text:
= {Automatic note-level transcription is considered one of the most challenging tasks in music information retrieval. The specific case of flamenco singing transcription poses a particular challenge due to its complex melodic progressions, intonation inaccuracies, the use of a high degree of ornamentation, and the presence of guitar accompaniment. In this study, we explore the limitations of existing state of the art transcription systems for the case of flamenco singing and propose a specific solution for this genre: We first extract the predominant melody and apply a novel contour filtering process to eliminate segments of the pitch contour which originate from the guitar accompaniment. We formulate a set of onset detection functions based on volume and pitch characteristics to segment the resulting vocal pitch contour into discrete note events. A quantised pitch label is assigned to each note event by combining global pitch class probabilities with local pitch contour statistics. The proposed system outperforms state of the art singing transcription systems with respect to voicing accuracy, onset detection, and overall performance when evaluated on flamenco singing datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {901–913},
numpages = {13},
keywords = {automatic music transcription, singing voice, music information retrieval, pitch contour, audio content description}
}

@article{10.1109/TASLP.2016.2533858,
author = {Sigtia, Siddharth and Benetos, Emmanouil and Dixon, Simon},
title = {An End-to-End Neural Network for Polyphonic Piano Music Transcription},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2533858},
doi = {10.1109/TASLP.2016.2533858},
abstract = {We present a supervised neural network model for polyphonic piano music transcription. The architecture of the proposed model is analogous to speech recognition systems and comprises an acoustic model and a music language model. The acoustic model is a neural network used for estimating the probabilities of pitches in a frame of audio. The language model is a recurrent neural network that models the correlations between pitch combinations over time. The proposed model is general and can be used to transcribe polyphonic music without imposing any constraints on the polyphony. The acoustic and language model predictions are combined using a probabilistic graphical model. Inference over the output variables is performed using the beam search algorithm. We perform two sets of experiments. We investigate various neural network architectures for the acoustic models and also investigate the effect of combining acoustic and music language model predictions using the proposed architecture. We compare performance of the neural network-based acoustic models with two popular unsupervised acoustic models. Results show that convolutional neural network acoustic models yield the best performance across all evaluation metrics. We also observe improved performance with the application of the music language models. Finally, we present an efficient variant of beam search that improves performance and reduces run-times by an order of magnitude, making the model suitable for real-time applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {927–939},
numpages = {13},
keywords = {recurrent neural networks, deep learning, automatic music transcription, music language models}
}

@article{10.1109/TASLP.2016.2533867,
author = {Krawczyk-Becker, Martin and Gerkmann, Timo},
title = {Fundamental Frequency Informed Speech Enhancement in a Flexible Statistical Framework},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2533867},
doi = {10.1109/TASLP.2016.2533867},
abstract = {Conventional statistical clean speech estimators, like the Wiener filter, are frequently used for the spectro-temporal enhancement of noise corrupted speech. Most of these approaches estimate the clean speech independently for each time-frequency point, neglecting the structure of the underlying speech sound. In this work, we derive a statistical estimator that explicitly takes into account information about the characteristic structure of voiced speech by means of a harmonic signal model. To this end, we also present a way to estimate a harmonic model-based clean speech representation and the corresponding error variance directly in the short-time Fourier transform domain. The resulting estimator is optimal in the minimum-mean-squared error sense and can conveniently be formulated in terms of a multichannel Wiener filter. The proposed estimator outperforms several reference algorithms in terms of speech quality and intelligibility as predicted by instrumental measures.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {940–951},
numpages = {12},
keywords = {speech enhancement, signal reconstruction, noise reduction}
}

@article{10.1109/TASLP.2016.2535199,
author = {Szurley, Joseph and Bertrand, Alexander and Van Dijk, Bas and Moonen, Marc},
title = {Binaural Noise Cue Preservation in a Binaural Noise Reduction System with a Remote Microphone Signal},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2535199},
doi = {10.1109/TASLP.2016.2535199},
abstract = {A general binaural noise reduction system is considered that employs the multichannel Wiener filter with partial noise estimation (MWFη) allowing for an explicit tradeoff between noise reduction and binaural noise cue preservation. In this paper, it is assumed that along with the general binaural system, a remote microphone signal with a high input signal-to-noise ratio (SNR) is available for inclusion in the MWFη. The use of this remote microphone signal with a high input SNR allows for a simultaneous increase in both noise reduction performance and preservation of the binaural noise cues. To further increase the performance, a modification to the partial noise estimation (PNE) variable, η, is proposed which relies on exploiting the aforementioned trade-off by either constraining the output SNR or binaural noise cues to the same level before and after the addition of the remote microphone signal. The validity of the theoretical results are supplemented via simulations using a binaural setup with a single speech and noise source.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {952–966},
numpages = {15},
keywords = {binaural hearing aids, noise reduction, multi-channel Wiener filtering, binaural cues}
}

@article{10.1109/TASLP.2016.2531902,
author = {Winter, Fiete and Ahrens, Jens and Spors, Sascha},
title = {On Analytic Methods for 2.5-d Local Sound Field Synthesis Using Circular Distributions of Secondary Sources},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2531902},
doi = {10.1109/TASLP.2016.2531902},
abstract = {Local sound field synthesis allows for synthesizing a given desired sound field inside a limited target region such that the field is free of considerable spatial aliasing artifacts. Spatial aliasing artifacts are a consequence of overlaps due to unavoidable repetitions of the space-spectral coefficients of the secondary source driving function. We analyze various conceivable analytic ways of restricting the bandwidth of the spatial spectrum of the driving function such that considerable overlapping is prevented: local spatial bandlimitation (A), spectral windowing (B), and local spatial bandlimitation plus spectral windowing (C). While solution B is computationally significantly more efficient than A and C, it provides only limited control over the spatial location around which the aliasing-free region evolves. Solutions A and C provide more flexibility and higher accuracy whereby both achieve largely identical results so that the spectral windowing after the local spatial bandlimitation may be skipped. We present a detailed analysis of the properties of the spatial aliasing artifacts arising in the synthesis of a virtual plane wave. We establish a procedure for predicting the maximum possible size of the aliasing-free target region depending on its location and on the propagation direction of the desired sound field. The results can help reducing regularization in numerical solutions as they represent physical limitations that can be considered in the choice of parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {914–926},
numpages = {13},
keywords = {sound field synthesis, ambisonics, spatial aliasing, spherical harmonics}
}

@article{10.1109/TASLP.2016.2530405,
author = {Adeli, Mohammad and Rouat, Jean and Wood, Sean and Molotchnikoff, St\'{e}phane and Plourde, Eric},
title = {A Flexible Bio-Inspired Hierarchical Model for Analyzing Musical Timbre},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2530405},
doi = {10.1109/TASLP.2016.2530405},
abstract = {A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {875–889},
numpages = {15},
keywords = {instantaneous roughness, timbre, cochlear filter bank, temporal envelope, time-averaged spectrum, modulation filter bank, musical instrument classification, bayesian network, multimodal timbre characterization}
}

@article{10.1109/TASLP.2016.2526787,
author = {Tsai, T. J. and Stolcke, Andreas},
title = {Robust and Efficient Multiple Alignment of Unsynchronized Meeting Recordings},
year = {2016},
issue_date = {May 2016},
publisher = {IEEE Press},
volume = {24},
number = {5},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2526787},
doi = {10.1109/TASLP.2016.2526787},
abstract = {This paper proposes a way to generate a single high-quality audio recording of a meeting using no equipment other than participants' personal devices. Each participant in the meeting uses their mobile device as a local recording node, and they begin recording whenever they arrive in an unsynchronized fashion. The main problem in generating a single summary recording is to temporally align the various audio recordings in a robust and efficient manner. We propose a way to do this using an adaptive audio fingerprint based on spectrotemporal eigenfilters, where the fingerprint design is learned on-the-fly in a totally unsupervised way to perform well on the data at hand. The adaptive fingerprints require only a few seconds of data to learn a robust design, and they require no tuning. Our method uses an iterative, greedy two-stage alignment algorithm which finds a rough alignment using indexing techniques, and then performs a more fine-grained alignment based on Hamming distance. Our proposed system achieves 99% alignment accuracy on challenging alignment scenarios extracted from the ICSI meeting corpus, and it outperforms five other well-known and state-of-the-art fingerprint designs. We conduct extensive analyses of the factors that affect the robustness of the adaptive fingerprints, and we provide a simple heuristic that can be used to adjust the fingerprint's robustness according to the amount of computation we are willing to perform.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = may,
pages = {833–845},
numpages = {13},
keywords = {meetings, alignment, eigenfilter, adaptive, audio fingerprint}
}

@article{10.1109/TASLP.2016.2520371,
author = {Palangi, Hamid and Deng, Li and Shen, Yelong and Gao, Jianfeng and He, Xiaodong and Chen, Jianshu and Song, Xinying and Ward, Rabab},
title = {Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2520371},
doi = {10.1109/TASLP.2016.2520371},
abstract = {This paper develops a model that addresses sentence embedding, a hot topic in current natural language processing research, using recurrent neural networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed LSTM-RNN model sequentially takes each word in a sentence, extracts its information, and embeds it into a semantic vector. Due to its ability to capture long term memory, the LSTM-RNN accumulates increasingly richer information as it goes through the sentence, and when it reaches the last word, the hidden layer of the network provides a semantic representation of the whole sentence. In this paper, the LSTM-RNN is trained in a weakly supervised manner on user click-through data logged by a commercial web search engine. Visualization and analysis are performed to understand how the embedding process works. The model is found to automatically attenuate the unimportant words and detect the salient keywords in the sentence. Furthermore, these detected keywords are found to automatically activate different cells of the LSTM-RNN, where words belonging to a similar topic activate the same cell. As a semantic representation of the sentence, the embedding vector can be used in many different applications. These automatic keyword detection and topic allocation abilities enabled by the LSTM-RNN allow the network to perform document retrieval, a difficult language processing task, where the similarity between the query and documents can be measured by the distance between their corresponding sentence embedding vectors computed by the LSTM-RNN. On a web search task, the LSTM-RNN embedding is shown to significantly outperform several existing state of the art methods. We emphasize that the proposed model generates sentence embedding vectors that are specially useful for web document retrieval tasks. A comparison with a well known general sentence embedding method, the Paragraph Vector, is performed. The results show that the proposed method in this paper significantly outperforms Paragraph Vector method for web document retrieval task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {694–707},
numpages = {14},
keywords = {sentence embedding, deep learning, long short-term memory}
}

@article{10.1109/TASLP.2015.2499602,
author = {Liu, Jianming and Grant, Steven L.},
title = {Proportionate Adaptive Filtering for Block-Sparse System Identification},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2499602},
doi = {10.1109/TASLP.2015.2499602},
abstract = {In this paper, a new family of proportionate normalized least mean square (PNLMS) adaptive algorithms that improve the performance of identifying block-sparse systems is proposed. The main proposed algorithm, called block-sparse PNLMS (BS-PNLMS), is based on the optimization of a mixed l2,1 norm of the adaptive filter's coefficients. It is demonstrated that both the NLMS and the traditional PNLMS are special cases of BS-PNLMS. Meanwhile, a block-sparse improved PNLMS (BS-IPNLMS) is also derived for both sparse and dispersive impulse responses. Simulation results demonstrate that the proposed BS-PNLMS and BS-IPNLMS algorithms outperformed the NLMS, PNLMS and IPNLMS algorithms with only a modest increase in computational complexity.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {623–630},
numpages = {8},
keywords = {proportionate adaptive algorithm, sparse system identification, block-sparse}
}

@article{10.1109/TASLP.2016.2531286,
author = {Su, Ming-Hsiang and Wu, Chung-Hsien and Zheng, Yu-Ting},
title = {Exploiting Turn-Taking Temporal Evolution for Personality Trait Perception in Dyadic Conversations},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2531286},
doi = {10.1109/TASLP.2016.2531286},
abstract = {In dyadic conversations, turn-taking is a dynamically evolving behavior strongly linked to paralinguistic communication. Turn-taking temporal evolution in a dyadic conversation is inevitable and can be incorporated into a modeling framework for characterizing and recognizing the personality traits (PTs) of two speakers. This study presents an approach to automatically predicting PTs in a dyadic conversation. First, a recurrent neural network (RNN) was used to model the relationship between Big Five Inventory 10 (BFI-10) items and linguistic features of spoken text in each turn of a speaker (speaker turn) to output a BFI-10 profile. The RNN applies a recurrent property to characterize the short-term temporal evolution of a dialog. Second, the coupled hidden Markov model (C-HMM) was employed to model the long-term turn-taking temporal evolution and cross-speaker contextual information for detecting the PTs of two individuals for the entire dialog represented by the BFI-10 profile sequence. The Mandarin Conversational Dialogue Corpus was used for evaluation. The evaluation result shows that an average perception accuracy of 79.66% for the big five traits was achieved using five-fold cross validation. Compared with conventional HMM and support vector machine-based methods, the proposed approach achieved a more favorable performance according to a statistical significance test. The encouraging results confirm the usability of this system for future applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {733–744},
numpages = {12},
keywords = {big-five personality trait, coupled hidden markov model, personality trait perception, dyadic conversation}
}

@article{10.1109/TASLP.2016.2514492,
author = {N\o{}rholm, Sidsel Marie and Jensen, Jesper Rindom and Christensen, Mads Gr\ae{}sb\o{}ll},
title = {Enhancement and Noise Statistics Estimation for Non-Stationary Voiced Speech},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2514492},
doi = {10.1109/TASLP.2016.2514492},
abstract = {In this paper, single channel speech enhancement in the time domain is considered. We address the problem of modelling non-stationary speech by describing the voiced speech parts by a harmonic linear chirp model instead of using the traditional harmonic model. This means that the speech signal is not assumed stationary, instead the fundamental frequency can vary linearly within each frame. The linearly constrained minimum variance (LCMV) filter and the amplitude and phase estimation (APES) filter are derived in this framework and compared to the harmonic versions of the same filters. It is shown through simulations on synthetic and speech signals, that the chirp versions of the filters perform better than their harmonic counterparts in terms of output signal-to-noise ratio (SNR) and signal reduction factor. For synthetic signals, the output SNR for the harmonic chirp APES based filter is increased 3 dB compared to the harmonic APES based filter at an input SNR of 10 dB, and at the same time the signal reduction factor is decreased. For speech signals, the increase is 1.5 dB along with a decrease in the signal reduction factor of 0.7. As an implicit part of the APES filter, a noise covariance matrix estimate is obtained. We suggest using this estimate in combination with other filters such as the Wiener filter. The performance of the Wiener filter and LCMV filter are compared using the APES noise covariance matrix estimate and a power spectral density (PSD) based noise covariance matrix estimate. It is shown that the APES covariance matrix works well in combination with the Wiener filter, and the PSD based covariance matrix works well in combination with the LCMV filter.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {645–658},
numpages = {14},
keywords = {non-stationary speech, speech enhancement, harmonic signal model, chirp model}
}

@article{10.1109/TASLP.2015.2505416,
author = {Jensen, Jesper Rindom and Benesty, Jacob and Christensen, Mads Gr\ae{}sb\o{}ll},
title = {Noise Reduction with Optimal Variable Span Linear Filters},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2505416},
doi = {10.1109/TASLP.2015.2505416},
abstract = {In this paper, the problem of noise reduction is addressed as a linear filtering problem in a novel way by using concepts from subspace-based enhancement methods, resulting in variable span linear filters. This is done by forming the filter coefficients as linear combinations of a number of eigenvectors stemming from a joint diagonalization of the covariance matrices of the signal of interest and the noise. The resulting filters are flexible in that it is possible to trade off distortion of the desired signal for improved noise reduction. This tradeoff is controlled by the number of eigenvectors included in forming the filter. Using these concepts, a number of different filter designs are considered, like minimum distortion, Wiener, maximum SNR, and tradeoff filters. Interestingly, all these can be expressed as special cases of variable span filters. We also derive expressions for the speech distortion and noise reduction of the various filter designs. Moreover, we consider an alternative approach, wherein the filter is designed for extracting an estimate of the noise signal, which can then be extracted from the observed signals, which is referred to as the indirect approach. Simulations demonstrate the advantages and properties of the variable span filter designs, and their potential performance gain compared to widely used speech enhancement methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {631–644},
numpages = {14},
keywords = {joint diagonalization, noise reduction, speech enhancement, optimal filters, subspace, span}
}

@article{10.1109/TASLP.2016.2530401,
author = {Phan, Huy and Hertel, Lars and Maass, Marco and Mazur, Radoslaw and Mertins, Alfred},
title = {Learning Representations for Nonspeech Audio Events through Their Similarities to Speech Patterns},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2530401},
doi = {10.1109/TASLP.2016.2530401},
abstract = {The human auditory system is very well matched to both human speech and environmental sounds. Therefore, the question arises whether human speech material may provide useful information for training systems for analyzing nonspeech audio signals, e.g., in a classification task. In order to answer this question, we consider speech patterns as basic acoustic concepts, which embody and represent the target nonspeech signal. To find out how similar the nonspeech signal is to speech, we classify it with a classifier trained on the speech patterns and use the classification posteriors to represent the closeness to the speech bases. The speech similarities are finally employed as a descriptor to represent the target signal. We further show that a better descriptor can be obtained by learning to organize the speech categories hierarchically with a tree structure. Furthermore, these descriptors are generic. That is, once the speech classifier has been learned, it can be employed as a feature extractor for different datasets without retraining. Lastly, we propose an algorithm to select a sufficient subset, which provides an approximate representation capability of the entire set of available speech patterns. We conduct experiments for the application of audio event analysis. Phone triplets from the TIMIT dataset were used as speech patterns to learn the descriptors for audio events of three different datasets with different complexity, including UPC-TALP, Freiburg-106, and NAR. The experimental results on the event classification task show that a good performance can be easily obtained even if a simple linear classifier is used. Furthermore, fusion of the learned descriptors as an additional source leads to state-of-the-art performance on all the three target datasets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {807–822},
numpages = {16},
keywords = {phone triplets, feature learning, speech patterns, nonspeech audio event, Representation}
}

@article{10.1109/TASLP.2016.2528171,
author = {Wang, Zhong-Qiu and Wang, DeLiang},
title = {A Joint Training Framework for Robust Automatic Speech Recognition},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2528171},
doi = {10.1109/TASLP.2016.2528171},
abstract = {Robustness against noise and reverberation is critical for ASR systems deployed in real-world environments. In robust ASR, corrupted speech is normally enhanced using speech separation or enhancement algorithms before recognition. This paper presents a novel joint training framework for speech separation and recognition. The key idea is to concatenate a deep neural network (DNN) based speech separation frontend and a DNN-based acoustic model to build a larger neural network, and jointly adjust the weights in each module. This way, the separation fron-tend is able to provide enhanced speech desired by the acoustic model and the acoustic model can guide the separation frontend to produce more discriminative enhancement. In addition, we apply sequence training to the jointly trained DNN so that the linguistic information contained in the acoustic and language models can be back-propagated to influence the separation frontend at the training stage. To further improve the robustness, we add more noise- and reverberation-robust features for acoustic modeling. At the test stage, utterance-level unsupervised adaptation is performed to adapt the jointly trained network by learning a linear transformation of the input of the separation frontend. The resulting sequence-discriminative jointly-trained multistream system with run-time adaptation achieves 10.63% average word error rate (WER) on the test set of the reverberant and noisy CHiME-2 dataset (task-2), which represents the best performance on this dataset and a 22.75% error reduction over the best existing method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {796–806},
numpages = {11},
keywords = {deep neural networks (dnn), robust automatic speech recognition, speech separation, joint training, time-frequency masking, chime-2}
}

@article{10.1109/TASLP.2015.2497148,
author = {Li, Peifeng and Zhou, Guodong},
title = {Joint Argument Inference in Chinese Event Extraction with Argument Consistency and Event Relevance},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2497148},
doi = {10.1109/TASLP.2015.2497148},
abstract = {Event extraction in Chinese suffers greatly from the frequent missing of arguments. Statistical analysis of the automatic content extraction (ACE) 2005 Chinese corpus shows that nearly 55% of arguments do not occur in their corresponding event mentions. This problem greatly hinders the wide deployment of event extraction in real applications. This paper proposes a novel joint argument inference model to recover those missing arguments in event mentions from a semantic perspective. In particular, this model employs various types of argument consistencies to reveal intra-event argument semantics in multiple dimensions, such as argument-argument, argument-role and argument-trigger. Moreover, this model explores several linguistic-driven event relevance phenomena, such as Coreference, Sequence, and Parallel, to unveil inter-event argument semantics in various layers, such as sentence, discourse, and document. An evaluation of the ACE 2005 Chinese corpus justifies the effectiveness of our joint argument inference model compared to a state-of-the-art baseline.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {612–622},
numpages = {11},
keywords = {argument inference, argument consistency, chinese event extraction, joint modeling, event relevance}
}

@article{10.1109/TASLP.2016.2522655,
author = {Takamichi, Shinnosuke and Toda, Tomoki and Black, Alan W. and Neubig, Graham and Sakti, Sakriani and Nakamura, Satoshi},
title = {Postfilters to Modify the Modulation Spectrum for Statistical Parametric Speech Synthesis},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2522655},
doi = {10.1109/TASLP.2016.2522655},
abstract = {This paper presents novel approaches based on modulation spectrum (MS) for high-quality statistical parametric speech synthesis, including text-to-speech (TTS) and voice conversion (VC). Although statistical parametric speech synthesis offers various advantages over concatenative speech synthesis, the synthetic speech quality is still not as good as that of con-catenative speech synthesis or the quality of natural speech. One of the biggest issues causing the quality degradation is the over-smoothing effect often observed in the generated speech parameter trajectories. Global variance (GV) is known as a feature well correlated with the over-smoothing effect, and the effectiveness of keeping the GV of the generated speech parameter trajectories similar to those of natural speech has been confirmed. However, the quality gap between natural speech and synthetic speech is still large. In this paper, we propose using the MS of the generated speech parameter trajectories as a new feature to effectively quantify the over-smoothing effect. Moreover, we propose post-filters to modify the MS utterance by utterance or segment by segment to make the MS of synthetic speech close to that of natural speech. The proposed postfilters are applicable to various synthesizers based on statistical parametric speech synthesis. We first perform an evaluation of the proposed method in the framework of hidden Markov model (HMM)-based TTS, examining its properties from different perspectives. Furthermore, effectiveness of the proposed postfilters are also evaluated in Gaussian mixture model (GMM)-based VC and classification and regression trees (CART)-based TTS (a.k.a., CLUSTERGEN). The experimental results demonstrate that 1) the proposed utterance-level postfilter achieves quality comparable to the conventional generation algorithm considering the GV, and yields significant improvements by applying to the GV-based generation algorithm in HMM-based TTS, 2) the proposed segment-level postfilter capable of achieving low-delay synthesis also yields significant improvements in synthetic speech quality, and 3) the proposed postfilters are also effective in not only HMM-based TTS but also GMM-based VC and CLUSTERGEN.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {755–767},
numpages = {13},
keywords = {CLUSTERGEN, statistical parametric speech synthesis, GMM-based voice conversion, HMM-based text-to-speech, global variance, modulation spectrum, over-smoothing, post-filter}
}

@article{10.1109/TASLP.2016.2522649,
author = {Jeffet, Michael and Shabtai, Noam R. and Rafaely, Boaz},
title = {Theory and Perceptual Evaluation of the Binaural Reproduction and Beamforming Tradeoff in the Generalized Spherical Array Beamformer},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2522649},
doi = {10.1109/TASLP.2016.2522649},
abstract = {Microphone arrays are widely used in speech enhancement systems for noisy and reverberant environments. Recently, a generalized spherical array beamforming approach was developed incorporating binaural sound reproduction in the beamforming process. This generalized spherical array beam-former (GSB) maintains the spatial information through the bin-aural cues and improves both the spatial realism and the speech intelligibility. In this paper, the theory of the tradeoff that arises when incorporating both beamforming and binaural reproduction in a single array is developed and investigated through a simulation study and a listening test. By representing the GSB formulation in matrix form for investigating the single plane-wave scenario, two measures are developed in order to evaluate the performance of the GSB in terms of both binaural reproduction and spatial selectivity. These measures are then employed in the evaluation of the performance of various GSB beam-patterns using simulations. A listening test experiment that validates the simulation results is then reported. Results validate the theory, i.e., the GSB can be used to integrate successfully binaural reproduction and beamforming, allowing the user to emphasize either of the two, but with a clear tradeoff; improving one is only possible at the expense of degrading the other.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {708–718},
numpages = {11},
keywords = {binaural sound reproduction, spherical microphone arrays, speech intelligibility, beamforming, spherical harmonics}
}

@article{10.1109/TASLP.2016.2526779,
author = {Andersen, Kristian Timm and Moonen, Marc},
title = {Adaptive Time-Frequency Analysis for Noise Reduction in an Audio Filter Bank with Low Delay},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2526779},
doi = {10.1109/TASLP.2016.2526779},
abstract = {In this paper, an adaptive time-frequency analysis scheme is proposed along with a synthesis scheme using an asymmetric window. The proposed scheme is suitable for audio noise reduction with a low delay in the range of 0 to 4 ms. The main novelty of the paper is the adaptive analysis scheme that can adapt to the incoming signal independently in both time and frequency by employing a complex filter on a DFT modulated filter bank. A number of adaptive time-frequency schemes are described that are suitable for low delay and low computational complexity. The adaptive time-frequency scheme is used for the computation of noise reduction gain factors, which are then adopted in a nonadaptive analysis/synthesis scheme. The synthesis scheme uses an asymmetric window to achieve a good tradeoff between low delay and a sharp frequency response. Examples are given of the adaptive analysis and measurements of the synthesis scheme are given to show that the filter bank has a gain dependent nonlinear phase response. Finally, a noise reduction task is performed that shows good performance compared to reference implementations in terms of segmental SNR and PESQ.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {784–795},
numpages = {12},
keywords = {adaptive time-frequency analysis, speech enhancement, noise reduction, low delay}
}

@article{10.1109/TASLP.2016.2521486,
author = {Parada, Pablo Peso and Sharma, Dushyant and Lainez, Jose and Barreda, Daniel and van Waterschoot, Toon and Naylor, Patrick A.},
title = {A Single-Channel Non-Intrusive C50 Estimator Correlated with Speech Recognition Performance},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2521486},
doi = {10.1109/TASLP.2016.2521486},
abstract = {Several intrusive measures of reverberation can be computed from measured and simulated room impulse responses, over the full frequency band or for each individual mel-frequency subband. It is initially shown that full-band clarity index C50 is the most correlated measure on average with reverberant speech recognition performance. This corroborates previous findings but now for the dataset to be used in this study. We extend the previous findings to show that C50 also exhibits the highest mutual information on average. Motivated by these extended findings, a nonintrusive room acoustic (NIRA) estimation method is proposed to estimate C50 from only the reverberant speech signal. The NIRA method is a data-driven approach based on computing a number of features from the speech signal and it employs these features to train a model used to perform the estimation. The choice of features and learning techniques are explored in this work using an evaluation set which comprises approximately 100 000 different reverberant signals (around 93 h of speech) including reverberation from measured and simulated room impulse responses. The feature importance of each feature with respect to the estimation of the target C50 is analysed following two different approaches. In both cases, the newly chosen set of features shows high importance for the target. The best C50 estimator provides a root-mean-square deviation around 3 dB on average for all reverberant test environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {719–732},
numpages = {14},
keywords = {room acoustic parameter estimation, reverberation, reverberant speech recognition}
}

@article{10.1109/TASLP.2016.2518804,
author = {Kodrasi, Ina and Doclo, Simon},
title = {Joint Dereverberation and Noise Reduction Based on Acoustic Multi-Channel Equalization},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2518804},
doi = {10.1109/TASLP.2016.2518804},
abstract = {Regularized acoustic multi-channel equalization techniques, such as regularized partial multi-channel equalization based on the multiple-input/output inverse theorem (RPMINT), are able to achieve a high dereverberation performance in the presence of room impulse response perturbations but may lead to amplification of the additive noise. In this paper, two time-domain techniques aiming at joint dereverberation and noise reduction based on acoustic multi-channel equalization are proposed. The first technique, namely RPMINT for joint derever-beration and noise reduction (RPM-DNR), extends RPMINT by explicitly taking the noise statistics into account. In addition to the regularization parameter used in RPMINT, the RPM-DNR technique introduces an additional weighting parameter, enabling a trade-off between dereverberation and noise reduction. The second technique, namely multi-channel Wiener filter for joint dereverberation and noise reduction (MWF-DNR), takes both the speech and the noise statistics into account and uses the RPMINT filter to compute a dereverberated reference signal for the multichannel Wiener filter. The MWF-DNR technique also introduces an additional weighting parameter, which now provides a trade-off between speech distortion and noise reduction. To automatically select the regularization and weighting parameters, for the RPM-DNR technique a novel procedure based on the L-hypersurface is proposed, whereas for the MWF-DNR technique two decoupled optimization procedures based on the L-curve are used. Extensive simulations demonstrate using instrumental measures that the RPM-DNR technique maintains the dereverberation performance of the RPMINT technique while improving its noise reduction performance. Furthermore, it is shown that the MWF-DNR technique yields a significantly better noise reduction performance than the RPM-DNR technique at the expense of a worse dereverberation performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {680–693},
numpages = {14},
keywords = {acoustic multi-channel equalization, noise reduction, automatic parameter selection, dereverberation, l-hypersurface}
}

@article{10.1109/TASLP.2016.2517318,
author = {Abdul-Rauf, Sadaf and Schwenk, Holger and Lambert, Patrik and Nawaz, Mohammad},
title = {Empirical Use of Information Retrieval to Build Synthetic Data for SMT Domain Adaptation},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2517318},
doi = {10.1109/TASLP.2016.2517318},
abstract = {In this paper, we present information retrieval as a powerful tool for addressing an imperative problem in the field of statistical machine translation, i.e., improving translation quality when not enough parallel corpora are available. We devise a framework, which uses information retrieval to create a synthetic corpus from the easily available monolingual corpora. We propose an improved unsupervised training approach with a data selection mechanism, which selects only the most appropriate sentences, thus reducing the amount of data, which is less related to the domain in the additional bitext. We also introduce a new method to choose sentences based on their relative similarity/difference from the query sentence. Using the synthetic corpus created by our method, we are able to improve state-of-the-art statistical machine translation systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {745–754},
numpages = {10},
keywords = {monolingual corpus, information retrieval, domain adaptation, statistical machine translation}
}

@article{10.1109/TASLP.2016.2526653,
author = {Wu, Zhizheng and De Leon, Phillip L. and Demiroglu, Cenk and Khodabakhsh, Ali and King, Simon and Ling, Zhen-Hua and Saito, Daisuke and Stewart, Bryan and Toda, Tomoki and Wester, Mirjam and Yamagishi, Junichi},
title = {Anti-Spoofing for Text-Independent Speaker Verification: An Initial Database, Comparison of Countermeasures, and Human Performance},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2526653},
doi = {10.1109/TASLP.2016.2526653},
abstract = {In this paper, we present a systematic study of the vulnerability of automatic speaker verification to a diverse range of spoofing attacks. We start with a thorough analysis of the spoofing effects of five speech synthesis and eight voice conversion systems, and the vulnerability of three speaker verification systems under those attacks. We then introduce a number of countermeasures to prevent spoofing attacks from both known and unknown attackers. Known attackers are spoofing systems whose output was used to train the countermeasures, while an unknown attacker is a spoofing system whose output was not available to the countermeasures during training. Finally, we benchmark automatic systems against human performance on both speaker verification and spoofing detection tasks.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {768–783},
numpages = {16},
keywords = {speech synthesis, voice conversion, speaker verification, anti-spoofing, countermeasure, spoofing attack, security}
}

@article{10.1109/TASLP.2016.2517567,
author = {Kamper, Herman and Jansen, Aren and Goldwater, Sharon},
title = {Unsupervised Word Segmentation and Lexicon Discovery Using Acoustic Word Embeddings},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2517567},
doi = {10.1109/TASLP.2016.2517567},
abstract = {In settings where only unlabeled speech data is available, speech technology needs to be developed without transcriptions, pronunciation dictionaries, or language modelling text. A similar problem is faced when modeling infant language acquisition. In these cases, categorical linguistic structure needs to be discovered directly from speech audio. We present a novel unsu-pervised Bayesian model that segments unlabeled speech and clusters the segments into hypothesized word groupings. The result is a complete unsupervised tokenization of the input speech in terms of discovered word types. In our approach, a potential word segment (of arbitrary length) is embedded in a fixed-dimensional acoustic vector space. The model, implemented as a Gibbs sampler, then builds a whole-word acoustic model in this space while jointly performing segmentation. We report word error rates in a small-vocabulary connected digit recognition task by mapping the unsupervised decoded output to ground truth transcriptions. The model achieves around 20% error rate, outperforming a previous HMM-based system by about 10% absolute. Moreover, in contrast to the baseline, our model does not require a pre-specified vocabulary size.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {669–679},
numpages = {11},
keywords = {unsupervised speech processing, word acquisition, word discovery, speech segmentation, unsupervised learning}
}

@article{10.1109/TASLP.2016.2516647,
author = {Mehta, Daryush D. and Van Stan, Jarrad H. and Hillman, Robert E.},
title = {Relationships between Vocal Function Measures Derived from an Acoustic Microphone and a Subglottal Neck-Surface Accelerometer},
year = {2016},
issue_date = {April 2016},
publisher = {IEEE Press},
volume = {24},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2516647},
doi = {10.1109/TASLP.2016.2516647},
abstract = {Monitoring subglottal neck-surface acceleration has received renewed attention due to the ability of low-profile accelerometers to confidentially and noninvasively track properties related to normal and disordered voice characteristics and behavior. This study investigated the ability of subglottal neck-surface acceleration to yield vocal function measures traditionally derived from the acoustic voice signal and help guide the development of clinically functional accelerometer-based measures from a physiological perspective. Results are reported for 82 adult speakers with voice disorders and 52 adult speakers with normal voices who produced the sustained vowels /a/, /i/, and /u/ at a comfortable pitch and loudness during the simultaneous recording of radiated acoustic pressure and subglottal neck-surface acceleration. As expected, timing-related measures of jitter exhibited the strongest correlation between acoustic and neck-surface acceleration waveforms (r ≤ 0.99), whereas amplitude-based measures of shimmer correlated less strongly (r ≤ 0.74). Additionally, weaker correlations were exhibited by spectral measures of harmonics-to-noise ratio (r ≤ 0.69) and tilt (r ≤ 0.57), whereas the cepstral peak prominence correlated more strongly (r ≤ 0.90). These empirical relationships provide evidence to support the use of accelerometers as effective complements to acoustic recordings in the assessment and monitoring of vocal function in the laboratory, clinic, and during an individual's daily activities.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {659–668},
numpages = {10},
keywords = {ambulatory voice monitoring, neck-surface accelerom-eter, vocal function analysis, vocal perturbation, harmonics-to-noise ratio, cepstral peak prominence}
}

@article{10.1109/TASLP.2015.2512499,
author = {Traa, Johannes and Wingate, David and Stein, Noah D. and Smaragdis, Paris},
title = {Robust Source Localization and Enhancement with a Probabilistic Steered Response Power Model},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2512499},
doi = {10.1109/TASLP.2015.2512499},
abstract = {Source localization and enhancement are often treated separately in the array processing literature. One can apply steered response power (SRP) localization to determine the sources' Directions-Of-Arrival (DOA) followed by beamforming and Wiener post-filtering to isolate the sources from each other and ambient interference. We show that when there is significant overlap between directional sources of interest in the time-frequency (TF) plane, traditional SRP localization breaks down. This may occur, for example, when the array is located near a reflector, significant early reflections are present, or the sources are harmonized. We propose a joint solution to the localization and enhancement problems via a probabilistic interpretation of the SRP function. We formulate optimization procedures for (1) a mixture of single-source SRP distributions (MoSRP) and (2) a multi-source SRP distribution (MultSRP). Unlike in traditional localization, the latter approach explicitly models source overlap in the TF plane. Results shows that the MultSRP model is capable of localizing sources with significant overlap in the TF domain and that either of the proposed methods out-performs standard SRP localization for multiple speakers.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {493–503},
numpages = {11},
keywords = {blind source separation, steered response power, beamforming, source localization}
}

@article{10.1109/TASLP.2016.2517326,
author = {Wang, Lin and Doclo, Simon},
title = {Correlation Maximization-Based Sampling Rate Offset Estimation for Distributed Microphone Arrays},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2517326},
doi = {10.1109/TASLP.2016.2517326},
abstract = {In this paper, we investigate the sampling rate mismatch problem in distributed microphone arrays and propose a correlation maximization algorithm to blindly estimate the sampling rate offset between two asynchronously sampled microphone signals. We approximate the sampling rate offset with a linear-phase drift model in the short-time Fourier transform (STFT) domain and show that the correlation coefficient between two microphone signals tends to present the highest value when the sampling of the two microphone signals is synchronized. Based on this finding we propose the correlation maximization algorithm, which performs sampling rate compensation on two microphone signals with different possible offset values and calculates their correlation coefficient after compensation. The offset value that leads to the largest correlation coefficient is chosen as the optimal estimate. Since the precision of the STFT linear-phase drift model used in the algorithm degrades as the sampling rate offset or the signal length is increased, we further propose a two-stage exhaustive search scheme to detect the optimal sampling rate offset. This scheme is able to minimize the influence of the linear-phase drift model error in order to improve the sampling rate offset estimation accuracy. Both simulated as well as real-world experiments confirm the effectiveness of the proposed algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {571–582},
numpages = {12},
keywords = {sampling rate offset, correlation coefficient, distributed microphone array}
}

@article{10.1109/TASLP.2015.2509248,
author = {Sonnleitner, Reinhard and Widmer, Gerhard},
title = {Robust Quad-Based Audio Fingerprinting},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2509248},
doi = {10.1109/TASLP.2015.2509248},
abstract = {We propose an audio fingerprinting method that adapts findings from the field of blind astrometry to define simple, efficiently representable characteristic feature combinations called quads. Based on these, an audio identification algorithm is described that is robust to noise and severe time-frequency scale distortions and accurately identifies the underlying scale transform factors. The low number and compact representation of content features allows for efficient application of exact fixed-radius near-neighbor search methods for fingerprint matching in large audio collections. We demonstrate the practicability of the method on a collection of 100,000 songs, analyze its performance for a diverse set of noise as well as severe speed, tempo and pitch scale modifications, and identify a number of advantages of our method over two state-of-the-art distortion-robust audio identification algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {409–421},
numpages = {13},
keywords = {media monitoring, pitch shifting, tempo change, time scaling, audio fingerprinting, DJ sets}
}

@article{10.1109/TASLP.2015.2512041,
author = {Lin, Zheng and Jin, Xiaolong and Xu, Xueke and Wang, Yuanzhuo and Cheng, Xueqi and Wang, Weiping and Meng, Dan},
title = {An Unsupervised Cross-Lingual Topic Model Framework for Sentiment Classification},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2512041},
doi = {10.1109/TASLP.2015.2512041},
abstract = {Sentiment classification aims to determine the sentiment polarity expressed in a text. In online customer reviews, the sentiment polarities of words are usually dependent on the corresponding aspects. For instance, in mobile phone reviews, we may expect the long battery time but not enjoy the long response time of the operating system. Therefore, it is necessary and appealing to consider aspects when conducting sentiment classification. Probabilistic topic models that jointly detect aspects and sentiments have gained much success recently. However, most of the existing models are designed to work well in a language with rich resources. Directly applying those models on poor-quality corpora often leads to poor results. Consequently, a potential solution is to use the cross-lingual topic model to improve the sentiment classification for a target language by leveraging data and knowledge from a source language. However, the existing cross-lingual topic models are not suitable for sentiment classification because sentiment factors are not considered therein. To solve these problems, we propose for the first time a novel cross-lingual topic model framework which can be easily combined with the state-of-the-art aspect/sentiment models. Extensive experiments in different domains and multiple languages demonstrate that our model can significantly improve the accuracy of sentiment classification in the target language.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {432–444},
numpages = {13},
keywords = {cross-language, sentiment classification, topic model}
}

@article{10.1109/TASLP.2015.2512039,
author = {Leijon, Arne and Henter, Gustav Eje and Dahlquist, Martin},
title = {Bayesian Analysis of Phoneme Confusion Matrices},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2512039},
doi = {10.1109/TASLP.2015.2512039},
abstract = {This paper presents a parametric Bayesian approach to the statistical analysis of phoneme confusion matrices measured for groups of individual listeners in one or more test conditions. Two different bias problems in conventional estimation of mutual information are analyzed and explained theoretically. Evaluations with synthetic datasets indicate that the proposed Bayesian method can give satisfactory estimates of mutual information and response probabilities, even for phoneme confusion tests using a very small number of test items for each phoneme category. The proposed method can reveal overall differences in performance between two test conditions with better power than conventional Wilcoxon significance tests or conventional confidence intervals. The method can also identify sets of confusion-matrix cells that are credibly different between two test conditions, with better power than a similar approximate frequentist method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {469–482},
numpages = {14},
keywords = {bayes methods, mutual information, parameter estimation, speech recognition}
}

@article{10.1109/TASLP.2016.2518801,
author = {Radmanesh, Nasim and Burnett, Ian S. and Rao, Bhaskar D.},
title = {A Lasso-LS Optimization with a Frequency Variable Dictionary in a Multizone Sound System},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2518801},
doi = {10.1109/TASLP.2016.2518801},
abstract = {This paper presents an approach for multizone wideband sound field generation using an efficient harmonic nested (EHN) dictionary for sparse loudspeakers' placement and weight. Effectively, the nested arrays provide a priori knowledge of prospective loudspeaker locations based on the frequency bands of interest. The nested arrays are then further optimized in the Lasso stage to form an efficient loudspeakers' location dictionary. The final loudspeaker locations and weightings are estimated by a two-stage Lasso-LS pressure matching optimization. In the first stage Lasso algorithm, the center band frequencies of octave bands from 1 kHz to 8 kHz were used to select active loudspeakers. A second stage then optimizes reproduction using all selected loudspeakers on the basis of a regularized LS algorithm. The results demonstrate that the proposed approach provides a solution for the multizone sound system with the mean squared error (MSE) under -30 dB across the targeted frequency range (500 Hz-16 kHz) using a linear array e.g. 13 loudspeakers. While, the single-stage LS approach generates the MSE peaks of -10 dB and -9 dB at 9 kHz within the active and silent zones respectively using an identical number of loudspeakers and array length.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {583–593},
numpages = {11},
keywords = {least-squares, sparse optimization, loudspeaker positioning, frequency variable dictionary, wideband, multizone sound field, lasso}
}

@article{10.1109/TASLP.2016.2515514,
author = {O'Hanlon, Ken and Nagano, Hidehisa and Keriven, Nicolas and Plumbley, Mark D.},
title = {Non-Negative Group Sparsity with Subspace Note Modelling for Polyphonic Transcription},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2515514},
doi = {10.1109/TASLP.2016.2515514},
abstract = {Automatic music transcription (AMT) can be performed by deriving a pitch-time representation through decomposition of a spectrogram with a dictionary of pitch-labelled atoms. Typically, non-negative matrix factorisation (NMF) methods are used to decompose magnitude spectrograms. One atom is often used to represent each note. However, the spectrum of a note may change over time. Previous research considered this variability using different atoms to model specific parts of a note, or large dictionaries comprised of datapoints from the spectrograms of full notes. In this paper, the use of subspace modelling of note spectra is explored, with group sparsity employed as a means of coupling activations of related atoms into a pitched subspace. Stepwise and gradient-based methods for non-negative group sparse decompositions are proposed. Finally, a group sparse NMF approach is used to tune a generic harmonic subspace dictionary, leading to improved NMF-based AMT results.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {530–542},
numpages = {13},
keywords = {stepwise optimal, automatic music transcription, non-negative matrix factorisation, group sparsity}
}

@article{10.1109/TASLP.2016.2515506,
author = {Shepstone, Sven Ewan and Lee, Kong Aik and Li, Haizhou and Tan, Zheng-Hua and Jensen, S\o{}ren Holdt},
title = {Total Variability Modeling Using Source-Specific Priors},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2515506},
doi = {10.1109/TASLP.2016.2515506},
abstract = {In total variability modeling, variable length speech utterances are mapped to fixed low-dimensional i-vectors. Central to computing the total variability matrix and i-vector extraction, is the computation of the posterior distribution for a latent variable conditioned on an observed feature sequence of an utterance. In both cases the prior for the latent variable is assumed to be non-informative, since for homogeneous datasets there is no gain in generality in using an informative prior. This work shows in the heterogeneous case, that using informative priors for computing the posterior, can lead to favorable results. We focus on modeling the priors using minimum divergence criterion or factor analysis techniques. Tests on the NIST 2008 and 2010 Speaker Recognition Evaluation (SRE) dataset show that our proposed method beats four baselines: For i-vector extraction using an already trained matrix, for the short2-short3 task in SRE'08, five out of eight female and four out of eight male common conditions, were improved. For the core-extended task in SRE'10, four out of nine female and six out of nine male common conditions were improved. When incorporating prior information into the training of the T matrix itself, the proposed method beats the baselines for six out of eight female and five out of eight male common conditions, for SRE'08, and five and six out of nine conditions, for the male and female case, respectively, for SRE'10. Tests using factor analysis for estimating priors show that two priors do not offer much improvement, but in the case of three separate priors (sparse data), considerable improvements were gained.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {504–517},
numpages = {14},
keywords = {total variability, source variation, i-vector, factor analysis, prior, expectation-maximization}
}

@article{10.1109/TASLP.2016.2517565,
author = {Grijalva, Felipe and Martini, Luiz and Florencio, Dinei and Goldenstein, Siome},
title = {A Manifold Learning Approach for Personalizing HRTFs from Anthropometric Features},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2517565},
doi = {10.1109/TASLP.2016.2517565},
abstract = {We present a new anthropometry-based method to personalize head-related transfer functions (HRTFs) using manifold learning in both azimuth and elevation angles with a single nonlinear regression model. The core element of our approach is a domain-specific nonlinear dimensionality reduction technique, denominated Isomap, over the intraconic component of HRTFs resulting from a spectral decomposition. HRTF intraconic components encode the most important cues for HRTF individualization, leaving out subject-independent cues. First, we modify the graph construction procedure of Isomap to integrate relevant prior knowledge of spatial audio into a single manifold for all subjects by exploiting the existing correlations among HRTFs across individuals, directions, and ears. Then, with the aim of preserving the multifactor nature of HRTFs (i.e. subject, direction and frequency), we train a single artificial neural network to predict low-dimensional HRTFs from anthropometric features. Finally, we reconstruct the HRTF from its estimated low-dimensional version using a neighborhood-based reconstruction approach. Our findings show that introducing prior knowledge in Isomap's manifold is a powerful way to capture the underlying factors of spatial hearing. Our experiments show, with p-values less than 0.05, that our approach outperforms using, either a PCA linear reduction, or the full HTRF, in its intermediate stages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {559–570},
numpages = {12},
keywords = {HRTF personalization, virtual auditory displays, spatial audio, manifold learning}
}

@article{10.1109/TASLP.2015.2511922,
author = {Tan, Tian and Qian, Yanmin and Yu, Kai},
title = {Cluster Adaptive Training for Deep Neural Network Based Acoustic Model},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2511922},
doi = {10.1109/TASLP.2015.2511922},
abstract = {Although context-dependent DNN-HMM systems have achieved significant improvements over GMM-HMM systems, significant performance degradation has been observed if the acoustic condition of the test data mismatches that of the training data. Hence, adaptation and adaptive training of DNN are of great research interest. Previous DNN adaptation works mainly focus on adapting parameters of a single DNN by applying linear transformations to feature or hidden-layer output; introducing vector representation of non-speech variability into the input. In these methods, large number of parameters are required to be estimated during adaptation. In this paper, the cluster adaptive training (CAT) framework is employed for DNN adaptive training. Here, multiple weight matrices are constructed to form the basis of a canonical parametric space. During adaptation, for a new acoustic condition, an interpolation vector is estimated to combine the weight basis into a single adapted weight matrix. Since only the interpolation vector need to be estimated during adaptation, the number of updated parameters is much smaller than existing DNN adaptation methods. The CAT-DNN approach was evaluated on an English switchboard task in unsupervised adaptation mode. It achieved significant WER reductions over the unadapted DNN-HMM, relative 7.6% to 10.6%, with only 10 parameters.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {459–468},
numpages = {10},
keywords = {adaptation, cluster adaptive training, deep neural network}
}

@article{10.1109/TASLP.2015.2512042,
author = {Williamson, Donald S. and Wang, Yuxuan and Wang, DeLiang},
title = {Complex Ratio Masking for Monaural Speech Separation},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2512042},
doi = {10.1109/TASLP.2015.2512042},
abstract = {Speech separation systems usually operate on the short-time Fourier transform (STFT) of noisy speech, and enhance only the magnitude spectrum while leaving the phase spectrum unchanged. This is done because there was a belief that the phase spectrum is unimportant for speech enhancement. Recent studies, however, suggest that phase is important for perceptual quality, leading some researchers to consider magnitude and phase spectrum enhancements. We present a supervised monaural speech separation approach that simultaneously enhances the magnitude and phase spectra by operating in the complex domain. Our approach uses a deep neural network to estimate the real and imaginary components of the ideal ratio mask defined in the complex domain. We report separation results for the proposed method and compare them to related systems. The proposed approach improves over other methods when evaluated with several objective metrics, including the perceptual evaluation of speech quality (PESQ), and a listening test where subjects prefer the proposed approach with at least a 69% rate.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {483–492},
numpages = {10},
keywords = {speech quality, speech separation, deep neural networks, complex ideal ratio mask}
}

@article{10.1109/TASLP.2016.2519146,
author = {Liu, Xin and Bao, Changchun},
title = {Audio Bandwidth Extension Based on Ensemble Echo State Networks with Temporal Evolution},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2519146},
doi = {10.1109/TASLP.2016.2519146},
abstract = {The bandwidth limitation of wideband audio systems degrades the subjective quality and naturalness of audio signals. In this paper, a new method for blind bandwidth extension of wideband audio signals is proposed based on ensemble echo state network with temporal evolution. The high-frequency components in the band of 7 ∼ 14 kHz are artificially restored only from the information in the wideband audio. For each region in the wideband feature space, a specific echo state network with recurrent structure is explored to dynamically model the local mapping relationship between wideband audio features and high-frequency spectral envelope. The transition process among regions is modeled by a hidden Markov model, and a network ensemble technique based on temporal evolution is used to fuse multiple echo state networks such that the high-frequency spectral envelope is estimated. Combining the high-frequency fine spectrum extended by spectral translation, the proposed method can effectively extend the wideband audio to super wideband. In addition, the proposed extension method is applied to the ITU-T G.729.1 wideband audio codec and is further evaluated in comparison with the ITU-T G.729.1 Annex E super-wideband audio codec and the hidden Markov model-based reference bandwidth extension method. Objective quality evaluation results indicate that the proposed method is preferred over the hidden Markov model-based reference bandwidth extension method in terms of log spectral distortion, cosh measure, and differential log spectral distortion. Further, the proposed method improves the auditory quality of the wideband audio and also gains a good performance in the subjective listening tests.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {594–607},
numpages = {14},
keywords = {hidden Markov model, audio coding, echo state network, audio bandwidth extension}
}

@article{10.1109/TASLP.2016.2514496,
author = {Hadad, Elior and Doclo, Simon and Gannot, Sharon},
title = {The Binaural LCMV Beamformer and Its Performance Analysis},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2514496},
doi = {10.1109/TASLP.2016.2514496},
abstract = {The recently proposed binaural linearly constrained minimum variance (BLCMV) beamformer is an extension of the well-known binaural minimum variance distortionless response (MVDR) beamformer, imposing constraints for both the desired and the interfering sources. Besides its capabilities to reduce interference and noise, it also enables to preserve the binaural cues of both the desired and interfering sources, hence making it particularly suitable for binaural hearing aid applications. In this paper, a theoretical analysis of the BLCMV beamformer is presented. In order to gain insights into the performance of the BLCMV beamformer, several decompositions are introduced that reveal its capabilities in terms of interference and noise reduction, while controlling the binaural cues of the desired and the interfering sources. When setting the parameters of the BLCMV beamformer, various considerations need to be taken into account, e.g. based on the amount of interference and noise reduction and the presence of estimation errors of the required relative transfer functions (RTFs). Analytical expressions for the performance of the BLCMV beamformer in terms of noise reduction, interference reduction, and cue preservation are derived. Comprehensive simulation experiments, using measured acoustic transfer functions as well as real recordings on binaural hearing aids, demonstrate the capabilities of the BLCMV beamformer in various noise environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {543–558},
numpages = {16},
keywords = {noise reduction, LCMV beamformer, relative transfer function, binaural cues, hearing aids}
}

@article{10.1109/TASLP.2015.2511623,
author = {Nagathil, Anil and Weihs, Claus and Martin, Rainer},
title = {Spectral Complexity Reduction of Music Signals for Mitigating Effects of Cochlear Hearing Loss},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2511623},
doi = {10.1109/TASLP.2015.2511623},
abstract = {In this paper we study reduced-rank approximations of music signals in the constant-Q spectral domain as a means to reduce effects stemming from cochlear hearing loss. The rationale behind computing reduced-rank approximations is that they allow to reduce the spectral complexity of a music signal. The method is motivated by studies with cochlear implant listeners which have shown that solo instrumental music or music remixed at higher signal-to-interference ratios are preferred over complex music ensembles or orchestras. For computing the reduced-rank approximations we investigate methods based on principal component analysis and partial least squares analysis, and compare them to source separation algorithms. The strategies, which are applied to music with a predominant leading voice, are compared in terms of their ability for mitigating effects of simulated reduced frequency selectivity and with respect to source signal distortions. Established instrumental measures and a newly developed measure indicate a considerable reduction of the auditory distortion resulting from cochlear hearing loss. Furthermore, a listening test reveals a significant preference for the reduced-rank approximations in terms of melody clarity and ease of listening.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {445–458},
numpages = {14},
keywords = {dimensionality reduction, principal component analysis, cochlear hearing loss, music signal processing}
}

@article{10.1109/TASLP.2016.2515502,
author = {Schneider, Martin and Kellermann, Walter},
title = {Multichannel Acoustic Echo Cancellation in the Wave Domain with Increased Robustness to Nonuniqueness},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2016.2515502},
doi = {10.1109/TASLP.2016.2515502},
abstract = {Acoustic echo cancellation (AEC) is significantly more challenging in the case of multichannel reproduction compared to the single-channel case. This is mainly due to the so-called nonuniqueness problem, which becomes especially severe for massive multichannel reproduction systems like, e.g., wave field synthesis systems or higher-order ambisonics, where the different loudspeaker signals are often strongly correlated. In this paper, a multichannel wave-domain AEC structure is proposed that maintains robustness without altering the loudspeaker signals, as is commonly required to preserve the reproduction quality. The proposed method exploits the dominance of certain couplings in the wave-domain representation of the loudspeaker-enclosure-microphone system. In this representation, preferring a strong weight of the dominant couplings in the identified system improves the system identification performance. A modification of the well-known generalized frequency-domain adaptive filtering algorithm is then presented to implement the proposed approach, including a self-contained description of all necessary components. An experimental evaluation compares the proposed method to competing approaches and shows that the proposed method compares well in terms of convergence behavior while avoiding the performance degradation, which is intrinsic to other approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {518–529},
numpages = {12},
keywords = {signal processing, echo cancellers, adaptive signal processing, adaptive filters, active noise reduction, acoustic signal processing}
}

@article{10.1109/TASLP.2015.2509257,
author = {Dong, Li and Wei, Furu and Xu, Ke and Liu, Shixia and Zhou, Ming},
title = {Adaptive Multi-Compositionality for Recursive Neural Network Models},
year = {2016},
issue_date = {March 2016},
publisher = {IEEE Press},
volume = {24},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2509257},
doi = {10.1109/TASLP.2015.2509257},
abstract = {Recursive neural network models have achieved promising results in many natural language processing tasks. The main difference among these models lies in the composition function, i.e., how to obtain the vector representation for a phrase or sentence using the representations of words it contains. This paper introduces a novel Adaptive Multi-Compositionality (AdaMC) layer to recursive neural network models. The basic idea is to use more than one composition function and adaptively select them depending on input vectors. We develop a general framework to model the semantic composition as a distribution of these composition functions. The composition functions and parameters used for adaptive selection are jointly learnt from the supervision of specific tasks. We integrate AdaMC into existing recursive neural network models and conduct extensive experiments on the Stanford Sentiment Treebank and semantic relation classification task. The experimental results demonstrate that AdaMC improves the performance of recursive neural network models and outperforms the baseline methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {422–431},
numpages = {10},
keywords = {neural network, compositionality, semantic composition, recursive neural network (RNN)}
}

@article{10.1109/TASLP.2015.2504866,
author = {Pan, Chao and Benesty, Jacob and Chen, Jingdong},
title = {Design of Directivity Patterns with a Unique Null of Maximum Multiplicity},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2504866},
doi = {10.1109/TASLP.2015.2504866},
abstract = {Differential beamforming is one of the most popular beamforming approaches, which has the great potential to form frequency-invariant directivity patterns. In this paper, we study the design of beampatterns with multiple nulls in the same direction, which is clearly different from the design of beampatterns with distinct nulls. Our contributions are as follows. First, we show how to constrain multiple nulls to the same direction and design the desired beampattern with both the traditional and robust approaches. Second, we derive an explicit form of the white noise gain (WNG) of the traditional approach as a function of the frequency, interelement spacing, and null direction, which shows that the cardioid is the optimal beampattern as far as the WNG is concerned. Third, we prove that the WNG improvement of the robust approach rarely depends on the null direction at low frequencies. Finally, considering the fact that the robust differential beamforming approach may produce a frequency-dependent beampattern while improving the WNG, we develop a weighted-norm approach that can make a good compromise between the robustness of differential beamforming with respect to white noise and the frequency-invariant beampattern. The performance of the developed approach is verified by simulations.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {226–235},
numpages = {10},
keywords = {differential microphone arrays, microphone arrays, beampattern design, beamforming, directivity factor, white noise gain}
}

@article{10.1109/TASLP.2015.2501724,
author = {Mohammadiha, Nasser and Doclo, Simon},
title = {Speech Dereverberation Using Non-Negative Convolutive Transfer Function and Spectro-Temporal Modeling},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2501724},
doi = {10.1109/TASLP.2015.2501724},
abstract = {This paper presents two single-channel speech dereverberation methods to enhance the quality of speech signals that have been recorded in an enclosed space. For both methods, the room acoustics are modeled using a non-negative approximation of the convolutive transfer function (N-CTF), and to additionally exploit the spectral properties of the speech signal, such as the low-rank nature of the speech spectrogram, the speech spectrogram is modeled using non-negative matrix factorization (NMF). Two methods are described to combine the N-CTF and NMF models. In the first method, referred to as the integrated method, a cost function is constructed by directly integrating the speech NMF model into the N-CTF model, while in the second method, referred to as the weighted method, the N-CTF and NMF based cost functions are weighted and summed. Efficient update rules are derived to solve both optimization problems. In addition, an extension of the integrated method is presented, which exploits the temporal dependencies of the speech signal. Several experiments are performed on reverberant speech signals with and without background noise, where the integrated method yields a considerably higher speech quality than the baseline N-CTF method and a state-of-the-art spectral enhancement method. Moreover, the experimental results indicate that the weighted method can even lead to a better performance in terms of instrumental quality measures, but that the optimal weighting parameter depends on the room acoustics and the utilized NMF model. Modeling the temporal dependencies in the integrated method was found to be useful only for highly reverberant conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {276–289},
numpages = {14},
keywords = {non-negative matrix factorization, spectral modeling, non-negative convolutive transfer function, speech dereverberation}
}

@article{10.1109/TASLP.2015.2506263,
author = {Koutrouvelis, Andreas I. and Kafentzis, George P. and Gaubitch, Nikolay D. and Heusdens, Richard},
title = {A Fast Method for High-Resolution Voiced/Unvoiced Detection and Glottal Closure/Opening Instant Estimation of Speech},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2506263},
doi = {10.1109/TASLP.2015.2506263},
abstract = {We propose a fast speech analysis method which simultaneously performs high-resolution voiced/unvoiced detection (VUD) and accurate estimation of glottal closure and glottal opening instants (GCIs and GOIs, respectively). The proposed algorithm exploits the structure of the glottal flow derivative in order to estimate GCIs and GOIs only in voiced speech using simple time-domain criteria. We compare our method with well-known GCI/GOI methods, namely, the dynamic programming projected phase-slope algorithm (DYPSA), the yet another GCI/GOI algorithm (YAGA) and the speech event detection using the residual excitation and a mean-based signal (SEDREAMS). Furthermore, we examine the performance of the aforementioned methods when combined with state-of-the-art VUD algorithms, namely, the robust algorithm for pitch tracking (RAPT) and the summation of residual harmonics (SRH). Experiments conducted on the APLAWD and SAM databases show that the proposed algorithm outperforms the state-of-the-art combinations of VUD and GCI/GOI algorithms with respect to almost all evaluation criteria for clean speech. Experiments on speech contaminated with several noise types (white Gaussian, babble, and car-interior) are also presented and discussed. The proposed algorithm outperforms the state-of-the-art combinations in most evaluation criteria for signal-to-noise ratio greater than 10 dB.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {316–328},
numpages = {13},
keywords = {pitch estimation, speech analysis, glottal opening instants (GOIs), voiced/unvoiced detection (VUD), glottal closure instants (GCIs)}
}

@article{10.1109/TASLP.2015.2507940,
author = {Schepker, Henning and Doclo, Simon},
title = {A Semidefinite Programming Approach to Min-Max Estimation of the Common Part of Acoustic Feedback Paths in Hearing Aids},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2507940},
doi = {10.1109/TASLP.2015.2507940},
abstract = {The convergence speed and the computational complexity of adaptive feedback cancellation algorithms both depend on the number of adaptive parameters used to model the acoustic feedback paths. To reduce the number of adaptive parameters it has been proposed to decompose the acoustic feedback paths as the convolution of a time-invariant common part and time-varying variable parts. Instead of estimating all parameters of the common and variable parts by minimizing the misalignment using a least-squares cost function, in this paper we propose to formulate the parameter estimation problem as a min-max optimization problem aiming to maximize the maximum stable gain (MSG). We formulate the min-max optimization problem as a semidefinite program and use a constraint based on Lyapunov theory to guarantee stability of the estimated common pole-zero filter. Experimental results using measured acoustic feedback paths show that the proposed min-max optimization outperforms least-squares optimization in terms of the MSG. Furthermore, the results indicate that the proposed common part decomposition is able to increase the MSG and reduce the number of variable part parameters even for unknown feedback paths that were not included in the optimization. Simulation results using an adaptive feedback cancellation algorithm based on the prediction-error-method show that the convergence speed can be increased by using the proposed feedback path decomposition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {366–377},
numpages = {12},
keywords = {maximum stable gain, hearing aids, min-max optimization, common part modeling, acoustic feedback cancellation}
}

@article{10.1109/TASLP.2015.2505415,
author = {Zhang, Xiao-Lei and Wang, DeLiang},
title = {Boosting Contextual Information for Deep Neural Network Based Voice Activity Detection},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2505415},
doi = {10.1109/TASLP.2015.2505415},
abstract = {Voice activity detection (VAD) is an important topic in audio signal processing. Contextual information is important for improving the performance of VAD at low signal-to-noise ratios. Here we explore contextual information by machine learning methods at three levels. At the top level, we employ an ensemble learning framework, named multi-resolution stacking (MRS), which is a stack of ensemble classifiers. Each classifier in a building block inputs the concatenation of the predictions of its lower building blocks and the expansion of the raw acoustic feature by a given window (called a resolution). At the middle level, we describe a base classifier in MRS, named boosted deep neural network (bDNN). bDNN first generates multiple base predictions from different contexts of a single frame by only one DNN and then aggregates the base predictions for a better prediction of the frame, and it is different from computationally-expensive boosting methods that train ensembles of classifiers for multiple base predictions. At the bottom level, we employ the multi-resolution cochleagram feature, which incorporates the contextual information by concatenating the cochleagram features at multiple spectrotemporal resolutions. Experimental results show that the MRS-based VAD outperforms other VADs by a considerable margin. Moreover, when trained on a large amount of noise types and a wide range of signal-to-noise ratios, the MRS-based VAD demonstrates surprisingly good generalization performance on unseen test scenarios, approaching the performance with noise-dependent training.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {252–264},
numpages = {13},
keywords = {noise-independent training, deep neural network, cochleagram, multi-resolution stacking, voice activity detection, ensemble learning}
}

@article{10.1109/TASLP.2015.2506269,
author = {Wu, Xiaoguang and Chen, Huawei},
title = {Directivity Factors of the First-Order Steerable Differential Array with Microphone Mismatches: Deterministic and Worst-Case Analysis},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2506269},
doi = {10.1109/TASLP.2015.2506269},
abstract = {It is known that differential microphone arrays are sensitive to microphone mismatches. This paper studies the effects of microphone mismatches on the directivity factor (DF) of the first-order steerable differential array (FOSDA) whose response is constructed by a linear combination of monopole and two orthogonal dipoles via a small four-microphone array. In particular, the DF of the FOSDA is studied from two perspectives, i.e., a deterministic analysis for deterministic microphone gain/phase errors, and a worst-case analysis for uncertain microphone gain/phase errors. Based on the theoretical analysis, several interesting properties are revealed, which are helpful to better understand the robustness characteristics of the FOSDA. Moreover, by worst-case analysis, it provides a useful guidance for robust FOSDA design, i.e., how to determine array size and select microphones with required characteristics such that a desired DF value can be guaranteed. In addition, the effect of sensor self-noise on the DF of the FOSDA is also discussed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {300–315},
numpages = {16},
keywords = {steer-able differential microphone array, microphone mismatch, superdirective beamforming, directivity factor}
}

@article{10.1109/TASLP.2015.2509241,
author = {Bentivogli, Luisa and Bertoldi, Nicola and Cettolo, Mauro and Federico, Marcello and Negri, Matteo and Turchi, Marco},
title = {On the Evaluation of Adaptive Machine Translation for Human Post-Editing},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2509241},
doi = {10.1109/TASLP.2015.2509241},
abstract = {We investigate adaptive machine translation (MT) as a way to reduce human workload and enhance user experience when professional translators operate in real-life conditions. A crucial aspect in our analysis is how to ensure a reliable assessment of MT technologies aimed to support human post-editing. We pay particular attention to two evaluation aspects: i) the design of a sound experimental protocol to reduce the risk of collecting biased measurements, and ii) the use of robust statistical testing methods (linear mixed-effects models) to reduce the risk of under/over-estimating the observed variations. Our adaptive MT technology is integrated in a web-based full-fledged computer-assisted translation (CAT) tool. We report on a post-editing field test that involved 16 professional translators working on two translation directions (English-Italian and English-French), with texts coming from two linguistic domains (legal, information technology). Our contrastive experiments compare user post-editing effort with static vs. adaptive MT in an end-to-end scenario where the system is evaluated as a whole. Our results evidence that adaptive MT leads to an overall reduction in post-editing effort (HTER) up to 10.6% (p < 0.05). A follow-up manual evaluation of the MT outputs and their corresponding post-edits confirms that the gain in HTER corresponds to higher quality of the adaptive MT system and does not come at the expense of the final human translation quality. Indeed, adaptive MT shows to return better suggestions than static MT (p < 0.01), and the resulting post-edits do not significantly differ in the two conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {388–399},
numpages = {12},
keywords = {machine translation, computer-assisted translation, post-editing, human in the loop evaluation, online adaptation, domain adaptation}
}

@article{10.1109/TASLP.2015.2506264,
author = {Sharma, Anil and Kaul, Sanjit},
title = {Two-Stage Supervised Learning-Based Method to Detect Screams and Cries in Urban Environments},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2506264},
doi = {10.1109/TASLP.2015.2506264},
abstract = {Smartphones can enable monitoring signs of distress as a human goes about his daily routine. Motivated by this possibility of 24x7 distress detection, we investigate detection of screaming and crying in urban environments, which we categorize into the contexts of indoors (home and office), outdoors, human conversation, large human gatherings, machinery, and audio from multimedia devices. Prior works are often restricted to specific environments or controlled settings. We propose a novel two-stage supervised learning based method, with tunable decision parameters for each stage, to achieve a desired true distress (scream and cry) detection rate (DR) and false alarm rate (FAR). We observe that the choice of the parameters is a function of the signal-to-noise ratio (SNR) of the distress signal, which is the ratio of the power of the distress signal to the power of the context audio. In the absence of SNR information, we show that a simple SNR estimation scheme performs well. Alternately, we show how the decision parameters can be selected based on the context estimated by the method. We show the results of testing the proposal over hundred hours of audio data recorded by the smartphones of ten volunteers as they went about their daily routines. Achieved performance is exemplified by a DR of 93.16% and a FAR of 4.76% at a SNR of 20 dB. The corresponding values for a SNR of 10 dB are 84.13% and 4.77%. Finally, we compare with, which also deals with audio event detection in noisy environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {290–299},
numpages = {10},
keywords = {scream detection, urban environment, cry detection, MFCC, smartphone, context estimation, SVM}
}

@article{10.1109/TASLP.2015.2507862,
author = {Nakamura, Tomohiko and Nakamura, Eita and Sagayama, Shigeki},
title = {Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2507862},
doi = {10.1109/TASLP.2015.2507862},
abstract = {This paper discusses real-time alignment of audio signals of music performance to the corresponding score (a.k.a. score following) which can handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips) in performances. This type of score following is particularly useful in automatic accompaniment for practices and rehearsals, where errors and repeats/skips are often made. Simple extensions of the algorithms previously proposed in the literature are not applicable in these situations for scores of practical length due to the problem of large computational complexity. To cope with this problem, we present two hidden Markov models of monophonic performance with errors and arbitrary repeats/skips, and derive efficient score-following algorithms with an assumption that the prior probability distributions of score positions before and after repeats/skips are independent from each other. We confirmed real-time operation of the algorithms with music scores of practical length (around 10000 notes) on a modern laptop and their tracking ability to the input performance within 0.7 s on average after repeats/skips in clarinet performance data. Further improvements and extension for polyphonic signals are also discussed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {329–339},
numpages = {11},
keywords = {score following, arbitrary repeats and skips, audio-to-score alignment, fast Viterbi algorithm, hidden Markov model, music signal processing}
}

@article{10.1109/TASLP.2015.2509780,
author = {Lee, Bong-Ki and Chang, Joon-Hyuk},
title = {Packet Loss Concealment Based on Deep Neural Networks for Digital Speech Transmission},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2509780},
doi = {10.1109/TASLP.2015.2509780},
abstract = {In this paper, we propose the regression-based packet loss concealment (PLC) for digital speech transmission by using deep neural networks (DNNs) with a multiple-layer deep architecture. For the DNN training, log-power spectra and phases are employed as features in the input layer for the large training set, which ensures non-linear mapping the frames from the last correctly received frame to the missing frame. Once the training is accomplished by the restricted Boltzmann machine (RBM)-based pre-training to initialize the DNN, minimum mean square error (MMSE)-based fine tuning is then performed based on the back-propagation algorithm. In the reconstruction stage, the trained DNN model is fed with the features of the previous frames in order to estimate the log-power spectra and phases of the missing frames. Reconstruction is further improved by using the cross-fading technique to mitigate discontinuity between the reconstruction signal and good frame signal in the time-domain. To demonstrate the performance of the proposed algorithm, hidden Markov model (HMM)-based PLC algorithm and the PLC algorithm standardized in adaptive multi-rate wideband (AMR-WB) Appendix I were used for comparison. The experimental results show that the proposed approach provides better speech quality and speech recognition accuracy than the conventional approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {378–387},
numpages = {10},
keywords = {speech quality, packet loss concealment (PLC), adaptive multi-rate wideband, deep neural network (DNN), regression model, network speech recognition}
}

@article{10.1109/TASLP.2015.2496156,
author = {Bahne, Adrian and Ahl\'{e}n, Anders},
title = {Optimizing the Similarity of Loudspeaker-Room Responses in Multiple Listening Positions},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2496156},
doi = {10.1109/TASLP.2015.2496156},
abstract = {A shortcoming of multichannel sound reproduction standards, such as stereo or 5.1 surround, is their incompatibility with multiple off-axis listening positions. Accurate reproduction of virtual sound sources can only be experienced in the sweet spot, which is located equidistant to the loudspeakers. We here present a novel methodology to compensate audio systems such that the channel similarity is optimized in several listening positions simultaneously. To that end we propose a novel MIMO personal audio filter design framework based on feed-forward control. By proper design choices, filters that successfully compensate for multiple off-axis positions and irregularities in the frequency sum responses are obtained. The design choices include allpass filters with appropriate phase shifts as target for each listening position in addition to a weighted similarity requirement. Evaluations based on measurements of two four-channel car audio systems show that the proposed method significantly improves timbral sound reproduction and phantom center reproduction in several listening positions simultaneously.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {340–353},
numpages = {14},
keywords = {virtual center, psychoacoustics, audio systems, loudspeakers, multiple sweet spots, acoustic signal processing, equalizers}
}

@article{10.1109/TASLP.2015.2504781,
author = {Hung, Jeih-Weih and Hsieh, Hsin-Ju and Chen, Berlin},
title = {Robust Speech Recognition via Enhancing the Complex-Valued Acoustic Spectrum in Modulation Domain},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2504781},
doi = {10.1109/TASLP.2015.2504781},
abstract = {The purpose of this paper is to develop a novel speech feature extraction framework for independently compensating the real and imaginary acoustic spectra of speech signals in the modulation domain with the techniques of histogram equalization (HEQ) and non-negative matrix factorization (NMF). By doing so, we can enhance not only the magnitude but also the phase components of the acoustic spectra, thereby creating noise-robust speech features. More specifically, the proposed framework makes the following three major contributions: First, via either of the HEQ and NMF operations, the long-term cross-frame correlation among the acoustic spectra at the same frequency can be captured to compensate for the spectral distortion caused by noise. Second, the noise effect can be handled in a high acoustic frequency resolution. Finally, the distortion dwelt in the acoustic spectra can be more extensively mitigated due to the independent processes for the respective real and imaginary parts. The evaluation experiments were carried out on the Aurora-2 and Aurora-4 benchmark tasks, and the corresponding results suggest that our proposed methods can achieve performance competitive to or better than many widely used noise robustness methods, including the well-known advanced front-end (AFE) extraction scheme, in speech recognition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {236–251},
numpages = {16},
keywords = {histogram equalization (HEQ), feature extraction, noise robustness, modulation spectrum, non-negative matrix factorization (NMF), automatic speech recognition (ASR)}
}

@article{10.1109/TASLP.2015.2504874,
author = {Rasumow, Eugen and Hansen, Martin and van de Par, Steven and P\"{u}schel, Dirk and Mellert, Volker and Doclo, Simon and Blau, Matthias},
title = {Regularization Approaches for Synthesizing HRTF Directivity Patterns},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2504874},
doi = {10.1109/TASLP.2015.2504874},
abstract = {As an alternative to traditional artificial heads, it is possible to synthesize individual head-related transfer functions (HRTFs) using a so-called virtual artificial head (VAH), consisting of a microphone array with an appropriate topology and filter coefficients optimized using a narrowband least squares cost function. The resulting spatial directivity pattern of such a VAH is known to be sensitive to small deviations of the assumed microphone characteristics, e.g., gain, phase and/or the positions of the microphones. In many beamformer design procedures, this sensitivity is reduced by imposing a white noise gain (WNG) constraint on the filter coefficients for a single desired look direction. In this paper, this constraint is shown to be inappropriate for regularizing the HRTF synthesis with multiple desired directions and three alternative different regularization approaches are proposed and evaluated. In the first approach, the measured deviations of the microphone characteristics are taken into account in the filter design. In the second approach, the filter coefficients are regularized using the mean WNG for all directions. The third approach additionally takes into account several frequency bins into both the optimization and the regularization. The different proposed regularization approaches are compared using analytic and measured transfer functions, including random deviations. Experimental results show that the approach using multiple frequency bands mimicking the spectral resolution of the human auditory system yields the best robustness among the considered regularization approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {215–225},
numpages = {11},
keywords = {virtual artificial head, white noise gain (WNG), beamforming, head-related transfer functions (HRTFs), regularization}
}

@article{10.1109/TASLP.2015.2499040,
author = {Turan, M.A. Tu\u{g}tekin and Erzin, Engin},
title = {Source and Filter Estimation for Throat-Microphone Speech Enhancement},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2499040},
doi = {10.1109/TASLP.2015.2499040},
abstract = {In this paper, we propose a new statistical enhancement system for throat microphone recordings through source and filter separation. Throat microphones (TM) are skin-attached piezoelectric sensors that can capture speech sound signals in the form of tissue vibrations. Due to their limited bandwidth, TM recorded speech suffers from intelligibility and naturalness. In this paper, we investigate learning phone-dependent Gaussian mixture model (GMM)-based statistical mappings using parallel recordings of acoustic microphone (AM) and TM for enhancement of the spectral envelope and excitation signals of the TM speech. The proposed mappings address the phone-dependent variability of tissue conduction with TM recordings. While the spectral envelope mapping estimates the line spectral frequency (LSF) representation of AM from TM recordings, the excitation mapping is constructed based on the spectral energy difference (SED) of AM and TM excitation signals. The excitation enhancement is modeled as an estimation of the SED features from the TM signal. The proposed enhancement system is evaluated using both objective and subjective tests. Objective evaluations are performed with the log-spectral distortion (LSD), the wideband perceptual evaluation of speech quality (PESQ) and mean-squared error (MSE) metrics. Subjective evaluations are performed with an A/B comparison test. Experimental results indicate that the proposed phone-dependent mappings exhibit enhancements over phone-independent mappings. Furthermore enhancement of the TM excitation through statistical mappings of the SED features introduces significant objective and subjective performance improvements to the enhancement of TM recordings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {265–275},
numpages = {11},
keywords = {statistical mapping, throat microphone, gaussian mixture model, speech enhancement}
}

@article{10.1109/TASLP.2015.2507858,
author = {Kates, James M. and Arehart, Kathryn H.},
title = {The Hearing-Aid Audio Quality Index (HAAQI)},
year = {2016},
issue_date = {February 2016},
publisher = {IEEE Press},
volume = {24},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2507858},
doi = {10.1109/TASLP.2015.2507858},
abstract = {This paper presents an index designed to predict music quality for individuals listening through hearing aids. The index is "intrusive," that is, it compares the degraded signal being evaluated to a reference signal. The index is based on a model of the auditory periphery that includes the effects of hearing loss. Outputs from the auditory model are used to measure changes in the signal time-frequency envelope modulation, temporal fine structure, and long-term spectrum caused by the hearing aid processing. The index is constructed by combining a term sensitive to noise and nonlinear distortion with a second term sensitive to changes in the long-term spectrum. The index is fitted to an existing database of music quality judgments made by listeners having normal or impaired hearing. The data comprise ratings for three music excerpts (classical orchestra, jazz trio, and jazz singer), each processed through 100 conditions representative of hearing-aid processing and listening situations. The overall accuracy of the index is high, with a correlation coefficient of 0.970 when computed over all of the processing conditions and averaged over the combined groups of listeners having normal and impaired hearing.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {354–365},
numpages = {12},
keywords = {objective audio quality measures, music quality measures, hearing aids, hearing loss}
}

@article{10.1109/TASLP.2015.2500732,
author = {Torbati, Amir H. Harati Nejad and Picone, Joseph},
title = {A Doubly Hierarchical Dirichlet Process Hidden Markov Model with a Non-Ergodic Structure},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2500732},
doi = {10.1109/TASLP.2015.2500732},
abstract = {Nonparametric Bayesian models use a Bayesian framework to learn model complexity automatically from the data, eliminating the need for a complex model selection process. A Hierarchical Dirichlet Process Hidden Markov Model (HDPHMM) is the nonparametric Bayesian equivalent of a hidden Markov model (HMM), but is restricted to an ergodic topology that uses a Dirichlet Process Model to achieve a mixture distribution-like model. For applications involving ordered sequences (e.g., speech recognition), it is desirable to impose a left-to-right structure on the model. In this paper, we introduce a model based on HDPHMM that: 1) shares data points between states, 2) models non-ergodic structures, and 3) models non-emitting states. The first point is particularly important because Gaussian mixture models, which support such sharing, have been very effective at modeling modalities in a signal (e.g., speaker variability). Further, sharing data points allows models to be estimated more accurately, an important consideration for applications such as speech recognition in which some mixture components occur infrequently. We demonstrate that this new model produces a 20% relative reduction in error rate for phoneme classification and an 18% relative reduction on a speech recognition task on the TIMIT Corpus compared to a baseline system consisting of a parametric HMM.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {174–184},
numpages = {11},
keywords = {nonparametric bayesian models, speech recognition, hierarchical dirichlet processes, hidden markov models}
}

@article{10.1109/TASLP.2015.2500728,
author = {Torres, Humberto and Gurlekian, Jorge},
title = {Novel Estimation Method for the Superpositional Intonation Model},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2500728},
doi = {10.1109/TASLP.2015.2500728},
abstract = {Fujisaki's intonation model parameterizes the F0's contour efficiently and because of its strong physiological basis has been successfully tested in different languages. One problem that has not been fully addressed is the extraction of the model's parameters, i.e., given a sentence, which model's parameter values best describe its intonation. Most of the proposed methods strive to optimize the parameters so as to obtain the best fit for the F0 contour globally. In this paper we propose to use text information from the sentence as the main guide or reference for adjusting the parameters. We present a method that defines a set of rules to fix and optimize the model's parameters. Optimization never loses sight of the text structure events that arouse it. When text information is not enough, the algorithm predicts parameters from F0 contour and tie them to the text. The process of parameter estimation can be seen as a way to go from text information to the F0 contour. Parameter optimization is carried out to fit the F0 contour locally. Our novel approach can be implemented manually or automatically. We present examples of manual implementation and the quantitative results of the automatic one. Tested on three corpora in Spanish, English and German, our automatic method shows a performance of 34% better than other tested methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {151–160},
numpages = {10},
keywords = {Fujisaki's intonational model, superpositional intonational model, model estimation}
}

@article{10.1109/TASLP.2015.2500028,
author = {Anderson, Craig A. and Teal, Paul D. and Poletti, Mark A.},
title = {Spatial Correlation of Radial Gaussian and Uniform Spherical Volume Near-Field Source Distributions},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2500028},
doi = {10.1109/TASLP.2015.2500028},
abstract = {In this paper, a pair of analytic expressions describing the correlation functions due to spherically symmetric radial Gaussian and uniform volume near-field source position distributions are presented. An approximate spatial correlation function solution for a radial Gaussian source location distribution is derived and compared with the existing numerical methods. An exact solution for a uniform volume source location distribution is also derived and compared with existing numerical methods. The approximate radial Gaussian solution produces a result consistent with numerical methods for compact source location distributions. The uniform volume solution matches the expected behavior. Finally, the spatial correlation functions were used to design spatially robust beamformers for compact microphone arrays. Both of the spatial correlation function solutions lead to improved spatial robustness for the applications of signal enhancement and suppression.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {143–150},
numpages = {8},
keywords = {spatial correlation functions, spatial robustness, beamforming}
}

@article{10.1109/TASLP.2015.2487051,
author = {Tahon, Marie and Devillers, Laurence},
title = {Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2487051},
doi = {10.1109/TASLP.2015.2487051},
abstract = {The search of a small acoustic feature set for emotion recognition faces three main challenges. Such a feature set must be robust to large diversity of contexts in real-life applications; model parameters must also be optimized for reduced subsets; finally, the result of feature selection must be evaluated in cross-corpus condition. The goal of the present study is to select a consensual set of acoustic features for valence recognition using classification and non-classification based feature ranking and cross-corpus experiments, and to optimize emotional models simultaneously. Five realistic corpora are used in this study: three of them were collected in the framework of the French project on robotics ROMEO, one is a game corpus (JEMO) and one is the well-known AIBO corpus. Combinations of features found with nonclassification based methods (information gain and Gaussian mixture models with Bhattacharyya distance) through multicorpora experiments are tested under cross-corpus conditions, simultaneously with SVM parameters optimization. Reducing the number of features goes in pair with optimizing model parameters. Experiments carried on randomly selected features from two acoustic feature sets show that a feature space reduction is needed to avoid over-fitting. Since a Grid search tends to find non-standard values with small feature sets, the authors propose a multicorpus optimization method based on different corpora and acoustic feature subsets which ensures more stability. The results show that acoustic families selected with both feature ranking methods are not relevant in cross-corpus experiments. Promising results have been obtained with a small set of 24 voiced cepstral coefficients while this family was ranked in the 2nd and 5th positions with both ranking methods. The proposed optimization method is more robust than the usual Grid search for cross-corpus experiments with small feature sets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {16–28},
numpages = {13},
keywords = {information gain, Bhattacharyya distance, acoustic features, SVM parameter optimization, crosscorpus, emotion recognition}
}

@article{10.1109/TASLP.2015.2498101,
author = {Sun, Meng and Zhang, Xiongwei and Van hamme, Hugo and Zheng, Thomas Fang},
title = {Unseen Noise Estimation Using Separable Deep Auto Encoder for Speech Enhancement},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2498101},
doi = {10.1109/TASLP.2015.2498101},
abstract = {Unseen noise estimation is a key yet challenging step to make a speech enhancement algorithm work in adverse environments. At worst, the only prior knowledge we know about the encountered noise is that it is different from the involved speech. Therefore, by subtracting the components which cannot be adequately represented by a well defined speech model, the noises can be estimated and removed. Given the good performance of deep learning in signal representation, a deep auto encoder (DAE) is employed in this work for accurately modeling the clean speech spectrum. In the subsequent stage of speech enhancement, an extra DAE is introduced to represent the residual part obtained by subtracting the estimated clean speech spectrum (by using the pre-trained DAE) from the noisy speech spectrum. By adjusting the estimated clean speech spectrum and the unknown parameters of the noise DAE, one can reach a stationary point to minimize the total reconstruction error of the noisy speech spectrum. The enhanced speech signal is thus obtained by transforming the estimated clean speech spectrum back into time domain. The above proposed technique is called separable deep auto encoder (SDAE). Given the under-determined nature of the above optimization problem, the clean speech reconstruction is confined in the convex hull spanned by a pre-trained speech dictionary. New learning algorithms are investigated to respect the non-negativity of the parameters in the SDAE. Experimental results on TIMIT with 20 noise types at various noise levels demonstrate the superiority of the proposed method over the conventional baselines.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {93–104},
numpages = {12},
keywords = {deep auto encoder, source separation, unseen noise compensation, speech enhancement}
}

@article{10.1109/TASLP.2015.2495249,
author = {Ardekani, Iman Tabatabaei and Kaipio, Jari P. and Nasiri, Alireza and Sharifzadeh, Hamid and Abdulla, Waleed H.},
title = {A Statistical Inverse Problem Approach to Online Secondary Path Modeling in Active Noise Control},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2495249},
doi = {10.1109/TASLP.2015.2495249},
abstract = {This paper recasts the problem of online secondary path modeling in the form of a statistical inverse problem. A statistical and, in particular, a Bayesian approach towards secondary path modeling is developed and the computational issues that emerge from this approach are discussed. All signals and parameters are modeled as random variables and the degree of information concerning them is coded in their probability density functions. An abstract solution is formulated in the form of a probability density function for the secondary path model. For extracting point estimates, common statistical estimation methods are investigated. It is shown that maximum likelihood estimation is not stable; however, Bayesian method of maximum a posteriori gives a reliable solution. An adaptive algorithm is then developed to compute this solution in a computationally efficient manner. This algorithm has three advantages, compared to the traditional secondary path modeling algorithms. First, it does not cause any interference with the main active noise control algorithm. Second, it does not require any additive-noise to be injected into the secondary path. Third, it does not require any off-line initiation. The convergence of the proposed algorithm is analyzed theoretically. The validity of the theoretical results is investigated by using computer simulation. Finally successful integration of the proposed algorithm into a real-time ANC system is reported.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {54–64},
numpages = {11},
keywords = {active noise control (ANC), bayesian estimation, statistical inverse problems, online secondary path modeling}
}

@article{10.1109/TASLP.2015.2497798,
author = {Adalbj\"{o}rnsson, Stefan I. and Kronvall, Ted and Burgess, Simon and \r{A}str\"{o}m, Kalle and Jakobsson, Andreas},
title = {Sparse Localization of Harmonic Audio Sources},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2497798},
doi = {10.1109/TASLP.2015.2497798},
abstract = {In this paper, we propose a novel method for estimating the locations of near- and/or far-field harmonic audio sources impinging on an arbitrary, but calibrated, sensor array. Using a joint pitch and location estimation formed in two steps, we first estimate the fundamental frequencies and complex amplitudes under a sinusoidal model assumption, whereafter the location of each source is found by utilizing both the difference in phase and the relative attenuation of the magnitude estimates. As audio recordings often consist of multi-pitch signals exhibiting some degree of reverberation, where both the number of pitches and the source locations are unknown, we propose to use sparse heuristics to avoid the necessity of detailed a priori assumptions on the spectral and spatial model orders. The method's performance is evaluated using both simulated and measured audio data, with the former showing that the proposed method achieves near-optimal performance, whereas the latter confirms the method's feasibility when used with real recordings.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {117–129},
numpages = {13},
keywords = {non-convex sparsity, convex optimization, multi-pitch estimation, TDOA, block sparsity, ADMM, near- and far-field localization}
}

@article{10.1109/TASLP.2015.2497248,
author = {Stafylakis, Themos and Kenny, Patrick and Alam, Md. Jahangir and Kockmann, Marcel},
title = {Speaker and Channel Factors in Text-Dependent Speaker Recognition},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2497248},
doi = {10.1109/TASLP.2015.2497248},
abstract = {We reformulate joint factor analysis so that it can serve as a feature extractor for text-dependent speaker recognition. The new formulation is based on left-to-right modeling with tied mixture HMMs and it is designed to deal with problems such as the inadequacy of subspace methods in modeling speaker-phrase variability, UBM mismatches that arise as a result of variable phonetic content, and the need to exploit text-independent resources in text-dependent speaker recognition. We pass the features extracted by factor analysis to a trainable backend which plays a role analogous to that of PLDA in the i-vector/PLDA cascade in text-independent speaker recognition. We evaluate these methods on a proprietary dataset consisting of English and Urdu passphrases collected in Pakistan. By using both text-independent data and text-dependent data for training purposes and by fusing results obtained with multiple front ends at the score level, we achieved equal error rates of around 1.3% and 2% on the English and Urdu portions of this task.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {65–78},
numpages = {14},
keywords = {factor analysis, speaker recognition}
}

@article{10.1109/TASLP.2015.2496222,
author = {He, Yanzhang and Baumann, Peter and Fang, Hao and Hutchinson, Brian and Jaech, Aaron and Ostendorf, Mari and Fosler-Lussier, Eric and Pierrehumbert, Janet},
title = {Using Pronunciation-Based Morphological Subword Units to Improve OOV Handling in Keyword Search},
year = {2016},
issue_date = {January 2016},
publisher = {IEEE Press},
volume = {24},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2015.2496222},
doi = {10.1109/TASLP.2015.2496222},
abstract = {Out-of-vocabulary (OOV) keywords present a challenge for keyword search (KWS) systems especially in the low-resource setting. Previous research has centered around approaches that use a variety of subword units to recover OOV words. This paper systematically investigates morphology-based subword modeling approaches on seven low-resource languages. We show that using morphological subword units (morphs) in speech recognition decoding is substantially better than expanding word-decoded lattices into subword units including phones, syllables and morphs. As alternatives to grapheme-based morphs, we apply unsupervised morphology learning to sequences of phonemes, graphones, and syllables. Using one of these phone-based morphs is almost always better than using the grapheme-based morphs, but the

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repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr