text:
cochlear implants. In a single speech source scenario, the frequency-domain autocorrelation matrix of the speech signal is often assumed to be a rank-1 matrix, which then allows to derive different rank-1 approximation based noise reduction filters. In practice, however, the rank of the autocorrelation matrix of the speech signal is usually greater than one. Firstly, the link between the different rank-1 approximation based noise reduction filters and the original speech distortion weighted multichannel Wiener filter is investigated when the rank of the autocorrelation matrix of the speech signal is indeed greater than one. Secondly, in low input signal-to-noise-ratio scenarios, due to noise non-stationarity, the estimation of the auto-correlation matrix of the speech signal can be problematic and the noise reduction filters can deliver unpredictable noise reduction performance. An eigenvalue decomposition based filter and a generalized eigenvalue decomposition based filter are introduced that include a more robust rank-1, or more generally rank-R, approximation of the autocorrelation matrix of the speech signal. These noise reduction filters are demonstrated to deliver a better noise reduction performance especially in low input signal-to-noise-ratio scenarios. The filters are especially useful in cochlear implants, where more speech distortion and hence a more aggressive noise reduction can be tolerated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {785–799},
numpages = {15}
}

@article{10.1109/TASLP.2014.2308398,
author = {Zhao, Xiaojia and Wang, Yuxuan and Wang, DeLiang},
title = {Robust Speaker Identification in Noisy and Reverberant Conditions},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2308398},
doi = {10.1109/TASLP.2014.2308398},
abstract = {Robustness of speaker recognition systems is crucial for real-world applications, which typically contain both additive noise and room reverberation. However, the combined effects of additive noise and convolutive reverberation have been rarely studied in speaker identification (SID). This paper addresses this issue in two phases. We first remove background noise through binary masking using a deep neural network classifier. Then we perform robust SID with speaker models trained in selected reverberant conditions, on the basis of bounded marginalization and direct masking. Evaluation results show that the proposed system substantially improves SID performance over related systems in a wide range of reverberation time and signal-to-noise ratios.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {836–845},
numpages = {10},
keywords = {noise, reverberation, ideal binary mask, deep neural network, robust speaker identification}
}

@article{10.1109/TASLP.2014.2308473,
author = {Cumani, Sandro and Plchot, Old\v{r}ich and Laface, Pietro},
title = {On the Use of I-Vector Posterior Distributions in Probabilistic Linear Discriminant Analysis},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2308473},
doi = {10.1109/TASLP.2014.2308473},
abstract = {The i-vector extraction process is affected by several factors such as the noise level, the acoustic content of the observed features, the channel mismatch between the training conditions and the test data, and the duration of the analyzed speech segment. These factors influence both the i-vector estimate and its uncertainty, represented by the i-vector posterior covariance. This paper presents a new PLDA model that, unlike the standard one, exploits the intrinsic i-vector uncertainty. Since the recognition accuracy is known to decrease for short speech segments, and their length is one of the main factors affecting the i-vector covariance, we designed a set of experiments aiming at comparing the standard and the new PLDA models on short speech cuts of variable duration, randomly extracted from the conversations included in the NIST SRE 2010 extended dataset, both from interviews and telephone conversations. Our results on NIST SRE 2010 evaluation data show that in different conditions the new model outperforms the standard PLDA by more than 10% relative when tested on short segments with duration mismatches, and is able to keep the accuracy of the standard model for long enough speaker segments. This technique has also been successfully tested in the NIST SRE 2012 evaluation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {846–857},
numpages = {12},
keywords = {I-vector extraction, I-vectors, probabilistic linear discriminant analysis, speaker recognition}
}

@article{10.1109/TASLP.2014.2305833,
author = {Narayanan, Arun and Wang, DeLiang},
title = {Investigation of Speech Separation as a Front-End for Noise Robust Speech Recognition},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2305833},
doi = {10.1109/TASLP.2014.2305833},
abstract = {Recently, supervised classification has been shown to work well for the task of speech separation. We perform an in-depth evaluation of such techniques as a front-end for noise-robust automatic speech recognition (ASR). The proposed separation front-end consists of two stages. The first stage removes additive noise via time-frequency masking. The second stage addresses channel mismatch and the distortions introduced by the first stage; a non-linear function is learned that maps the masked spectral features to their clean counterpart. Results show that the proposed front-end substantially improves ASR performance when the acoustic models are trained in clean conditions. We also propose a diagonal feature discriminant linear regression (dFDLR) adaptation that can be performed on a per-utterance basis for ASR systems employing deep neural networks and HMM. Results show that dFDLR consistently improves performance in all test conditions. Surprisingly, the best average results are obtained when dFDLR is applied to models trained using noisy log-Mel spectral features from the multi-condition training set. With no channel mismatch, the best results are obtained when the proposed speech separation front-end is used along with multi-condition training using log-Mel features followed by dFDLR adaptation. Both these results are among the best on the Aurora-4 dataset.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {826–835},
numpages = {10},
keywords = {deep neural networks, feature mapping, robust ASR, time-frequency masking, Aurora-4}
}

@article{10.1109/TASLP.2014.2304637,
author = {Li, Jinyu and Deng, Li and Gong, Yifan and Haeb-Umbach, Reinhold},
title = {An Overview of Noise-Robust Automatic Speech Recognition},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2304637},
doi = {10.1109/TASLP.2014.2304637},
abstract = {New waves of consumer-centric applications, such as voice search and voice interaction with mobile devices and home entertainment systems, increasingly require automatic speech recognition (ASR) to be robust to the full range of real-world noise and other acoustic distorting conditions. Despite its practical importance, however, the inherent links between and distinctions among the myriad of methods for noise-robust ASR have yet to be carefully studied in order to advance the field further. To this end, it is critical to establish a solid, consistent, and common mathematical foundation for noise-robust ASR, which is lacking at present. This article is intended to fill this gap and to provide a thorough overview of modern noise-robust techniques for ASR developed over the past 30 years. We emphasize methods that are proven to be successful and that are likely to sustain or expand their future applicability. We distill key insights from our comprehensive overview in this field and take a fresh look at a few old problems, which nevertheless are still highly relevant today. Specifically, we have analyzed and categorized a wide range of noise-robust techniques using five different criteria: 1) feature-domain vs. model-domain processing, 2) the use of prior knowledge about the acoustic environment distortion, 3) the use of explicit environment-distortion models, 4) deterministic vs. uncertainty processing, and 5) the use of acoustic models trained jointly with the same feature enhancement or model adaptation process used in the testing stage. With this taxonomy-oriented review, we equip the reader with the insight to choose among techniques and with the awareness of the performance-complexity tradeoffs. The pros and cons of using different noise-robust ASR techniques in practical application scenarios are provided as a guide to interested practitioners. The current challenges and future research directions in this field is also carefully analyzed.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {745–777},
numpages = {33},
keywords = {robustness, distortion modeling, compensation, uncertainty processing, noise, speech recognition, joint model training}
}

@article{10.1109/TASLP.2014.2310353,
author = {Wu, Chung-Hsien and Shen, Han-Ping and Yang, Yan-Ting},
title = {Chinese-English Phone Set Construction for Code-Switching ASR Using Acoustic and DNN-Extracted Articulatory Features},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2310353},
doi = {10.1109/TASLP.2014.2310353},
abstract = {This study proposes a data-driven approach to phone set construction for code-switching automatic speech recognition (ASR). Acoustic and context-dependent cross-lingual articulatory features (AFs) are incorporated into the estimation of the distance between triphone units for constructing a Chinese-English phone set. The acoustic features of each triphone in the training corpus are extracted for constructing an acoustic triphone HMM. Furthermore, the articulatory features of the "last/first" state of the corresponding preceding/succeeding triphone in the training corpus are used to construct an AF-based GMM. The AFs, extracted using a deep neural network (DNN), are used for code-switching articulation modeling to alleviate the data sparseness problem due to the diverse context-dependent phone combinations in intra-sentential code-switching. The triphones are then clustered to obtain a Chinese-English phone set based on the acoustic HMMs and the AF-based GMMs using a hierarchical triphone clustering algorithm. Experimental results on code-switching ASR show that the proposed method for phone set construction outperformed other traditional methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {858–862},
numpages = {5},
keywords = {phone set construction, code-switching, articulatory features, speech recognition}
}

@article{10.1109/TASLP.2014.2303296,
author = {Sarikaya, Ruhi and Hinton, Geoffrey E. and Deoras, Anoop},
title = {Application of Deep Belief Networks for Natural Language Understanding},
year = {2014},
issue_date = {April 2014},
publisher = {IEEE Press},
volume = {22},
number = {4},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2303296},
doi = {10.1109/TASLP.2014.2303296},
abstract = {Applications of Deep Belief Nets (DBN) to various problems have been the subject of a number of recent studies ranging from image classification and speech recognition to audio classification. In this study we apply DBNs to a natural language understanding problem. The recent surge of activity in this area was largely spurred by the development of a greedy layer-wise pretraining method that uses an efficient learning algorithm called Contrastive Divergence (CD). CD allows DBNs to learn a multi-layer generative model from unlabeled data and the features discovered by this model are then used to initialize a feed-forward neural network which is fine-tuned with backpropagation. We compare a DBN-initialized neural network to three widely used text classification algorithms: Support Vector Machines (SVM), boosting and Maximum Entropy (MaxEnt). The plain DBN-based model gives a call-routing classification accuracy that is equal to the best of the other models. However, using additional unlabeled data for DBN pre-training and combining DBN-based learned features with the original features provides significant gains over SVMs, which, in turn, performed better than both MaxEnt and Boosting.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = apr,
pages = {778–784},
numpages = {7},
keywords = {DBN, deep learning, natural language understanding, call-routing, deep neural nets, RBM}
}

@article{10.1109/TASLP.2014.2300344,
author = {Raczynski, Stanislaw A. and Vincent, Emmanuel},
title = {Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2300344},
doi = {10.1109/TASLP.2014.2300344},
abstract = {In this work we present a new Bayesian topic model: latent hierarchical Pitman-Yor process allocation (LHPYA), which uses hierarchical Pitman-Yor process priors for both word and topic distributions, and generalizes a few of the existing topic models, including the latent Dirichlet allocation (LDA), the bigram topic model and the hierarchical Pitman-Yor topic model. Using such priors allows for integration of  $n$-grams with a topic model, while smoothing them with the state-of-the-art method. Our model is evaluated by measuring its perplexity on a dataset of musical genre and harmony annotations 3 Genre Database (3GDB) and by measuring its ability to predict musical genre from chord sequences. In terms of perplexity, for a 262-chord dictionary we achieve a value of 2.74, compared to 18.05 for trigrams and 7.73 for a unigram topic model. In terms of genre prediction accuracy with 9 genres, the proposed approach performs about 33% better in relative terms than genre-dependent $n$ -grams, achieving 60.4% of accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {672–681},
numpages = {10}
}

@article{10.1109/TASLP.2014.2300339,
author = {Wei, Wen-Li and Wu, Chung-Hsien and Lin, Jen-Chun and Li, Han},
title = {Exploiting Psychological Factors for Interaction Style Recognition in Spoken Conversation},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2300339},
doi = {10.1109/TASLP.2014.2300339},
abstract = {Determining how a speaker is engaged in a conversation is crucial for achieving harmonious interaction between computers and humans. In this study, a fusion approach was developed based on psychological factors to recognize Interaction Style ($IS$ ) in spoken conversation, which plays a key role in creating natural dialogue agents. The proposed Fused Cross-Correlation Model (FCCM) provides a unified probabilistic framework to model the relationships among the psychological factors of emotion, personality trait ($PT$), transient  $IS$, and  $IS$ history, for recognizing $IS$. An emotional arousal-dependent speech recognizer was used to obtain the recognized spoken text for extracting linguistic features to estimate transient  $IS$ likelihood and recognize $PT$. A temporal course modeling approach and an emotional sub-state language model, based on the temporal phases of an emotional expression, were employed to obtain a better emotion recognition result. The experimental results indicate that the proposed FCCM yields satisfactory results in  $IS$ recognition and also demonstrate that combining psychological factors effectively improves  $IS$ recognition accuracy.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {659–671},
numpages = {13}
}

@article{10.1109/TASLP.2013.2297012,
author = {Asaei, Afsaneh and Golbabaee, Mohammad and Bourlard, Herve and Cevher, Volkan},
title = {Structured Sparsity Models for Reverberant Speech Separation},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2297012},
doi = {10.1109/TASLP.2013.2297012},
abstract = {We tackle the speech separation problem through modeling the acoustics of the reverberant chambers. Our approach exploits structured sparsity models to perform speech recovery and room acoustic modeling from recordings of concurrent unknown sources. The speakers are assumed to lie on a two-dimensional plane and the multipath channel is characterized using the image model. We propose an algorithm for room geometry estimation relying on localization of the early images of the speakers by sparse approximation of the spatial spectrum of the virtual sources in a free-space model. The images are then clustered exploiting the low-rank structure of the spectro-temporal components belonging to each source. This enables us to identify the early support of the room impulse response function and its unique map to the room geometry. To further tackle the ambiguity of the reflection ratios, we propose a novel formulation of the reverberation model and estimate the absorption coefficients through a convex optimization exploiting joint sparsity model formulated upon spatio-spectral sparsity of concurrent speech representation. The acoustic parameters are then incorporated for separating individual speech signals through either structured sparse recovery or inverse filtering the acoustic channels. The experiments conducted on real data recordings of spatially stationary sources demonstrate the effectiveness of the proposed approach for speech separation and recognition.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {620–633},
numpages = {14}
}

@article{10.1109/TASLP.2013.2297013,
author = {Rashobh, Rajan S. and Khong, Andy W. H. and Liu, Di},
title = {Multichannel Equalization in the KLT and Frequency Domains With Application to Speech Dereverberation},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2297013},
doi = {10.1109/TASLP.2013.2297013},
abstract = {Equalization of acoustic channels usually involves inversion of acoustic impulse responses (AIRs), and generally employs multichannel techniques. In this paper, we propose three equalization algorithms, one in the Karhunen-Lo\`{e}ve transform (KLT) domain and the other two in the frequency domain. Our proposed algorithm in the KLT domain provides a platform to achieve equalization in conjunction with denoising. Existing multiple-input/output inverse theorem (MINT)-based non-adaptive algorithms require the inversion of a matrix with dimension that is proportional to the AIR length, and is computationally expensive. To overcome this limitation, we propose the frequency-domain algorithm which is computationally very efficient and thus can be employed for the equalization of high-order AIRs in practical applications. In addition, the frequency-domain method is more robust to AIR estimation errors. To achieve further reduction in the complexity without significant performance degradation, we then propose a modified version of the frequency-domain algorithm.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {634–646},
numpages = {13}
}

@article{10.1109/TASLP.2013.2297018,
author = {Wu, Chung-Hsien and Huang, Yi-Chin and Lee, Chung-Han and Guo, Jun-Cheng},
title = {Synthesis of Spontaneous Speech With Syllable Contraction Using State-Based Context-Dependent Voice Transformation},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2297018},
doi = {10.1109/TASLP.2013.2297018},
abstract = {Pronunciation normally varies in spontaneous speech, and is an integral aspect of spontaneous expression. This study describes a voice transformation-based approach to generating spontaneous speech with syllable contractions for Hidden Markov Model (HMM)-based speech synthesis. A multi-dimensional linear regression model is adopted as the context-dependent, state-based transformation function to convert the feature sequence of read speech to that of spontaneous speech with syllable contraction. With insufficient number of training data, the obtained transformation functions are categorized using a decision tree based on linguistic and articulatory features for better and efficient selection of suitable transformation functions. Furthermore, to cope with the problem of small parallel corpus, cross-validation of trained transformation function is performed to ensure correct transformation functions are obtained and prevent over-fitting. Consequently, pronunciation variations of syllable contraction for the trained and the unseen syllable-contracted words are generated from the transformation function retrieved from the decision tree using linguistic and articulatory features. Objective and subjective tests were used to evaluate the performance of the proposed approach. Evaluation results demonstrate that the proposed transformation function substantially improves apparent spontaneity of the synthesized speech compared to the conventional methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {585–595},
numpages = {11}
}

@article{10.1109/TASLP.2014.2300341,
author = {Samarasinghe, Prasanga and Abhayapala, Thushara and Poletti, Mark},
title = {Wavefield Analysis Over Large Areas Using Distributed Higher Order Microphones},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2014.2300341},
doi = {10.1109/TASLP.2014.2300341},
abstract = {Successful recording of large spatial soundfields is a prevailing challenge in acoustic signal processing due to the enormous numbers of microphones required. This paper presents the design and analysis of an array of higher order microphones that uses 2D wavefield translation to provide a mode matching solution to the height invariant recording problem. It is shown that the use of $M$th order microphones significantly reduces the number of microphone units by a factor of $1/(2M + 1)$ at the expense of increased complexity at each microphone unit. Robustness of the proposed array is also analyzed based on the condition number of the translation matrix while discussing array configurations that result in low condition numbers. The white-noise gain (WNG) of the array is then derived to verify that improved WNG can be achieved when the translation matrix is well conditioned. Furthermore, the array’s performance is studied for interior soundfield recording as well as exterior soundfield recording using appropriate simulation examples.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {647–658},
numpages = {12}
}

@article{10.1109/TASLP.2013.2294578,
author = {Yang, Jae-Mo and Kang, Hong-Goo},
title = {Online Speech Dereverberation Algorithm Based on Adaptive Multichannel Linear Prediction},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294578},
doi = {10.1109/TASLP.2013.2294578},
abstract = {This paper proposes a real-time acoustic channel equalization method that uses an adaptive multichannel linear prediction technique. In general, multichannel equalization algorithms can eliminate reverberation if they meet the following specific conditions including: the co-primeness between channels and sufficient filter length. It also requires the characteristic of correct channel information, however, it is difficult to estimate accurate acoustic channels in a practical system. The proposed method utilizes a theoretically perfect channel equalization algorithm and considers problems that may arise in the actual system. Linear-predictive multi-input equalization (LIME) is also an appropriate attempt at blind dereverberation by assuring the theoretical basis. However, a huge computational cost is incurred by calculating the large dimensions of a covariance matrix and its inversion. The proposed equalizer is developed as a multichannel linear prediction (MLP) oriented structure with a new formula that is optimized to time-varying acoustical room environments. Moreover, experimental results show that the proposed method works well even if the channel characteristics of each microphone are similar. The results of experiments using various room impulse response (RIR) models, including both the synthesized and real room environments, show that the proposed method is superior to conventional methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {608–619},
numpages = {12}
}

@article{10.1109/TASLP.2013.2294585,
author = {Airaksinen, Manu and Raitio, Tuomo and Story, Brad and Alku, Paavo},
title = {Quasi Closed Phase Glottal Inverse Filtering Analysis With Weighted Linear Prediction},
year = {2014},
issue_date = {March 2014},
publisher = {IEEE Press},
volume = {22},
number = {3},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294585},
doi = {10.1109/TASLP.2013.2294585},
abstract = {This study presents a new glottal inverse filtering (GIF) technique based on closed phase analysis over multiple fundamental periods. The proposed quasi closed phase (QCP) analysis method utilizes weighted linear prediction (WLP) with a specific attenuated main excitation (AME) weight function that attenuates the contribution of the glottal source in the linear prediction model optimization. This enables the use of the autocorrelation criterion in linear prediction in contrast to the covariance criterion used in conventional closed phase analysis. The QCP method was compared to previously developed methods by using synthetic vowels produced with the conventional source-filter model as well as with a physical modeling approach. The obtained objective measures show that the QCP method improves the GIF performance in terms of errors in typical glottal source parametrizations for both low- and high-pitched vowels. Additionally, QCP was tested in a physiologically oriented vocoder, where the analysis/synthesis quality was evaluated with a subjective listening test indicating improved perceived quality for normal speaking style.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = mar,
pages = {596–607},
numpages = {12}
}

@article{10.1109/TASLP.2013.2292356,
author = {Hasan, Taufiq and Hansen, John H. L.},
title = {Maximum Likelihood Acoustic Factor Analysis Models for Robust Speaker Verification in Noise},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2292356},
doi = {10.1109/TASLP.2013.2292356},
abstract = {Recent speaker recognition/verification systems generally utilize an utterance dependent fixed dimensional vector as features to Bayesian classifiers. These vectors, known as i-Vectors, are lower dimensional representations of Gaussian Mixture Model (GMM) mean super-vectors adapted from a Universal Background Model (UBM) using speech utterance features, and extracted utilizing a Factor Analysis (FA) framework. This method is based on the assumption that the speaker dependent information resides in a lower dimensional sub-space. In this study, we utilize a mixture of Acoustic Factor Analyzers (AFA) to model the acoustic features instead of a GMM-UBM. Following our previously proposed AFA technique (“Acoustic factor analysis for robust speaker verification,” by Hasan and Hansen, IEEE Trans. Audio, Speech, Lang. Process., vol. 21, no. 4, April 2013), this model is based on the assumption that the speaker relevant information lies in a lower dimensional subspace in the multi-dimensional feature space localized by the mixture components. Unlike our previous method, here we train the AFA-UBM model directly from the data using an Expectation-Maximization (EM) algorithm. This method shows improved robustness to noise as the nuisance dimensions are removed in each EM iteration. Two variants of the AFA model are considered utilizing an isotropic and diagonal covariance residual term. The method is integrated within a standard i-Vector system where the hidden variables of the model, termed as acoustic factors, are utilized as the input for total variability modeling. Experimental results obtained on the 2012 National Institute of Standards and Technology (NIST) Speaker Recognition Evaluation (SRE) core-extended trials indicate the effectiveness of the proposed strategy in both clean and noisy conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {381–391},
numpages = {11}
}

@article{10.1109/TASL.2013.2282191,
author = {Dehong Gao and Wenjie Li and Xiaoyan Cai and Renxian Zhang and You Ouyang},
title = {Sequential Summarization: A Full View of Twitter Trending Topics},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2282191},
doi = {10.1109/TASL.2013.2282191},
abstract = {As an information delivering platform, Twitter collects millions of tweets every day. However, some users, especially new users, often find it difficult to understand trending topics in Twitter when confronting the overwhelming and unorganized tweets. Existing work has attempted to provide a short snippet to explain a topic, but this only provides limited benefits and cannot satisfy the users' expectations. In this paper, we propose a new summarization task, namely sequential summarization, which aims to provide a serial of chronologically ordered short sub-summaries for a trending topic in order to provide a complete story about the development of the topic while retaining the order of information presentation. Different from the traditional summarization task, the numbers of sub-summaries for different topics are not fixed. Two approaches, i.e., stream-based and semantic-based approaches, are developed to detect the important subtopics within a trending topic. Then a short sub-summary is generated for each subtopic. In addition, we propose three new measures to evaluate the position-aware coverage, sequential novelty and sequence correlation of the system-generated summaries. The experimental results based on the proposed evaluation criteria have demonstrated the effectiveness of the proposed approaches.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {293–302},
numpages = {10}
}

@article{10.1109/TASLP.2013.2294584,
author = {Min Zhang and Xiangyu Duan and Wenliang Chen},
title = {Bayesian Constituent Context Model for Grammar Induction},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294584},
doi = {10.1109/TASLP.2013.2294584},
abstract = {Constituent Context Model (CCM) is an effective generative model for grammar induction, the aim of which is to induce hierarchical syntactic structure from natural text. The CCM simply defines the Multinomial distribution over constituents, which leads to a severe data sparse problem because long constituents are unlikely to appear in unseen data sets. This paper proposes a Bayesian method for constituent smoothing by defining two kinds of prior distributions over constituents: the Dirichlet prior and the Pitman-Yor Process prior. The Dirichlet prior functions as an additive smoothing method, and the PYP prior functions as a back-off smoothing method. Furthermore, a modified CCM is proposed to differentiate left constituents and right constituents in binary branching trees. Experiments show that both the proposed Bayesian smoothing method and the modified CCM are effective, and combining them attains or significantly improves the state-of-the-art performance of grammar induction evaluated on standard treebanks of various languages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {531–541},
numpages = {11}
}

@article{10.1109/TASLP.2013.2294581,
author = {Hacihabiboglu, Huseyin},
title = {Theoretical Analysis of Open Spherical Microphone Arrays for Acoustic Intensity Measurements},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294581},
doi = {10.1109/TASLP.2013.2294581},
abstract = {Acoustic intensity is a vectorial measure of acoustic energy flow through a given region of interest. Three-dimensional measurement of acoustic intensity requires special microphone array configurations. This paper provides a theoretical analysis of open spherical microphone arrays for the 3-D measurement of acoustic intensity. The calculations of the pressure and the particle velocity components of the sound field inside a closed volume are expressed using the Kirchhoff-Helmholtz integral equation. The conditions which simplify the calculation are identified. This calculation is then constrained to a finite set of microphones positioned at prescribed points on an open sphere. Several open spherical array topologies are proposed. Their magnitude and directional errors and measurement bandwidths are investigated via numerical simulations. A comparison with conventional open-sphere 3-D intensity probes is presented.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {465–476},
numpages = {12}
}

@article{10.1109/TASLP.2013.2297016,
author = {Dah-Chung Chang and Fei-Tao Chu},
title = {Feedforward Active Noise Control With a New Variable Tap-Length and Step-Size Filtered-X LMS Algorithm},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2297016},
doi = {10.1109/TASLP.2013.2297016},
abstract = {The fixed tap-length and step-size filtered-X least mean-square (FxLMS) algorithm is conventionally used in active noise control (ANC) systems. A tradeoff between the performance and the convergence rate is a well-known problem due to the choice of the step size. Although the variable-step-size FxLMS algorithms have been proposed for fast convergence, a long tap-length filter is frequently required in order to deal with different environments such that the convergence rate is still subject to a small step size for the long tap length. In this paper, we study a new ANC system with a variable tap-length and step-size FxLMS algorithm. Based on the assumption of an unsymmetric and two-sided exponential decay response model for the ANC control filter, the new FxLMS algorithm has the minimum mean-square deviation for the optimal filter coefficients. In the online secondary path modeling ANC system, simulation results show that the new algorithm with different kind of variable step sizes can provide significant improvements of convergence rate and noise reduction ratio, compared to the fixed-tap-length FxLMS algorithms.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {542–555},
numpages = {14}
}

@article{10.1109/TASLP.2013.2295918,
author = {Gonzalez, Sira and Brookes, Mike},
title = {PEFAC - A Pitch Estimation Algorithm Robust to High Levels of Noise},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2295918},
doi = {10.1109/TASLP.2013.2295918},
abstract = {We present PEFAC, a fundamental frequency estimation algorithm for speech that is able to identify voiced frames and estimate pitch reliably even at negative signal-to-noise ratios. The algorithm combines a normalization stage, to remove channel dependency and to attenuate strong noise components, with a harmonic summing filter applied in the log-frequency power spectral domain, the impulse response of which is chosen to sum the energy of the fundamental frequency harmonics while attenuating smoothly-varying noise components. Temporal continuity constraints are applied to the selected pitch candidates and a voiced speech probability is computed from the likelihood ratio of two classifiers, one for voiced speech and one for unvoiced speech/silence. We compare the performance of our algorithm with that of other widely used algorithms and demonstrate that it performs well in both high and low levels of additive noise.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {518–530},
numpages = {13}
}

@article{10.1109/TASLP.2013.2294586,
author = {Pui-Yu Hui and Meng, Helen},
title = {Latent Semantic Analysis for Multimodal User Input With Speech and Gestures},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294586},
doi = {10.1109/TASLP.2013.2294586},
abstract = {This paper describes our work in semantic interpretation of a “multimodal language” with speech and gestures using latent semantic analysis (LSA). Our aim is to infer the domain-specific informational goal of multimodal inputs. The informational goal is characterized by lexical terms used in the spoken modality, partial semantics of gestures in the pen modality, as well as term co-occurrence patterns across modalities, leading to “multimodal terms.” We designed and collected a multimodal corpus of navigational inquiries. We also obtained perfect (i.e. manual) and imperfect (i.e. automatic via recognition) transcriptions for these. We automatically align parsed spoken locative references (SLRs) with their corresponding pen gesture(s) using the Viterbi alignment, according to their numeric and location type features. Then, we characterize each cross-modal integration pattern as a 3-tuple multimodal term with SLR, pen gesture type and their temporal relationship. We propose to use latent semantic analysis (LSA) to derive the latent semantics from manual (i.e. perfect) and automatic (i.e. imperfect) transcriptions of the collected multimodal inputs. In order to achieve this, both multimodal and lexical terms are used to compose an inquiry-term matrix, which is then factorized using singular value decomposition (SVD) to derive the latent semantics automatically. Informational goal inference based on the latent semantics shows that the informational goal inference accuracy of a disjoint test set is 99% and 84% when a perfect and imperfect projection model is used respectively, which performs significantly better than (at least 9.9% absolute) the baseline performance using vector-space model (VSM).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {417–429},
numpages = {13}
}

@article{10.1109/TASLP.2013.2292308,
author = {Souden, Mehrez and Kinoshita, Keisuke and Delcroix, Marc and Nakatani, Tomohiro},
title = {Location Feature Integration for Clustering-Based Speech Separation in Distributed Microphone Arrays},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2292308},
doi = {10.1109/TASLP.2013.2292308},
abstract = {In distributed microphone arrays (DMAs) the source location information can be defined at the intra and inter-node levels. Indeed, while the first type of information results from the diversity of acoustic channels recorded by microphones embedded in the same node, the second is attributed to the differences between the acoustic channels observed by spatially distributed nodes. Both cues are very useful in DMA processing, and the aim of this paper is to utilize both of them to cluster and separate multiple competing speech signals. To capture the intra-node information, we employ the normalized recording vector, while at the inter-node level, we consider different features including the energy level differences with and without the phase differences between nodes. We model the intra-node information using the Watson mixture model (WMM), and propose using the Gamma mixture model (GaMM), Dirichlet mixture model (DMM), and WMM to model different inter-node location features. Furthermore, we propose several integrations of the intra-node and inter-node feature contributions to cluster speech recordings using the expectation maximization algorithm. Finally, simulation results are provided to demonstrate the performance of all ensuing methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {354–367},
numpages = {14}
}

@article{10.1109/TASLP.2013.2292362,
author = {Yu Tsao and Matsuda, Shigeki and Hori, Chiori and Kashioka, Hideki and Chin-Hui Lee},
title = {A MAP-Based Online Estimation Approach to Ensemble Speaker and Speaking Environment Modeling},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2292362},
doi = {10.1109/TASLP.2013.2292362},
abstract = {An ensemble speaker and speaking environment modeling (ESSEM) approach was recently developed. This ESSEM process consists of offline and online phases. The offline phase establishes an environment structure using speech data collected under a wide range of acoustic conditions, whereas the online phase estimates a set of acoustic models that matches the testing environment based on the established environment structure. Since the estimated acoustic models accurately characterize particular testing conditions, ESSEM can improve the speech recognition performance under adverse conditions. In this work, we propose two maximum a posteriori (MAP) based algorithms to improve the online estimation part of the original ESSEM framework. We first develop MAP-based environment structure adaptation to refine the original environment structure. Next, we propose to utilize the MAP criterion to estimate the mapping function of ESSEM and enhance the environment modeling capability. For the MAP estimation, three types of priors are derived; they are the clustered prior (CP), the sequential prior (SP), and the hierarchical prior (HP) densities. Since each prior density is able to characterize specific acoustic knowledge, we further derive a combination mechanism to integrate the three priors. Based on the experimental results on the Aurora-2 task, we verify that using the MAP-based online mapping function estimation can enable ESSEM to achieve better performance than using the maximum-likelihood (ML) based counterpart. Moreover, by using an integration of the online environment structuring adaptation and mapping function estimation, the proposed MAP-based ESSEM framework is found to provide the best performance. Compared with our baseline results, MAP-based ESSEM achieves an average word error rate reduction of 15.53% (5.41 to 4.57%) under 50 testing conditions at a signal-to-noise ratio (SNR) of 0 to 20 dB over the three standardized testing sets.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {403–416},
numpages = {14}
}

@article{10.1109/TASLP.2013.2292361,
author = {Schwartz, Ofer and Gannot, Sharon},
title = {Speaker Tracking Using Recursive EM Algorithms},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2292361},
doi = {10.1109/TASLP.2013.2292361},
abstract = {The problem of localizing and tracking a known number of concurrent speakers in noisy and reverberant enclosures is addressed in this paper. We formulate the localization task as a maximum likelihood (ML) parameter estimation problem, and solve it by utilizing the expectation-maximization (EM) procedure. For the tracking scenario, we propose to adapt two recursive EM (REM) variants. The first, based on Titterington's scheme, is a Newton-based recursion. In this work we also extend Titterington's method to deal with constrained maximization, encountered in the problem at hand. The second is based on Capp\'{e} and Moulines' scheme. We discuss the similarities and dissimilarities of these two variants and show their applicability to the tracking problem by a simulated experimental study.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {392–402},
numpages = {11}
}

@article{10.1109/TASLP.2013.2290497,
author = {Lun, Daniel P. K. and Tak-Wai Shen and Ho, K. C.},
title = {A Novel Expectation-Maximization Framework for Speech Enhancement in Non-Stationary Noise Environments},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2290497},
doi = {10.1109/TASLP.2013.2290497},
abstract = {Voiced speeches have a quasi-periodic nature that allows them to be compactly represented in the cepstral domain. It is a distinctive feature compared with noises. Recently, the temporal cepstrum smoothing (TCS) algorithm was proposed and was shown to be effective for speech enhancement in non-stationary noise environments. However, the missing of an automatic parameter updating mechanism limits its adaptability to noisy speeches with abrupt changes in SNR across time frames or frequency components. In this paper, an improved speech enhancement algorithm based on a novel expectation-maximization (EM) framework is proposed. The new algorithm starts with the traditional TCS method which gives the initial guess of the periodogram of the clean speech. It is then applied to an L1 norm regularizer in the M-step of the EM framework to estimate the true power spectrum of the original speech. It in turn enables the estimation of the a-priori SNR and is used in the E-step, which is indeed a logmmse gain function, to refine the estimation of the clean speech periodogram. The M-step and E-step iterate alternately until converged. A notable improvement of the proposed algorithm over the traditional TCS method is its adaptability to the changes (even abrupt changes) in SNR of the noisy speech. Performance of the proposed algorithm is evaluated using standard measures based on a large set of speech and noise signals. Evaluation results show that a significant improvement is achieved compared to conventional approaches especially in non-stationary noise environment where most conventional algorithms fail to perform.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {335–346},
numpages = {12}
}

@article{10.1109/TASLP.2013.2294580,
author = {McVicar, Matt and Santos-Rodriguez, Raul and Yizhao Ni and Tijl De Bie},
title = {Automatic Chord Estimation from Audio: A Review of the State of the Art},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294580},
doi = {10.1109/TASLP.2013.2294580},
abstract = {In this overview article, we review research on the task of Automatic Chord Estimation (ACE). The major contributions from the last 14 years of research are summarized, with detailed discussions of the following topics: feature extraction, modeling strategies, model training and datasets, and evaluation strategies. Results from the annual benchmarking evaluation Music Information Retrieval Evaluation eXchange (MIREX) are also discussed as well as developments in software implementations and the impact of ACE within MIR. We conclude with possible directions for future research.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {556–575},
numpages = {20}
}

@article{10.1109/TASLP.2013.2295926,
author = {Taemin Cho and Bello, Juan P.},
title = {On the Relative Importance of Individual Components of Chord Recognition Systems},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2295926},
doi = {10.1109/TASLP.2013.2295926},
abstract = {Most chord recognition systems share a common architecture comprising two main stages: feature extraction and pattern matching, and two optional sub stages: pre-filtering and post-filtering. Understanding the interaction between these basic components is very important not only for achieving optimal performance, but also for assessing the potential and limitations of the system. Unfortunately, there are no studies that sufficiently evaluate the effects of the different approaches to each processing step and the interactions between these steps. In this paper we attempt to remedy this deficiency by performing a systematic evaluation encompassing a wide variety of techniques used for each processing step. In our study we find that filtering has a significant impact on performance, but providing musical context information in the transition matrix is rendered moot by the need to enforce continuity in the estimations. We discovered that the benefits of using complex chord models can be largely offset by an appropriate choice of features. In addition, the initial performance gap between different features were not fully compensated by any subsequent processing stages.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {477–492},
numpages = {16}
}

@article{10.1109/TASLP.2013.2292328,
author = {Kallasjoki, Heikki and Gemmeke, Jort F. and Palomaki, Kalle J.},
title = {Estimating Uncertainty to Improve Exemplar-Based Feature Enhancement for Noise Robust Speech Recognition},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2292328},
doi = {10.1109/TASLP.2013.2292328},
abstract = {We present a method of improving automatic speech recognition performance under noisy conditions by using a source separation approach to extract the underlying clean speech signal. The feature enhancement processing is complemented with heuristic estimates of the uncertainty of the source separation, that are used to further assist the recognition. The uncertainty heuristics are converted to estimates of variance for the extracted clean speech using a Gaussian Mixture Model based mapping, and applied in the decoding stage under the observation uncertainty framework. We propose six heuristics, and evaluate them using both artificial and real-world noisy data, and with acoustic models trained on clean speech, a multi-condition noisy data set, and the multi-condition set processed with the source separation front-end. Taking the uncertainty of the enhanced features into account is shown to improve recognition performance when the acoustic models are trained on unenhanced data, while training on enhanced noisy data yields the lowest error rates.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {368–380},
numpages = {13}
}

@article{10.1109/TASL.2013.2283105,
author = {van Hengel, P. W. J. and Krijnders, J. D.},
title = {A Comparison of Spectro-Temporal Representations of Audio Signals},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2283105},
doi = {10.1109/TASL.2013.2283105},
abstract = {This article compares methods for the conversion of timeseries into a spectro-temporal representation. These methods are designed based on a resemblance with the auditory processing of sound in the mammalian inner ear, or on mathematical principles related to, for example, Fourier analysis. This study provides a comparison between several of these methods. Two tests were devised for this comparison: one based on susceptibility to noise and one on the expression of spectro-temporal detail. These two aspects were considered of importance for real world applications. While some methods produced good results on one of the two tests, others produced good results on both. Overall the transmission line model using an impedance function suggested by Zweig (“Finding the impedance of the organ of Corti,” J. Acoust. Soc. Amer., vol. 89, no. 3, pp. 1229-1254, 1991) provided the best results, though not significantly. Also a larger computational load may hinder application in some domains. The gammatone filterbank and straightforward spectrogram provide good alternatives with less computational load. The introduction of nonlinearity was shown to deteriorate performance on both tests, in both the filterbank and in the transmission line model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {303–313},
numpages = {11}
}

@article{10.1109/TASLP.2013.2287056,
author = {Molina, Emilio and Barbancho, Ana M. and Tardon, Lorenzo J. and Barbancho, Isabel},
title = {Dissonance Reduction In Polyphonic Audio Using Harmonic Reorganization},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2287056},
doi = {10.1109/TASLP.2013.2287056},
abstract = {In this paper, a method for automatic reduction of dissonance in recorded isolated chords is proposed. Previous approaches address this problem using source separation and note-level processing. In our approach, we manipulate the harmonic structure as a whole in order to avoid beating partials which, according to prior research on dissonance perception, typically produce an unpleasant sound. The proposed system firstly performs a sinusoidal plus residual modeling of the input and analyses the various fundamental frequencies present in the chord. This information is used to create a symbolic representation of the in-tune version of the input according to some musical rules. Then, the partials of the signals are shifted in order to fit the in-tune harmonic structure of the input chord. The input is assumed to contain one isolated chord, with relatively stable fundamental frequencies belonging to the Western chromatic scale. The evaluation has been performed by 31 expert musicians, which have quantified the perceived consonance of six varied, out-of-tune chords in three variants: unprocessed, processed with our system and processed by a state-of-the-art commercial tool (Melodyne Editor). The proposed approach attains an important reduction of the perceived dissonance, showing better performance than Melodyne Editor for most of the cases evaluated.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {325–334},
numpages = {10}
}

@article{10.1109/TASLP.2013.2290502,
author = {Cosentino, Stefano and Falk, Tiago H. and McAlpine, David and Marquardt, Torsten},
title = {Cochlear Implant Filterbank Design and Optimization: A Simulation Study},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2290502},
doi = {10.1109/TASLP.2013.2290502},
abstract = {Cochlear implants (CIs) are devices capable of restoring hearing function in profoundly-deaf patients to an acceptable degree of performance. An essential processing step in any cochlear implant is frequency analysis, which is usually performed via banks of filters. Here, we simulate and test the suitability of different filters and filterbank architectures for CIs with respect to their performance in speech intelligibility. Four different filters were implemented in an established model of CI hearing, the tone-excited vocoder, namely: GTF (Gammatone Filter), DAPGF (Differentiated All-Pole GTF), OZGF (One-Zero GTF) and BUTF (Butterworth). Three filterbank parameters, the filter order ( N), the filter quality factor ( Q) and the number of channels ( Ch), and their combinations were tested using objective and subjective metrics. Simulation results show that all filters tested are suitable for CI implementation, but that the choice of Q and N parameter values is crucial. For most conditions, optimal ( N,Q) combinations were within few units away from the combination (2, 4).},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {347–353},
numpages = {7}
}

@article{10.1109/TASLP.2013.2294582,
author = {Otsuka, Takuma and Ishiguro, Katsuhiko and Sawada, Hiroshi and Okuno, Hiroshi G.},
title = {Bayesian Nonparametrics for Microphone Array Processing},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294582},
doi = {10.1109/TASLP.2013.2294582},
abstract = {Sound source localization and separation from a mixture of sounds are essential functions for computational auditory scene analysis. The main challenges are designing a unified framework for joint optimization and estimating the sound sources under auditory uncertainties such as reverberation or unknown number of sounds. Since sound source localization and separation are mutually dependent, their simultaneous estimation is required for better and more robust performance. A unified model is presented for sound source localization and separation based on Bayesian nonparametrics. Experiments using simulated and recorded audio mixtures show that a method based on this model achieves state-of-the-art sound source separation quality and has more robust performance on the source number estimation under reverberant environments.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {493–504},
numpages = {12}
}

@article{10.1109/TASLP.2013.2287055,
author = {Zitouni, Imed and Benajiba, Yassine},
title = {Aligned-Parallel-Corpora Based Semi-Supervised Learning for Arabic Mention Detection},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2287055},
doi = {10.1109/TASLP.2013.2287055},
abstract = {In the last two decades, significant effort has been put into annotating linguistic resources in several languages. Despite this valiant effort, there are still many languages left that have only small amounts of such resources. The goal of this article is to present and investigate a method of propagating information (specifically mentions) from a resource-rich language such as English into a relatively less-resource language such as Arabic. We compare also this approach to its equivalent counterpart using monolingual resources. Part of the investigation is to quantify the contribution of propagating information in different conditions - based on the availability of resources in the target language. Experiments on the language pair Arabic-English show that one can achieve relatively decent performance by propagating information from a language with richer resources such as English into Arabic alone (no resources or models in the source language Arabic). Furthermore, results show that propagated features from English do help improve the Arabic system performance even when used in conjunction with all feature types built from the source language. Experiments also show that using propagated features in conjunction with lexically-derived features only (as can be obtained directly from a mention annotated corpus) brings the system performance at the one obtained in the target language by using feature derived from many linguistic resources, therefore improving the system when such resources are not available.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {314–324},
numpages = {11}
}

@article{10.1109/TASLP.2013.2295925,
author = {Primavera, Andrea and Cecchi, Stefania and Junfeng Li and Piazza, Francesco},
title = {Objective and Subjective Investigation on a Novel Method for Digital Reverberator Parameters Estimation},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2295925},
doi = {10.1109/TASLP.2013.2295925},
abstract = {Reverberation is a well known effect that has an important role in our listening experience. A great deal of research has been devoted in the last decades aiming to artificially reproduce the reverberation effect exploiting a hybrid reverberation structure. In this context, several automatic procedures have been presented in the literature in order to derive the reverberator structure considering the mixing time evaluation and the minimization functions definition for the late reverberation device. Taking into consideration these aspects, a deep analysis of hybrid digital reverberator audio quality is here proposed, introducing a new parameter for the definition of the mixing time and two new cost functions for the definition of the late reverberation parameters. More in detail, starting from the considerations derived from a previous accurate approach based on the mel frequency cepstral coefficients, the new cost functions are based on the evaluation of the perceptual linear predictive and power normalized cepstral coefficients. Several results are reported, in terms of objective measure, performance analysis and subjective measures, taking into consideration different real impulse responses and various input stimuli and making a comparison with the state of the art. In particular, the obtained results show that a good accuracy can be achieved also considering a low number of coefficients, therefore improving the computational performance.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {441–452},
numpages = {12}
}

@article{10.1109/TASLP.2013.2297015,
author = {Jianjun He and Ee-Leng Tan and Woon-Seng Gan},
title = {Linear Estimation Based Primary-Ambient Extraction for Stereo Audio Signals},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2297015},
doi = {10.1109/TASLP.2013.2297015},
abstract = {Audio signals for moving pictures and video games are often linear combinations of primary and ambient components. In spatial audio analysis-synthesis, these mixed signals are usually decomposed into primary and ambient components to facilitate flexible spatial rendering and enhancement. Existing approaches such as principal component analysis (PCA) and least squares (LS) are widely used to perform this decomposition from stereo signals. However, the performance of these approaches in primary-ambient extraction (PAE) has not been well studied and no comparative analysis among the existing approaches has been carried out so far. In this paper, we generalize the existing approaches into a linear estimation framework. Under this framework, we propose a series of performance measures to identify the components that contribute to the extraction error. Based on the generalized linear estimation framework and our proposed performance measures, a comparative study and experimental testing of the linear estimation based PAE approaches including existing PCA, LS, and three proposed variant LS approaches are presented.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {505–517},
numpages = {13}
}

@article{10.1109/TASLP.2013.2294579,
author = {Speed, Matt and Murphy, Damian and Howard, David},
title = {Modeling the Vocal Tract Transfer Function Using a 3D Digital Waveguide Mesh},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2294579},
doi = {10.1109/TASLP.2013.2294579},
abstract = {The digital waveguide mesh has been shown to be capable of reproducing the acoustic impulse response of cylindrical vocal tract analogs. This study extends the same methodology to three-dimensional simulation of the acoustic response of graphical models of the vocal tract obtained from magnetic resonance imaging for a group of trained subjects. By such simulation of the vocal tract transfer function and convolution with an appropriate source waveform, basic phonemes are resynthesized and compared with benchmark audio recordings. The technologies and techniques used for simulation are described, alongside the protocol for image capture and the process for collection of benchmark audio. The results of simulation and acoustic recording are then evaluated and compared. The value of three-dimensional simulation in comparison to existing lower-dimensionality equivalents is assessed. It is found that while three-dimensional simulation provides a strong representation of the low frequency vocal tract transfer function, at higher frequencies its performance becomes geometry-dependent. MRI imaging and benchmark audio is provided for future studies and to permit comparison with comparable means of acoustic simulation.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {453–464},
numpages = {12}
}

@article{10.1109/TASLP.2013.2295914,
author = {Jensen, Jesper and Taal, Cees H.},
title = {Speech Intelligibility Prediction Based on Mutual Information},
year = {2014},
issue_date = {February 2014},
publisher = {IEEE Press},
volume = {22},
number = {2},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2295914},
doi = {10.1109/TASLP.2013.2295914},
abstract = {This paper deals with the problem of predicting the average intelligibility of noisy and potentially processed speech signals, as observed by a group of normal hearing listeners. We propose a model which performs this prediction based on the hypothesis that intelligibility is monotonically related to the mutual information between critical-band amplitude envelopes of the clean signal and the corresponding noisy/processed signal. The resulting intelligibility predictor turns out to be a simple function of the mean-square error (mse) that arises when estimating a clean critical-band amplitude using a minimum mean-square error (mmse) estimator based on the noisy/processed amplitude. The proposed model predicts that speech intelligibility cannot be improved by any processing of noisy critical-band amplitudes. Furthermore, the proposed intelligibility predictor performs well ( ρ > 0.95) in predicting the intelligibility of speech signals contaminated by additive noise and potentially non-linearly processed using time-frequency weighting.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = feb,
pages = {430–440},
numpages = {11}
}

@article{10.1109/TASLP.2013.2290172,
author = {Deng, Li and Renals, Steve and Federico, Marcello and Ostendorf, Mari},
title = {Editorial: Expanding the Technical Reach of Our Transactions},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2290172},
doi = {10.1109/TASLP.2013.2290172},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {5},
numpages = {1}
}

@article{10.1109/TASLP.2013.2285469,
author = {Hung-Yi Lee and Lin-Shan Lee},
title = {Improved Semantic Retrieval of Spoken Content by Document/Query Expansion with Random Walk Over Acoustic Similarity Graphs},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285469},
doi = {10.1109/TASLP.2013.2285469},
abstract = {In a text context, document/query expansion has proven very useful in retrieving objects semantically related to the query. However, when applying text-based techniques on spoken content, the inevitable recognition errors seriously degrade performance even when the retrieval process is performed over lattices. We propose the estimation of more accurate term distributions (or unigram language models) for the spoken documents by acoustic similarity graphs. In this approach, a graph is constructed for each term describing the acoustic similarity among all signal regions hypothesized to be the considered term. Score propagation based on a random walk over the graph offers more reliable scores of the term hypotheses, which in turn yield more accurate term distributions (or unigram language models). This approach was applied with the language modeling retrieval approach, including using document expansion based on latent topic analysis and query expansion with a query-regularized mixture model. We extend these approaches from words to subword n-grams, and the query expansion from document-level to utterance-level and from term-based to topic-based. Experiments performed on Mandarin broadcast news showed improved performance under almost all tested conditions.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {80–94},
numpages = {15}
}

@article{10.1109/TASL.2013.2281575,
author = {Liang Lu and Ghoshal, Arnab and Renals, Steve},
title = {Cross-Lingual Subspace Gaussian Mixture Models for Low-Resource Speech Recognition},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2281575},
doi = {10.1109/TASL.2013.2281575},
abstract = {This paper studies cross-lingual acoustic modeling in the context of subspace Gaussian mixture models (SGMMs). SGMMs factorize the acoustic model parameters into a set that is globally shared between all the states of a hidden Markov model (HMM) and another that is specific to the HMM states. We demonstrate that the SGMM global parameters are transferable between languages, particularly when the parameters are trained multilingually. As a result, acoustic models may be trained using limited amounts of transcribed audio by borrowing the SGMM global parameters from one or more source languages, and only training the state-specific parameters on the target language audio. Model regularization using ℓ1-norm penalty is shown to be particularly effective at avoiding overtraining and leading to lower word error rates. We investigate maximum a posteriori (MAP) adaptation of subspace parameters in order to reduce the mismatch between the SGMM global parameters of the source and target languages. In addition, monolingual and cross-lingual speaker adaptive training is used to reduce the model variance introduced by speakers. We have systematically evaluated these techniques by experiments on the GlobalPhone corpus.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {17–27},
numpages = {11}
}

@article{10.1109/TASLP.2013.2285480,
author = {Leutnant, Volker and Krueger, Alexander and Haeb-Umbach, Reinhold},
title = {A New Observation Model in the Logarithmic Mel Power Spectral Domain for the Automatic Recognition of Noisy Reverberant Speech},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285480},
doi = {10.1109/TASLP.2013.2285480},
abstract = {In this contribution we present a theoretical and experimental investigation into the effects of reverberation and noise on features in the logarithmic mel power spectral domain, an intermediate stage in the computation of the mel frequency cepstral coefficients, prevalent in automatic speech recognition (ASR). Gaining insight into the complex interaction between clean speech, noise, and noisy reverberant speech features is essential for any ASR system to be robust against noise and reverberation present in distant microphone input signals. The findings are gathered in a probabilistic formulation of an observation model which may be used in model-based feature compensation schemes. The proposed observation model extends previous models in three major directions: First, the contribution of additive background noise to the observation error is explicitly taken into account. Second, an energy compensation constant is introduced which ensures an unbiased estimate of the reverberant speech features, and, third, a recursive variant of the observation model is developed resulting in reduced computational complexity when used in model-based feature compensation. The experimental section is used to evaluate the accuracy of the model and to describe how its parameters can be determined from test data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {95–109},
numpages = {15}
}

@article{10.1109/TASLP.2013.2290499,
author = {Shabtai, Noam R. and Rafaely, Boaz},
title = {Generalized Spherical Array Beamforming for Binaural Speech Reproduction},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2290499},
doi = {10.1109/TASLP.2013.2290499},
abstract = {Microphone arrays are used in speech signal processing applications such as teleconferencing and telepresence, in order to enhance a desired speech signal in the presence of speech signals from other speakers, reverberation and background noise. These arrays usually provide a single-channel output, so that no spatial information is available in the output signal. However, spatial information on the sound sources may increase the intelligibility of a speech signal perceived by a human listener. This work presents a mathematical framework for generalized spherical array beamforming that in addition to suppressing noise and reverberation, is aiming to preserve spatial information on the sources in the recording venue. The generalized beamforming, formulated in the spherical harmonics domain, is based on binaural sound reproduction where the head-related transfer functions are incorporated into a headphones presentation. The performance of the proposed generalized beamformer is compared to that of a single-channel output maximum-directivity beamformer. Listening tests with human subjects show that when the generalized beamformer is used the intelligibility is improved at low input SNRs.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {238–247},
numpages = {10}
}

@article{10.1109/TASL.2013.2283104,
author = {Chao Pan and Jingdong Chen and Benesty, Jacob},
title = {Performance Study of the MVDR Beamformer as a Function of the Source Incidence Angle},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2283104},
doi = {10.1109/TASL.2013.2283104},
abstract = {Linear microphone arrays combined with the minimum variance distortionless response (MVDR) beamformer have been widely studied in various applications to acquire desired signals and reduce the unwanted noise. Most of the existing array systems assume that the desired sources are in the broadside direction. In this paper, we study and analyze the performance of the MVDR beamformer as a function of the source incidence angle. Using the signal-to-noise ratio (SNR) and beampattern as the criteria, we investigate its performance in four different scenarios: spatially white noise, diffuse noise, diffuse-plus-white noise, and point-source-plus-white noise. The results demonstrate that the optimal performance of the MVDR beamformer occurs when the source is in the endfire directions for diffuse noise and point-source noise while its SNR gain does not depend on the signal incidence angle in spatially white noise. This indicates that most current systems may not fully exploit the potential of the MVDR beamformer. This analysis does not only help us better understand this algorithm, but also helps us design better array systems for practical applications.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {67–79},
numpages = {13}
}

@article{10.1109/TASL.2013.2283100,
author = {Crespo, Joao B. and Hendriks, Richard C.},
title = {Multizone Speech Reinforcement},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2283100},
doi = {10.1109/TASL.2013.2283100},
abstract = {In this article, we address speech reinforcement (near-end listening enhancement) for a scenario where there are several playback zones. In such a framework, signals from one zone can leak into other zones (crosstalk), causing intelligibility and/or quality degradation. An optimization framework is built by exploring a signal model where effects of noise, reverberation and zone crosstalk are taken into account simultaneously. Through the symbolic usage of a general smooth distortion measure, necessary optimality conditions are derived in terms of distortion measure gradients and the signal model. Subsequently, as an illustrative example of the framework, the conditions are applied for the mean-square error (MSE) expected distortion under a hybrid stochastic-deterministic model for the corruptions. A crosstalk cancellation algorithm follows, which depends on diffuse reverberation and across zone direct path components. Simulations validate the optimality of the algorithm and show a clear benefit in multizone processing, as opposed to the iterated application of a single-zone algorithm. Also, comparisons with least-squares crosstalk cancellers in literature show the profit of using a hybrid model.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {54–66},
numpages = {13}
}

@article{10.1109/TASLP.2013.2290505,
author = {Cumani, Sandro and Laface, Pietro},
title = {Factorized Sub-Space Estimation for Fast and Memory Effective I-Vector Extraction},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2290505},
doi = {10.1109/TASLP.2013.2290505},
abstract = {Most of the state-of-the-art speaker recognition systems use a compact representation of spoken utterances referred to as i-vector. Since the “standard” i-vector extraction procedure requires large memory structures and is relatively slow, new approaches have recently been proposed that are able to obtain either accurate solutions at the expense of an increase of the computational load, or fast approximate solutions, which are traded for lower memory costs. We propose a new approach particularly useful for applications that need to minimize their memory requirements. Our solution not only dramatically reduces the memory needs for i-vector extraction, but is also fast and accurate compared to recently proposed approaches. Tested on the female part of the tel-tel extended NIST 2010 evaluation trials, our approach substantially improves the performance with respect to the fastest but inaccurate eigen-decomposition approach, using much less memory than other methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {248–259},
numpages = {12}
}

@article{10.1109/TASLP.2013.2285482,
author = {Chen, Nancy F. and Tam, Sharon W. and Wade Shen and Campbell, Joseph P.},
title = {Characterizing Phonetic Transformations and Acoustic Differences Across English Dialects},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285482},
doi = {10.1109/TASLP.2013.2285482},
abstract = {In this work, we propose a framework that automatically discovers dialect-specific phonetic rules. These rules characterize when certain phonetic or acoustic transformations occur across dialects. To explicitly characterize these dialect-specific rules, we adapt the conventional hidden Markov model to handle insertion and deletion transformations. The proposed framework is able to convert pronunciation of one dialect to another using learned rules, recognize dialects using learned rules, retrieve dialect-specific regions, and refine linguistic rules. Potential applications of our proposed framework include computer-assisted language learning, sociolinguistics, and diagnosis tools for phonological disorders.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {110–124},
numpages = {15}
}

@article{10.1109/TASLP.2013.2288081,
author = {Zhenghua Li and Min Zhang and Wanxiang Che and Ting Liu and Wenliang Chen},
title = {Joint Optimization for Chinese POS Tagging and Dependency Parsing},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2288081},
doi = {10.1109/TASLP.2013.2288081},
abstract = {Dependency parsing has gained more and more interest in natural language processing in recent years due to its simplicity and general applicability for diverse languages. Previous work demonstrates that part-of-speech (POS) is an indispensable feature in dependency parsing since pure lexical features suffer from serious data sparseness problem. However, due to little morphological changes, Chinese POS tagging has proven to be much more challenging than morphology-richer languages such as English (94% vs. 97% on POS tagging accuracy). This leads to severe error propagation for Chinese dependency parsing. Our experiments show that parsing accuracy drops by about 6% when replacing manual POS tags of the input sentence with automatic ones generated by a state-of-the-art statistical POS tagger. To address this issue, this paper proposes a solution by jointly optimizing POS tagging and dependency parsing in a unique model. We propose for our joint models several dynamic programming based decoding algorithms which can incorporate rich POS tagging and syntactic features. Then we present an effective pruning strategy to reduce the search space of candidate POS tags, leading to significant improvement of parsing speed. Experimental results on two Chinese data sets, i.e. Penn Chinese Treebank 5.1 and Penn Chinese Treebank 7, demonstrate that our joint models significantly improve both the state-of-the-art tagging and parsing accuracies. Detailed analysis shows that the joint method can help resolve syntax-sensitive POS ambiguities {ssrNN,ssrVV}. In return, the POS tags become more reliable and helpful for parsing since the syntactic features are used in POS tagging. This is the fundamental reason for the performance improvement.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {274–286},
numpages = {13}
}

@article{10.1109/TASLP.2013.2286906,
author = {Tomar, Vikrant Singh and Rose, Richard C.},
title = {A Family of Discriminative Manifold Learning Algorithms and Their Application to Speech Recognition},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2286906},
doi = {10.1109/TASLP.2013.2286906},
abstract = {This paper presents a family of discriminative manifold learning approaches to feature space dimensionality reduction in noise robust automatic speech recognition (ASR). The specific goal of these techniques is to preserve local manifold structure in feature space while at the same time maximizing the separability between classes of feature vectors. In the manifold space, the relationships among the feature vectors are defined using nonlinear kernels. Two separate distance measures are used to characterize the kernels, namely the conventional Euclidean distance and a cosine-correlation based distance. The performance of the proposed techniques is evaluated on two task domains involving noise corrupted utterances of connected digits and read newspaper text. Performance is compared to existing approaches used for feature space transformations, including linear discriminant analysis (LDA) and locality preserving linear projections (LPP). The proposed approaches are found to provide a significant reduction in word error rate (WER) with respect to the more well-known techniques for a variety of noise conditions. Another contribution of the paper is to quantify the interaction between acoustic noise conditions and the shape and size of local neighborhoods which are used in manifold learning to define local relationships among feature vectors. Based on this analysis, a procedure for reducing the impact of varying acoustic conditions on manifold learning is proposed .},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {161–171},
numpages = {11}
}

@article{10.1109/TASL.2013.2281574,
author = {Taghia, Jalal and Martin, Rainer},
title = {Objective Intelligibility Measures Based on Mutual Information for Speech Subjected to Speech Enhancement Processing},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2281574},
doi = {10.1109/TASL.2013.2281574},
abstract = {We propose a novel method for objective speech intelligibility prediction which can be useful in many application domains such as hearing instruments and forensics. Most objective intelligibility measures available in the literature employ some kind of signal-to-noise ratio (SNR) or a correlation-based comparison between the spectro-temporal representations of clean and processed speech. In this paper, we investigate the speech intelligibility prediction from the viewpoint of information theory and introduce novel objective intelligibility measures based on the estimated mutual information between the temporal envelopes of clean speech and processed speech in the subband domain. Mutual information allows to account for higher order statistics and hence to consider dependencies beyond the conventional second order statistics. Using data from three different listening tests it is shown that the proposed objective intelligibility measures provide promising results for speech intelligibility prediction in different scenarios of speech enhancement where speech is processed by non-linear modification strategies.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {6–16},
numpages = {11}
}

@article{10.1109/TASLP.2013.2285484,
author = {Zhiyao Duan and Jinyu Han and Pardo, Bryan},
title = {Multi-Pitch Streaming of Harmonic Sound Mixtures},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285484},
doi = {10.1109/TASLP.2013.2285484},
abstract = {Multi-pitch analysis of concurrent sound sources is an important but challenging problem. It requires estimating pitch values of all harmonic sources in individual frames and streaming the pitch estimates into trajectories, each of which corresponds to a source. We address the streaming problem for monophonic sound sources. We take the original audio, plus frame-level pitch estimates from any multi-pitch estimation algorithm as inputs, and output a pitch trajectory for each source. Our approach does not require pre-training of source models from isolated recordings. Instead, it casts the problem as a constrained clustering problem, where each cluster corresponds to a source. The clustering objective is to minimize the timbre inconsistency within each cluster. We explore different timbre features for music and speech. For music, harmonic structure and a newly proposed feature called uniform discrete cepstrum (UDC) are found effective; while for speech, MFCC and UDC works well. We also show that timbre-consistency is insufficient for effective streaming. Constraints are imposed on pairs of pitch estimates according to their time-frequency relationships. We propose a new constrained clustering algorithm that satisfies as many constraints as possible while optimizing the clustering objective. We compare the proposed approach with other state-of-the-art supervised and unsupervised multi-pitch streaming approaches that are specifically designed for music or speech. Better or comparable results are shown.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {138–150},
numpages = {13}
}

@article{10.1109/TASLP.2013.2285474,
author = {Senoussaoui, Mohammed and Kenny, Patrick and Stafylakis, Themos and Dumouchel, Pierre},
title = {A Study of the Cosine Distance-Based Mean Shift for Telephone Speech Diarization},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285474},
doi = {10.1109/TASLP.2013.2285474},
abstract = {Speaker clustering is a crucial step for speaker diarization. The short duration of speech segments in telephone speech dialogue and the absence of prior information on the number of clusters dramatically increase the difficulty of this problem in diarizing spontaneous telephone speech conversations. We propose a simple iterative Mean Shift algorithm based on the cosine distance to perform speaker clustering under these conditions. Two variants of the cosine distance Mean Shift are compared in an exhaustive practical study. We report state of the art results as measured by the Diarization Error Rate and the Number of Detected Speakers on the LDC CallHome telephone corpus.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {217–227},
numpages = {11}
}

@article{10.1109/TASL.2013.2282190,
author = {Gasic, Milica and Young, Steve},
title = {Gaussian Processes for POMDP-Based Dialogue Manager Optimization},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2282190},
doi = {10.1109/TASL.2013.2282190},
abstract = {A partially observable Markov decision process (POMDP) has been proposed as a dialog model that enables automatic optimization of the dialog policy and provides robustness to speech understanding errors. Various approximations allow such a model to be used for building real-world dialog systems. However, they require a large number of dialogs to train the dialog policy and hence they typically rely on the availability of a user simulator. They also require significant designer effort to hand-craft the policy representation. We investigate the use of Gaussian processes (GPs) in policy modeling to overcome these problems. We show that GP policy optimization can be implemented for a real world POMDP dialog manager, and in particular: 1) we examine different formulations of a GP policy to minimize variability in the learning process; 2) we find that the use of GP increases the learning rate by an order of magnitude thereby allowing learning by direct interaction with human users; and 3) we demonstrate that designer effort can be substantially reduced by basing the policy directly on the full belief space thereby avoiding ad hoc feature space modeling. Overall, the GP approach represents an important step forward towards fully automatic dialog policy optimization in real world systems.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {28–40},
numpages = {13}
}

@article{10.1109/TASLP.2013.2286919,
author = {Arisoy, Ebru and Chen, Stanley F. and Ramabhadran, Bhuvana and Sethy, Abhinav},
title = {Converting Neural Network Language Models into Back-off Language Models for Efficient Decoding in Automatic Speech Recognition},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2286919},
doi = {10.1109/TASLP.2013.2286919},
abstract = {Neural network language models (NNLMs) have achieved very good performance in large-vocabulary continuous speech recognition (LVCSR) systems. Because decoding with NNLMs is computationally expensive, there is interest in developing methods to approximate NNLMs with simpler language models that are suitable for fast decoding. In this work, we propose an approximate method for converting a feedforward NNLM into a back-off n-gram language model that can be used directly in existing LVCSR decoders. We convert NNLMs of increasing order to pruned back-off language models, using lower-order models to constrain the n-grams allowed in higher-order models. In experiments on Broadcast News data, we find that the resulting back-off models retain the bulk of the gain achieved by NNLMs over conventional n-gram language models, and give accuracy improvements as compared to existing methods for converting NNLMs to back-off models. In addition, the proposed approach can be applied to any type of non-back-off language model to enable efficient decoding.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {184–192},
numpages = {9}
}

@article{10.1109/TASLP.2013.2286917,
author = {Doi, Hironori and Toda, Tomoki and Nakamura, Keigo and Saruwatari, Hiroshi and Shikano, Kiyohiro},
title = {Alaryngeal Speech Enhancement Based on One-to-Many Eigenvoice Conversion},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2286917},
doi = {10.1109/TASLP.2013.2286917},
abstract = {In this paper, we present novel speaking-aid systems based on one-to-many eigenvoice conversion (EVC) to enhance three types of alaryngeal speech: esophageal speech, electrolaryngeal speech, and body-conducted silent electrolaryngeal speech. Although alaryngeal speech allows laryngectomees to utter speech sounds, it suffers from the lack of speech quality and speaker individuality. To improve the speech quality of alaryngeal speech, alaryngeal-speech-to-speech (AL-to-Speech) methods based on statistical voice conversion have been proposed. In this paper, one-to-many EVC capable of flexibly controlling the converted voice quality by adapting the conversion model to given target natural voices is further implemented for the AL-to-Speech methods to effectively recover speaker individuality of each type of alaryngeal speech. These proposed systems are compared with each other from various perspectives. The experimental results demonstrate that our proposed systems are capable of effectively addressing the issues of alaryngeal speech, e.g., yielding significant improvements in speech quality of each type of alaryngeal speech.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {172–183},
numpages = {12}
}

@article{10.1109/TASL.2013.2282214,
author = {Mezghani-Marrakchi, Imen and Mahe, Gael and Djaziri-Larbi, Sonia and Jaidane, Meriem and Turki-Hadj Alouane, Monia},
title = {Nonlinear Audio Systems Identification Through Audio Input Gaussianization},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASL.2013.2282214},
doi = {10.1109/TASL.2013.2282214},
abstract = {Nonlinear audio system identification generally relies on Gaussianity, whiteness and stationarity hypothesis on the input signal, although audio signals are non-Gaussian, highly correlated and non-stationary. However, since the physical behavior of nonlinear audio systems is input-dependent, they should be identified using natural audio signals (speech or music) as input, instead of artificial signals (sweeps or noise) as usually done. We propose an identification scheme that conditions audio signals to fit the desired properties for an efficient identification. The identification system consists in (1) a Gaussianization step that makes the signal near-Gaussian under a perceptual constraint; (2) a predictor filterbank that whitens the signal; (3) an orthonormalization step that enhances the statistical properties of the input vector of the last step, under a Gaussianity hypothesis; (4) an adaptive nonlinear model. The proposed scheme enhances the convergence rate of the identification and reduces the steady state identification error, compared to other schemes, for example the classical adaptive nonlinear identification.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {41–53},
numpages = {13}
}

@article{10.1109/TASLP.2013.2287052,
author = {Tachibana, Hideyuki and Ono, Nobutaka and Sagayama, Shigeki},
title = {Singing Voice Enhancement in Monaural Music Signals Based on Two-Stage Harmonic/Percussive Sound Separation on Multiple Resolution Spectrograms},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2287052},
doi = {10.1109/TASLP.2013.2287052},
abstract = {We propose a novel singing voice enhancement technique for monaural music audio signals, which is a quite challenging problem. Many singing voice enhancement techniques have been proposed recently. However, our approach is based on a quite different idea from these existing methods. We focused on the fluctuation of a singing voice and considered to detect it by exploiting two differently resolved spectrograms, one has rich temporal resolution and poor frequency resolution, while the other has rich frequency resolution and poor temporal resolution. On such two spectrograms, the shapes of fluctuating components are quite different. Based on this idea, we propose a singing voice enhancement technique that we call two-stage harmonic/percussive sound separation (HPSS). In this paper, we describe the details of two-stage HPSS and evaluate the performance of the method. The experimental results show that SDR, a commonly-used criterion on the task, was improved by around 4 dB, which is a considerably higher level than existing methods. In addition, we also evaluated the performance of the method as a preprocessing for melody estimation in music. The experimental results show that our singing voice enhancement technique considerably improved the performance of a simple pitch estimation technique. These results prove the effectiveness of the proposed method.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {228–237},
numpages = {10}
}

@article{10.1109/TASLP.2013.2285487,
author = {Shilin Liu and Khe Chai Sim},
title = {Temporally Varying Weight Regression: A Semi-Parametric Trajectory Model for Automatic Speech Recognition},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285487},
doi = {10.1109/TASLP.2013.2285487},
abstract = {Standard Hidden Markov Model (HMM) assumes that successive observations are independent to one another given the state sequence. This leads to a poor trajectory model for speech. Many explicit trajectory modeling techniques have been studied in the past to improve trajectory modeling for HMM. However, these techniques do not yield promising improvements over conventional HMM systems where differential parameters and Gaussian Mixture Model have been used implicitly to circumvent the poor trajectory modeling issue of HMM. Recently, semi-parametric trajectory modeling techniques based on temporally varying model parameters such as fMPE and pMPE have been shown to yield promising improvements over state-of-the-art systems on large vocabulary continuous speech recognition tasks. These techniques use high dimensional posterior features derived from a long span of acoustic features to model temporally varying attributes of the speech signal. Bases corresponding to these posterior features are then discriminatively estimated to yield temporally varying mean (fMPE) and precision matrix (pMPE) parameters. Motivated by the success of fMPE and pMPE, Temporally Varying Weight Regression (TVWR) was recently proposed to model HMM trajectory implicitly using time-varying Gaussian weights. In this paper, a complete formulation of TVWR is given based on a probabilistic modeling framework. Parameter estimation formulae using both maximum likelihood (ML) and minimum phone error (MPE) criteria are derived. Experimental results based on the Wall Street Journal ( CSR-WSJ0 + WSJ1) and Aurora 4 corpora show that consistent promising improvements over the standard HMM systems can be obtained in both the 20 k open vocabulary recognition task (NIST Nov'92 WSJ0) and 5 k closed vocabulary noisy speech recognition for both ML and MPE criteria.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {151–160},
numpages = {10}
}

@article{10.1109/TASLP.2013.2286920,
author = {Jin, Craig T. and Epain, Nicolas and Parthy, Abhaya},
title = {Design, Optimization and Evaluation of a Dual-Radius Spherical Microphone Array},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2286920},
doi = {10.1109/TASLP.2013.2286920},
abstract = {Spherical Microphone Arrays (SMAs) constitute a powerful tool for analyzing the spatial properties of sound fields. However, the performance of SMA-based signal processing algorithms ultimately depends on the physical characteristics of the array. In particular, the range of frequencies over which an SMA provide rich spatial information is conditioned by the size of the array, the angular position of the sensors and other factors. In this work, we investigate the design of SMAs offering a wider frequency range of operation than that offered by conventional designs. To achieve this goal, microphones are distributed both on and at a distance from the surface of a rigid spherical baffle. The contributions of the paper are as follows. First, we present a general framework for modeling SMAs whose sensors are located at different distances from the array center and calculating optimal filters for the decomposition of the sound field into spherical harmonic modes. Second, we present an optimization method to design multi-radius SMAs with an optimally wide frequency range of operation given the total number of sensors available and target spatial resolution. Lastly, based on the optimization results, we built a prototype dual-radius SMA with 64 microphones. We present measurement results for the prototype microphone array and compare these results with theory.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {193–204},
numpages = {12}
}

@article{10.1109/TASLP.2013.2286922,
author = {Mignot, Remi and Chardon, Gilles and Daudet, Laurent},
title = {Low Frequency Interpolation of Room Impulse Responses Using Compressed Sensing},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2286922},
doi = {10.1109/TASLP.2013.2286922},
abstract = {Measuring the Room Impulse Responses within a finite 3D spatial domain can require a very large number of measurements with standard uniform sampling. In this paper, we show that, at low frequencies, this sampling can be done with significantly less measurements, using some modal properties of the room. At a given temporal frequency, a plane wave approximation of the acoustic field leads to a sparse approximation, and therefore a compressed sensing framework can be used for its acquisition. This paper describes three different sparse models that can be constructed, and the corresponding estimation algorithms: two models that exploit the structured sparsity of the soundfield, with projections of the modes onto plane waves sharing the same wavenumber, and one that computes a sparse decomposition on a dictionary of independent plane waves with time / space variable separation. These models are compared numerically and experimentally, with an array of 120 microphones irregularly placed within a 2 \texttimes{}2 \texttimes{}2 m volume inside a room, with an approximate uniform distribution. One of the most challenging part is the design of estimation algorithms whose computational complexity remains tractable.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {205–216},
numpages = {12}
}

@article{10.1109/TASLP.2013.2285483,
author = {Markovic, D. and Kowalczyk, K. and Antonacci, F. and Hofmann, C. and Sarti, A. and Kellermann, W.},
title = {Estimation of Acoustic Reflection Coefficients Through Pseudospectrum Matching},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2285483},
doi = {10.1109/TASLP.2013.2285483},
abstract = {Estimating the geometric and reflective properties of the environment is important for a wide range of applications of space-time audio processing, from acoustic scene analysis to room equalization and spatial audio rendering. In this manuscript, we propose a methodology for frequency-subband in-situ estimation of the reflection coefficients of planar surfaces. This is a rather challenging task, as the reflection coefficients depend on the frequency and the angle of incidence and their estimate is highly sensitive to background noise and interfering sources. Our method is based on the assumption that we know the geometry of the reflectors; the position and the radiation pattern of the source; the position and the spatial response of the array. Applying beamforming algorithms on a single set of measured sensor data, we estimate the angular distribution of the acoustic energy (angular pseudospectrum) that impinges on a microphone array. We then apply a two-step iterative estimation technique based on an Expectation-Maximization (EM) algorithm. The first step estimates the scaling factors. The second one infers the reflection coefficients from the scaling factors. Under the assumption of additive white Gaussian noise, we finally determine the reflection coefficients with a Maximum Likelihood (ML) estimation method. The effectiveness and the accuracy of the proposed technique are assessed through experiments based on measured data.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {125–137},
numpages = {13}
}

@article{10.1109/TASLP.2013.2290861,
author = {Yuan Zeng and Hendriks, Richard C.},
title = {Distributed Delay and Sum Beamformer for Speech Enhancement via Randomized Gossip},
year = {2014},
issue_date = {January 2014},
publisher = {IEEE Press},
volume = {22},
number = {1},
issn = {2329-9290},
url = {https://doi.org/10.1109/TASLP.2013.2290861},
doi = {10.1109/TASLP.2013.2290861},
abstract = {In this paper, we investigate the use of randomized gossip for distributed speech enhancement and present a distributed delay and sum beamformer (DDSB). In a randomly connected wireless acoustic sensor network, the DDSB estimates the desired signal at each node by communicating only with its neighbors. We first provide the asynchronous DDSB (ADDSB) where each pair of neighboring nodes updates its data asynchronously. Then, we introduce an improved general distributed synchronous averaging (IGDSA) algorithm, which can be used in any connected network, and combine that with the DDSB algorithm where multiple node pairs can update their estimates simultaneously. For convergence analysis, we first provide bounds for the worst case averaging time of the ADDSB for the best and worst connected networks, and then we compare the convergence rate of the ADDSB with the original synchronous DDSB (OSDDSB) and the improved synchronous DDSB (ISDDSB) in regular networks. This convergence rate comparison is extended to randomly connected non-regular networks using simulations. The simulation results show that the DDSB using the different updating schemes converges to the optimal estimates of the centralized beamformer and that the proposed IGDSA algorithm converges much faster than the original synchronous communication scheme, in particular for non-regular networks. Moreover, comparisons are performed with several existing distributed speech enhancement methods from literature, assuming that the steering vector is given. In the simulated scenario, the proposed method leads to a slight performance improvement at the expense of a higher communication cost. The presented method is not constrained to a certain network topology (e.g., tree connected or fully connected), while this is the case for many of the reference methods.},
journal = {IEEE/ACM Trans. Audio, Speech and Lang. Proc.},
month = jan,
pages = {260–273},
numpages = {14}
}

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repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr