IEEE Transactions on Audio Speech and Language Processing最新文献_第6页

Noise Model Transfer: Novel Approach to Robustness Against Nonstationary Noise 噪声模型转移:抗非平稳噪声鲁棒性的新方法

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2272513

Takuya Yoshioka, T. Nakatani

引用次数: 11

An Experimental Analysis on Integrating Multi-Stream Spectro-Temporal, Cepstral and Pitch Information for Mandarin Speech Recognition 融合多流谱时、倒谱和音高信息的普通话语音识别实验分析

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2263803

Yow-Bang Wang, Shang-Wen Li, Lin-Shan Lee

{"title":"An Experimental Analysis on Integrating Multi-Stream Spectro-Temporal, Cepstral and Pitch Information for Mandarin Speech Recognition","authors":"Yow-Bang Wang, Shang-Wen Li, Lin-Shan Lee","doi":"10.1109/TASL.2013.2263803","DOIUrl":"https://doi.org/10.1109/TASL.2013.2263803","url":null,"abstract":"Gabor features have been proposed for extracting spectro-temporal modulation information from speech signals, and have been shown to yield large improvements in recognition accuracy. We use a flexible Tandem system framework that integrates multi-stream information including Gabor, MFCC, and pitch features in various ways, by modeling either or both of the tone and phoneme variations in Mandarin speech recognition. We use either phonemes or tonal phonemes (tonemes) as either the target classes of MLP posterior estimation and/or the acoustic units of HMM recognition. The experiments yield a comprehensive analysis on the contributions to recognition accuracy made by either of the feature sets. We discuss their complementarities in tone, phoneme, and toneme classification. We show that Gabor features are better for recognition of vowels and unvoiced consonants, while MFCCs are better for voiced consonants. Also, Gabor features are capable of capturing changes in signals across time and frequency bands caused by Mandarin tone patterns, while pitch features further offer extra tonal information. This explains why the integration of Gabor, MFCC, and pitch features offers such significant improvements.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2263803","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Reliable Accent-Specific Unit Generation With Discriminative Dynamic Gaussian Mixture Selection for Multi-Accent Chinese Speech Recognition 基于判别动态高斯混合选择的汉语多口音语音识别中可靠的口音单位生成

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2265087

Chao Zhang, Yi Liu, Yunqing Xia, Xuan Wang, Chin-Hui Lee

{"title":"Reliable Accent-Specific Unit Generation With Discriminative Dynamic Gaussian Mixture Selection for Multi-Accent Chinese Speech Recognition","authors":"Chao Zhang, Yi Liu, Yunqing Xia, Xuan Wang, Chin-Hui Lee","doi":"10.1109/TASL.2013.2265087","DOIUrl":"https://doi.org/10.1109/TASL.2013.2265087","url":null,"abstract":"In this paper, we propose a discriminative dynamic Gaussian mixture selection (DGMS) strategy to generate reliable accent-specific units (ASUs) for multi-accent speech recognition. Time-aligned phone recognition is used to generate the ASUs that model accent variations explicitly and accurately. DGMS reconstructs and adjusts a pre-trained set of hidden Markov model (HMM) state densities to build dynamic observation densities for each input speech frame. A discriminative minimum classification error criterion is adopted to optimize the sizes of the HMM state observation densities with a genetic algorithm (GA). To the author's knowledge, the discriminative optimization for DGMS accomplishes discriminative training of discrete variables that is first proposed. We found the proposed framework is able to cover more multi-accent changes, thus reduce some performance loss in pruned beam search, without increasing the model size of the original acoustic model set. Evaluation on three typical Chinese accents, Chuan, Yue and Wu, shows that our approach outperforms traditional acoustic model reconstruction techniques with a syllable error rate reduction of 8.0%, 5.5% and 5.0%, respectively, while maintaining a good performance on standard Putonghua speech.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2265087","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62890303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems 连接主义语音识别系统中说话人自适应的厄米多项式

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2270370

S. Siniscalchi, Jinyu Li, Chin-Hui Lee

{"title":"Hermitian Polynomial for Speaker Adaptation of Connectionist Speech Recognition Systems","authors":"S. Siniscalchi, Jinyu Li, Chin-Hui Lee","doi":"10.1109/TASL.2013.2270370","DOIUrl":"https://doi.org/10.1109/TASL.2013.2270370","url":null,"abstract":"Model adaptation techniques are an efficient way to reduce the mismatch that typically occurs between the training and test condition of any automatic speech recognition (ASR) system. This work addresses the problem of increased degradation in performance when moving from speaker-dependent (SD) to speaker-independent (SI) conditions for connectionist (or hybrid) hidden Markov model/artificial neural network (HMM/ANN) systems in the context of large vocabulary continuous speech recognition (LVCSR). Adapting hybrid HMM/ANN systems on a small amount of adaptation data has been proven to be a difficult task, and has been a limiting factor in the widespread deployment of hybrid techniques in operational ASR systems. Addressing the crucial issue of speaker adaptation (SA) for hybrid HMM/ANN system can thereby have a great impact on the connectionist paradigm, which will play a major role in the design of next-generation LVCSR considering the great success reported by deep neural networks - ANNs with many hidden layers that adopts the pre-training technique - on many speech tasks. Current adaptation techniques for ANNs based on injecting an adaptable linear transformation network connected to either the input, or the output layer are not effective especially with a small amount of adaptation data, e.g., a single adaptation utterance. In this paper, a novel solution is proposed to overcome those limits and make it robust to scarce adaptation resources. The key idea is to adapt the hidden activation functions rather than the network weights. The adoption of Hermitian activation functions makes this possible. Experimental results on an LVCSR task demonstrate the effectiveness of the proposed approach.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2270370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62891042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 79

Blind Channel Magnitude Response Estimation in Speech Using Spectrum Classification 基于频谱分类的语音盲信道幅度响应估计

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-10-01 DOI: 10.1109/TASL.2013.2270406

N. Gaubitch, M. Brookes, P. Naylor

引用次数: 20

Video-Aided Model-Based Source Separation in Real Reverberant Rooms 真实混响室内基于视频辅助模型的声源分离

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-09-01 DOI: 10.1109/TASL.2013.2261814

Muhammad Salman Khan, S. M. Naqvi, Ata ur-Rehman, Wenwu Wang, J. Chambers

{"title":"Video-Aided Model-Based Source Separation in Real Reverberant Rooms","authors":"Muhammad Salman Khan, S. M. Naqvi, Ata ur-Rehman, Wenwu Wang, J. Chambers","doi":"10.1109/TASL.2013.2261814","DOIUrl":"https://doi.org/10.1109/TASL.2013.2261814","url":null,"abstract":"Source separation algorithms that utilize only audio data can perform poorly if multiple sources or reverberation are present. In this paper we therefore propose a video-aided model-based source separation algorithm for a two-channel reverberant recording in which the sources are assumed static. By exploiting cues from video, we first localize individual speech sources in the enclosure and then estimate their directions. The interaural spatial cues, the interaural phase difference and the interaural level difference, as well as the mixing vectors are probabilistically modeled. The models make use of the source direction information and are evaluated at discrete time-frequency points. The model parameters are refined with the well-known expectation-maximization (EM) algorithm. The algorithm outputs time-frequency masks that are used to reconstruct the individual sources. Simulation results show that by utilizing the visual modality the proposed algorithm can produce better time-frequency masks thereby giving improved source estimates. We provide experimental results to test the proposed algorithm in different scenarios and provide comparisons with both other audio-only and audio-visual algorithms and achieve improved performance both on synthetic and real data. We also include dereverberation based pre-processing in our algorithm in order to suppress the late reverberant components from the observed stereo mixture and further enhance the overall output of the algorithm. This advantage makes our algorithm a suitable candidate for use in under-determined highly reverberant settings where the performance of other audio-only and audio-visual methods is limited.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2261814","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889329","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Sound Field Classification in Small Microphone Arrays Using Spatial Coherences 基于空间相干的小型传声器阵列声场分类

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-09-01 DOI: 10.1109/TASL.2013.2261813

R. Scharrer, M. Vorländer

{"title":"Sound Field Classification in Small Microphone Arrays Using Spatial Coherences","authors":"R. Scharrer, M. Vorländer","doi":"10.1109/TASL.2013.2261813","DOIUrl":"https://doi.org/10.1109/TASL.2013.2261813","url":null,"abstract":"The quality and performance of many multi-channel signal processing strategies in microphone arrays as well as mobile devices for the enhancement of speech intelligibility and audio quality depends to a large extent on the acoustic sound field that they are exposed to. As long as the assumption on the sound field is not met, the performance decreases significantly and may even yield worse results for the user than an unprocessed signal. Current hearing aids provide the user for instance with different programs to adapt the signal processing to the acoustic situation. Signal classification describes the signal content and not the type of sound field. Therefore, a further classification of the sound field, in addition to the signal classification, would increase the possibilities for an optimal adaption of the automatic program selection and the signal processing methods in mobile devices. To this end a sound field classification method is proposed that is based on the complex coherences between the input signals of distributed acoustic sensors. In addition to the general approach an exemplary setup of a hearing aid equipped with two microphone sensors is discussed. As only coherences are used, the method classifies the sound field regardless of the signal carried by it. This approach complements and extends the current signal classification approach used in common mobile devices. The method was successfully verified with simulated audio input signals and with real life examples.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2261813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Musical Instrument Recognition in Polyphonic Audio Using Missing Feature Approach 基于缺失特征方法的复调音频乐器识别

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-09-01 DOI: 10.1109/TASL.2013.2248720

D. Giannoulis, Anssi Klapuri

{"title":"Musical Instrument Recognition in Polyphonic Audio Using Missing Feature Approach","authors":"D. Giannoulis, Anssi Klapuri","doi":"10.1109/TASL.2013.2248720","DOIUrl":"https://doi.org/10.1109/TASL.2013.2248720","url":null,"abstract":"A method is described for musical instrument recognition in polyphonic audio signals where several sound sources are active at the same time. The proposed method is based on local spectral features and missing-feature techniques. A novel mask estimation algorithm is described that identifies spectral regions that contain reliable information for each sound source, and bounded marginalization is then used to treat the feature vector elements that are determined to be unreliable. The mask estimation technique is based on the assumption that the spectral envelopes of musical sounds tend to be slowly-varying as a function of log-frequency and unreliable spectral components can therefore be detected as positive deviations from an estimated smooth spectral envelope. A computationally efficient algorithm is proposed for marginalizing the mask in the classification process. In simulations, the proposed method clearly outperforms reference methods for mixture signals. The proposed mask estimation technique leads to a recognition accuracy that is approximately half-way between a trivial all-one mask (all features are assumed reliable) and an ideal “oracle” mask.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2248720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62888540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 32

Characterization of Multiple Transient Acoustical Sources From Time-Transform Representations 多瞬态声源的时间变换表征

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-09-01 DOI: 10.1109/TASL.2013.2263141

N. Wachowski, M. Azimi-Sadjadi

引用次数: 3

A Multichannel MMSE-Based Framework for Speech Source Separation and Noise Reduction 一种基于多通道mmse的语音源分离与降噪框架

IEEE Transactions on Audio Speech and Language Processing Pub Date : 2013-09-01 DOI: 10.1109/TASL.2013.2263137

M. Souden, S. Araki, K. Kinoshita, T. Nakatani, H. Sawada

{"title":"A Multichannel MMSE-Based Framework for Speech Source Separation and Noise Reduction","authors":"M. Souden, S. Araki, K. Kinoshita, T. Nakatani, H. Sawada","doi":"10.1109/TASL.2013.2263137","DOIUrl":"https://doi.org/10.1109/TASL.2013.2263137","url":null,"abstract":"We propose a new framework for joint multichannel speech source separation and acoustic noise reduction. In this framework, we start by formulating the minimum-mean-square error (MMSE)-based solution in the context of multiple simultaneous speakers and background noise, and outline the importance of the estimation of the activities of the speakers. The latter is accurately achieved by introducing a latent variable that takes N+1 possible discrete states for a mixture of N speech signals plus additive noise. Each state characterizes the dominance of one of the N+1 signals. We determine the posterior probability of this latent variable, and show how it plays a twofold role in the MMSE-based speech enhancement. First, it allows the extraction of the second order statistics of the noise and each of the speech signals from the noisy data. These statistics are needed to formulate the multichannel Wiener-based filters (including the minimum variance distortionless response). Second, it weighs the outputs of these linear filters to shape the spectral contents of the signals' estimates following the associated target speakers' activities. We use the spatial and spectral cues contained in the multichannel recordings of the sound mixtures to compute the posterior probability of this latent variable. The spatial cue is acquired by using the normalized observation vector whose distribution is well approximated by a Gaussian-mixture-like model, while the spectral cue can be captured by using a pre-trained Gaussian mixture model for the log-spectra of speech. The parameters of the investigated models and the speakers' activities (posterior probabilities of the different states of the latent variable) are estimated via expectation maximization. Experimental results including comparisons with the well-known independent component analysis and masking are provided to demonstrate the efficiency of the proposed framework.","PeriodicalId":55014,"journal":{"name":"IEEE Transactions on Audio Speech and Language Processing","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/TASL.2013.2263137","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62889428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 101