure, the new structure allows for a simpler implementation of the GSC blocks for various MVDR constraints. Second, In contrast to earlier works, RETFs are used in the MVDR criterion rather than either the entire RTFs or only the direct-path of the desired speech signal. An estimator of the RETFs is proposed as well. Third, the late reverberation and noise are processed by both the beamforming stage and the PF stage. Since the relative power of the noise and the late reverberation varies with the frame index, a computationally efficient method for the required matrix inversion is proposed to circumvent the cumbersome mathematical operation. The algorithm was evaluated and compared with two alternative multichannel algorithms and one single-channel algorithm using simulated data and data recorded in a room with a reverberation time of 0.5 s for various source-microphone array distances (1-4 m) and several signal-to-noise levels. The processed signals were tested using two commonly used objective measures, namely perceptual evaluation of speech quality and log-spectral distance. As an additional objective measure, the improvement in word accuracy percentage of an acoustic speech recognition system is also demonstrated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {240–251},
numpages = {12},
keywords = {relative transfer function, generalized sidelobe canceller, dereverberation, multichannel Wiener filter, minimum variance distortionless response (MVDR) beamforming}
}

@article{10.1109/TASLP.2014.2363589,
author = {Matsuyama, Yoichi and Saito, Akihiro and Fujie, Shinya and Kobayashi, Tetsunori},
title = {Automatic Expressive Opinion Sentence Generation for Enjoyable Conversational Systems},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2363589},
doi = {10.1109/TASLP.2014.2363589},
abstract = {In terms of functional conversations, Grice's Maxim of Quantity suggests that responses should contain no more information than was explicitly asked for. However, in our daily conversations, more informative response skills are usually employed in order to hold enjoyable conversations with interlocutors. These responses are usually produced as forms of one's additional opinions, which usually contain their original viewpoints as well as novel means of expression, rather than simple and common responses characteristic of the general public. In this paper, we propose automatic expressive opinion sentence generation mechanisms for enjoyable conversational systems. The generated opinions are extracted from a large number of reviews on the web, and ranked in terms of contextual relevance, length of sentences, and amount of information represented by the frequency of adjectives. The sentence generator also has an additional phrasing skill. Three controlled lab experiments were conducted, where subjects were requested to read generated sentences and watch videos filmed about conversations between the robot and a person. The results implied that mechanisms effectively promote users' enjoyment and interests.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {313–326},
numpages = {14},
keywords = {natural sentence generation, opinion generation, conversational robots, question answering}
}

@article{10.1109/TASLP.2014.2381871,
author = {Feng, Zu-Ren and Zhou, Qing and Zhang, Jun and Jiang, Ping and Yang, Xue-Wen},
title = {A Target Guided Subband Filter for Acoustic Event Detection in Noisy Environments Using Wavelet Packets},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2381871},
doi = {10.1109/TASLP.2014.2381871},
abstract = {This paper deals with acoustic event detection (AED), such as screams, gunshots, and explosions, in noisy environments. The main aim is to improve the detection performance under adverse conditions with a very low signal-to-noise ratio (SNR). A novel filtering method combined with an energy detector is presented. The wavelet packet transform (WPT) is first used for time-frequency representation of the acoustic signals. The proposed filter in the wavelet packet domain then uses a priori knowledge of the target event and an estimate of noise features to selectively suppress the background noise. It is in fact a content-aware band-pass filter which can automatically pass the frequency bands that are more significant in the target than in the noise. Theoretical analysis shows that the proposed filtering method is capable of enhancing the target content while suppressing the background noise for signals with a low SNR. A condition to increase the probability of correct detection is also obtained. Experiments have been carried out on a large dataset of acoustic events that are contaminated by different types of environmental noise and white noise with varying SNRs. Results show that the proposed method is more robust and better adapted to noise than ordinary energy detectors, and it can work even with an SNR as low as 15 dB. A practical system for real time processing and multi-target detection is also proposed in this work.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {361–372},
numpages = {12},
keywords = {filter, background noise, wavelet packets, acoustic event detection (AED)}
}

@article{10.1109/TASLP.2014.2384279,
author = {Sugiura, Ryosuke and Kamamoto, Yutaka and Harada, Noboru and Kameoka, Hirokazu and Moriya, Takehiro},
title = {Resolution Warped Spectral Representation for Low-Delay and Low-Bit-Rate Audio Coder},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2384279},
doi = {10.1109/TASLP.2014.2384279},
abstract = {We have devised a high-quality frequency-domain audio coder based on the state-of-the-art monaural wide-band coder aiming at its use in low-delay and low-bit-rate conditions. The coder efficiently represents frequency spectral envelopes of the target signals with low computational complexity using optimally prepared non-negative sparse matrices. The experimental results reveal that this representation has positive effects on the objective and subjective quality of the coder resulting in the comparable quality to the same bit rate of 3GPP Extended Adaptive Multi-Rate WideBand (AMR-WB+), a coder which permits more than four times longer delay compared with the proposed coder. Consequently, this coder is suitable for applications in mobile communications, which require low delay and low complexity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {288–299},
numpages = {12},
keywords = {line spectrum pairs, frequency warping, low delay, audio compression, transform coding excitation, linear predictive coding, non-negative matrix}
}

@article{10.1109/TASLP.2014.2365992,
author = {Schasse, Alexander and Gerkmann, Timo and Martin, Rainer and S\"{o}rgel, Wolfgang and Pilgrim, Thomas and Puder, Henning},
title = {Two-Stage Filter-Bank System for Improved Single-Channel Noise Reduction in Hearing Aids},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2365992},
doi = {10.1109/TASLP.2014.2365992},
abstract = {The filter-bank system implemented in hearing aids has to fulfill various constraints such as low latency and high stop-band attenuation, usually at the cost of low frequency resolution. In the context of frequency-domain noise-reduction algorithms, insufficient frequency resolution may lead to annoying residual noise artifacts since the spectral harmonics of the speech cannot properly be resolved. Especially in case of female speech signals, the noise between the spectral harmonics causes a distinct roughness of the processed signals. Therefore, this work proposes a two-stage filter-bank system, such that the frequency resolution can be improved for the purpose of noise reduction, while the original first-stage hearing-aid filter-bank system can still be used for compression and amplification. We also propose methods to implement the second filter-bank stage with little additional algorithmic delay. Furthermore, the computational complexity is an important design criterion. This finally leads to an application of the second filter-bank stage to lower frequency bands only, resulting in the ability to resolve the harmonics of speech. The paper presents a systematic description of the second filter-bank stage, discusses its influence on the processed signals in detail and further presents the results of a listening test which indicates the improved performance compared to the original single-stage filter-bank system.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {383–393},
numpages = {11},
keywords = {single-channel noise reduction, low delay processing, cascaded filter-bank system, hearing aids}
}

@article{10.1109/TASLP.2014.2381931,
author = {Weng, Chao and Juang, Biing-Hwang},
title = {Discriminative Training Using Non-Uniform Criteria for Keyword Spotting on Spontaneous Speech},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2381931},
doi = {10.1109/TASLP.2014.2381931},
abstract = {In this work, we formulate the problem of keyword spotting as a non-uniform error automatic speech recognition (ASR) problem and propose a model training methodology based on the non-uniform minimum classification error (MCE) approach. The main idea is to adapt the fundamental MCE criteria to reflect the cost-sensitive notion in that errors on keywords are much more significant than errors on non-keywords in an automatic speech recognition task. The notion of cost sensitivity leads to emphasis of keyword models in parameter optimization. Then we present a system which takes advantage of the weighted finite-state transducer (WFST) framework to efficiently implement the non-uniform MCE. To enhance the approach of non-uniform error cost minimization for keyword spotting, we further formulate a technique called "adaptive boosted non-uniform MCE" which incorporates the idea of boosting. We validate the proposed framework on two challenging large-scale spontaneous conversational telephone speech (CTS) datasets in two different languages (English and Mandarin). Experimental results show our framework can achieve consistent and significant spotting performance gains over both the maximum likelihood estimation (MLE) baseline and conventional discriminatively-trained systems with uniform error cost.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {300–312},
numpages = {13},
keywords = {minimum classification error (MCE), discriminative training (DT), non-uniform criteria, weighted finite-state transducer (WFST), keyword spotting}
}

@article{10.1109/TASLP.2014.2387388,
author = {Arora, Vipul and Behera, Laxmidhar},
title = {Multiple F0 Estimation and Source Clustering of Polyphonic Music Audio Using PLCA and HMRFs},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387388},
doi = {10.1109/TASLP.2014.2387388},
abstract = {Source transcription of pitched polyphonic music entails providing the pitch (F0) values corresponding to each source in a separate channel. This problem is an important step towards many important problems in music and speech processing. It involves 1) estimating the multiple F0 values in each short time frame, and 2) clustering the F0 values into streams corresponding to different sources. We address the problem in an unsupervised way, with only the total number of sources given beforehand. The framework of probabilistic latent component analysis (PLCA) is used to decompose the polyphonic short-time magnitude spectra for multiple F0 estimation and source-specific feature extraction. It is further embedded into the structure of hidden Markov random fields (HMRF) for clustering the F0s into different sources. This clustering is constrained by the cognitive grouping of continuous F0 contours as well as segregation of simultaneous F0s into different source streams. Such constraints are effectively and elegantly modeled by the HMRF's. Simulated annealing varies the degree of constraints for better clustering. The paper also proposes a novel strategy using the trade-off between precision and recall of multiple F0 estimation for better clustering. Evaluations over a variety of datasets show the efficacy of the proposed algorithm and its robustness to the presence of spurious F0s while clustering. It also outperforms a state-of-the-art unsupervised source streaming algorithm in a set of comparative experiments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {278–287},
numpages = {10},
keywords = {hidden Markov random fields, multiple F0 estimation, acoustic scene analysis, automatic music transcription, polyphonic instrument identification}
}

@article{10.1109/TASLP.2014.2387382,
author = {Wang, Haipeng and Lee, Tan and Leung, Cheung-Chi and Ma, Bin and Li, Haizhou},
title = {Acoustic Segment Modeling with Spectral Clustering Methods},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2387382},
doi = {10.1109/TASLP.2014.2387382},
abstract = {This paper presents a study of spectral clustering-based approaches to acoustic segment modeling (ASM). ASM aims at finding the underlying phoneme-like speech units and building the corresponding acoustic models in the unsupervised setting, where no prior linguistic knowledge and manual transcriptions are available. A typical ASM process involves three stages, namely initial segmentation, segment labeling, and iterative modeling. This work focuses on the improvement of segment labeling. Specifically, we use posterior features as the segment representations, and apply spectral clustering algorithms on the posterior representations. We propose a Gaussian component clustering (GCC) approach and a segment clustering (SC) approach. GCC applies spectral clustering on a set of Gaussian components, and SC applies spectral clustering on a large number of speech segments. Moreover, to exploit the complementary information of different posterior representations, a multiview segment clustering (MSC) approach is proposed. MSC simultaneously utilizes multiple posterior representations to cluster speech segments. To address the computational problem of spectral clustering in dealing with large numbers of speech segments, we use inner product similarity graph and make reformulations to avoid the explicit computation of the affinity matrix and Laplacian matrix. We carried out two sets of experiments for evaluation. First, we evaluated the ASM accuracy on the OGI-MTS dataset, and it was shown that our approach could yield 18.7% relative purity improvement and 15.1% relative NMI improvement compared with the baseline approach. Second, we examined the performances of our approaches in the real application of zero-resource query-by-example spoken term detection on SWS2012 dataset, and it was shown that our approaches could provide consistent improvement on four different testing scenarios with three evaluation metrics.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {264–277},
numpages = {14},
keywords = {zero-resource query-by-example spoken term detection, unsupervised training, acoustic segment modeling, sub-word unit discovery, multiview segment clustering}
}

@article{10.1109/TASLP.2014.2384271,
author = {Petkov, Petko N. and Kleijn, W. Bastiaan},
title = {Spectral Dynamics Recovery for Enhanced Speech Intelligibility in Noise},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2384271},
doi = {10.1109/TASLP.2014.2384271},
abstract = {Speech intelligibility in noisy environments decreases with an increase in the noise power. We hypothesize that the differences of subsequent short-term spectra of speech, which we collectively refer to as the speech spectral dynamics, can be used to characterize speech intelligibility. We propose a distortion measure to characterize the deviation of the dynamics of the noisy modified speech from the dynamics of natural speech. Optimizing this distortion measure, we derive a parametric relationship between the signal band-power before and after modification. The parametric nature of the solution ensures adaptation to the noise level, the speech statistics and a penalty on the power gain. A multi-band speech modification system based on the single-band optimal solution is designed under a total signal power constraint and evaluated in selected noise conditions. The results indicate that the proposed approach compares favorably to a reference method based on optimizing a measure of the speech intelligibility index. Very low computational complexity and high intelligibility gain make this an attractive approach for speech modification in a wide range of application scenarios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {327–338},
numpages = {12},
keywords = {environment adaptation, speech intelligibility enhancement, speech modification}
}

@article{10.1109/TASLP.2014.2384274,
author = {Gerazov, Branislav and Ivanovski, Zoran},
title = {Kernel Power Flow Orientation Coefficients for Noise-Robust Speech Recognition},
year = {2015},
issue_date = {February 2015},
publisher = {IEEE Press},
volume = {23},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2384274},
doi = {10.1109/TASLP.2014.2384274},
abstract = {Noise-robustness has become a crucial parameter in Automatic Speech Recognition (ASR) systems today with their increased use in noise-filled real-world environments. One way to address this issue is to develop features that are innately noise-robust. The Kernel Power flow Orientation Coefficients (KPOCs) are a novel feature set based on spectro-temporal analysis that uses a bank of 2D kernels to extract the dominant orientation of the power flow at each point in the auditory spectrogram of the speech signal. The collection of dominant power flow orientation angles forms a novel representation of the speech signal named the Power flow Orientation Spectrogram (POS), which is innately resistant to the spectral masking introduced by the presence of noise and reverberation. This approach not only grants KPOC its noise robustness, but also keeps the number of output coefficients inherently small, thus eliminating the need of the feature dimensionality reduction otherwise necessary in the conventional the spectro-temporal approach. KPOCs performance has been evaluated on three experimental frameworks, and the results have shown that they outperform a number of well-known noise-robust features for average and low SNRs. The relative improvement in Word Recognition Accuracy (WRA) to the classic Mel Frequency Cepstral Coefficients (MFCCs) for the Aurora 2 task goes from 32% up to 190% for SNRs in the range from 10 down to 5 dB. The experimental results also show that in clean training the performance of KPOC approaches that of the state-of-the-art noise-robust ASR frontends in all noise scenarios for small vocabulary ASR tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {407–419},
numpages = {13},
keywords = {speech recognition, spectro-temporal, 2D kernels, features, noise-robust}
}

@article{10.1109/TASLP.2014.2375558,
author = {Zhang, Zixing and Coutinho, Eduardo and Deng, Jun and Schuller, Bj\"{o}rn},
title = {Cooperative Learning and Its Application to Emotion Recognition from Speech},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2375558},
doi = {10.1109/TASLP.2014.2375558},
abstract = {In this paper, we propose a novel method for highly efficient exploitation of unlabeled data--Cooperative Learning. Our approach consists of combining Active Learning and Semi-Supervised Learning techniques, with the aim of reducing the costly effects of human annotation. The core underlying idea of Cooperative Learning is to share the labeling work between human and machine efficiently in such a way that instances predicted with insufficient confidence value are subject to human labeling, and those with high confidence values are machine labeled. We conducted various test runs on two emotion recognition tasks with a variable number of initial supervised training instances and two different feature sets. The results show that Cooperative Learning consistently outperforms individual Active and Semi-Supervised Learning techniques in all test cases. In particular, we show that our method based on the combination of Active Learning and Co-Training leads to the same performance of a model trained on the whole training set, but using 75% fewer labeled instances. Therefore, our method efficiently and robustly reduces the need for human annotations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {115–126},
numpages = {12},
keywords = {cooperative learning, supervised learning, multi-view learning, acoustics, semi-supervised learning, active learning, emotion recognition}
}

@article{10.1109/TASLP.2014.2367822,
author = {Poignant, Johann and Besacier, Laurent and Qu\'{e}not, Georges},
title = {Unsupervised Speaker Identification in TV Broadcast Based on Written Names},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2367822},
doi = {10.1109/TASLP.2014.2367822},
abstract = {Identifying speakers in TV broadcast in an unsupervised way (i.e., without biometric models) is a solution for avoiding costly annotations. Existing methods usually use pronounced names, as a source of names, for identifying speech clusters provided by a diarization step but this source is too imprecise for having sufficient confidence. To overcome this issue, another source of names can be used: the names written in a title block in the image track. We first compared these two sources of names on their abilities to provide the name of the speakers in TV broadcast. This study shows that it is more interesting to use written names for their high precision for identifying the current speaker. We also propose two approaches for finding speaker identity based only on names written in the image track. With the "late naming" approach, we propose different propagations of written names onto clusters. Our second proposition, "Early naming," modifies the speaker diarization module (agglomerative clustering) by adding constraints preventing two clusters with different associated written names to be merged together. These methods were tested on the REPERE corpus phase 1, containing 3 hours of annotated videos. Our best "late naming" system reaches an F-measure of 73.1%. "early naming" improves over this result both in terms of identification error rate and of stability of the clustering stopping criterion. By comparison, a mono-modal, supervised speaker identification system with 535 speaker models trained on matching development data and additional TV and radio data only provided a 57.2% F-measure.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {57–68},
numpages = {12},
keywords = {TV broadcast, speaker diarization, written names, multimodal fusion, speaker identification}
}

@article{10.1109/TASLP.2015.2390431,
author = {Li, Haizhou},
title = {Inaugural Editorial: Embracing New Opportunities for Growth},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2390431},
doi = {10.1109/TASLP.2015.2390431},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {5–6},
numpages = {2}
}

@article{10.1109/TASLP.2014.2367817,
author = {Wu, Yuntao and Amir, Leshem and Jensen, Jesper Rindom and Liao, Guisheng},
title = {Joint Pitch and DOA Estimation Using the ESPRIT Method},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2367817},
doi = {10.1109/TASLP.2014.2367817},
abstract = {In this paper, the problem of joint multi-pitch and direction-of-arrival (DOA) estimation for multichannel harmonic sinusoidal signals is considered. A spatio-temporal matrix signal model for a uniform linear array is defined, and then the ESPRIT method based on subspace techniques that exploits the invariance property in the time domain is first used to estimate the multi pitch frequencies of multiple harmonic signals. Followed by the estimated pitch frequencies, the DOA estimations based on the ESPRIT method are also presented by using the shift invariance structure in the spatial domain. Compared to the existing state-of-the-art algorithms, the proposed method based on ESPRIT without 2-D searching is computationally more efficient but performs similarly. An asymptotic performance analysis of the DOA and pitch estimation of the proposed method are also presented. Finally, the effectiveness of the proposed method is illustrated on a synthetic signal as well as real-life recorded data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {32–45},
numpages = {14},
keywords = {ESPRIT, pitch estimation, DOA estimation, performance analysis}
}

@article{10.1109/TASLP.2014.2377583,
author = {Jensen, Jesper Rindom and Christensen, Mads Gr\ae{}sb\o{}l and Benesty, Jacob and Jensen, S\o{}ren Holdt},
title = {Joint Spatio-Temporal Filtering Methods for DOA and Fundamental Frequency Estimation},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2377583},
doi = {10.1109/TASLP.2014.2377583},
abstract = {In this paper, spatio-temporal filtering methods are proposed for estimating the direction-of-arrival (DOA) and fundamental frequency of periodic signals, like those produced by the speech production system and many musical instruments using microphone arrays. This topic has quite recently received some attention in the community and is quite promising for several applications. The proposed methods are based on optimal, adaptive filters that leave the desired signal, having a certain DOA and fundamental frequency, undistorted and suppress everything else. The filtering methods simultaneously operate in space and time, whereby it is possible resolve cases that are otherwise problematic for pitch estimators or DOA estimators based on beamforming. Several special cases and improvements are considered, including a method for estimating the covariance matrix based on the recently proposed iterative adaptive approach (IAA). Experiments demonstrate the improved performance of the proposed methods under adverse conditions compared to the state of the art using both synthetic signals and real signals, as well as illustrate the properties of the methods and the filters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {174–185},
numpages = {12},
keywords = {fundamental frequency estimation, LCMV beamformer, DOA estimation, periodogram-based beamformer, joint estimation, 2-D filtering}
}

@article{10.1109/TASLP.2014.2364452,
author = {Xu, Yong and Du, Jun and Dai, Li-Rong and Lee, Chin-Hui},
title = {A Regression Approach to Speech Enhancement Based on Deep Neural Networks},
year = {2015},
issue_date = {January 2015},
publisher = {IEEE Press},
volume = {23},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2364452},
doi = {10.1109/TASLP.2014.2364452},
abstract = {In contrast to the conventional minimum mean square error (MMSE)-based noise reduction techniques, we propose a supervised method to enhance speech by means of finding a mapping function between noisy and clean speech signals based on deep neural networks (DNNs). In order to be able to handle a wide range of additive noises in real-world situations, a large training set that encompasses many possible combinations of speech and noise types, is first designed. A DNN architecture is then employed as a nonlinear regression function to ensure a powerful modeling capability. Several techniques have also been proposed to improve the DNN-based speech enhancement system, including global variance equalization to alleviate the over-smoothing problem of the regression model, and the dropout and noise-aware training strategies to further improve the generalization capability of DNNs to unseen noise conditions. Experimental results demonstrate that the proposed framework can achieve significant improvements in both objective and subjective measures over the

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