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Factor analysis of mixture of auto-associative neural networks for speaker verification

Factor analysis of mixture of auto-associative neural networ...

引用

Speaker and language Recognition Workshop, Odyssey 2012

作者： Garimella, Sri Hermansky, Hynek Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States

ISBN: (纸本)9789810730932

This paper introduces the theory of factor analysis of the mixture of Auto-Associative Neural Networks (AANNs) with application in speaker verification. First, we formulate the problem of learning a low-dimensional subspace in part of the mixture of AANNs parameter space, and subsequently derive the update equations by minimizing loss function of the mixture. Second, we apply this technique to build a neural network based speaker verification system, in which the low-dimensional subspace is trained to capture both speaker and channel variabilities. This low-dimensional (or i-vector) representation is used as features for the probabilistic linear discriminant analysis (PLDA) model, as in state-of-the-art speaker verification systems. The proposed factor analysis approach shows promising results on the NIST-08 speaker recognition evaluation (SRE), and yields 18% relative improvement in minimum detection cost function (minDCF) over the previously proposed subspace based mixture of AANNs system. © Odyssey 2012 - Speaker and language Recognition Workshop. All rights reserved.

关键词： Mixtures

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Vowel-category based Short Utterance Speaker Recognition

Vowel-category based Short Utterance Speaker Recognition

引用

2012 International Conference on Systems and Informatics, ICSAI 2012

作者： Fatima, Nakhat Zheng, Thomas Fang Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University 100084 Beijing China

ISBN: (纸本)9781467301992

The impact of Short Utterances in Speaker Recognition is of significant importance. Despite the advancements in short utterance speaker recognition (SUSR), text dependence and the role of phonemes in carrying speaker information needs further investigation. This paper presents a novel method of using vowel categories for SUSR. We define Vowel Categories (VC's) considering Chinese and English languages. After recognition and extraction of phonemes, the obtained vowels are divided into VC's, which are then used to develop Universal Background VC Models (UBVCM) for each VC. Conventional GMM-UBM system is used for training and testing. The proposed categories give minimum EERs of 13.76%, 14.03% and 16.18% for 3, 2 and 1 second respectively. Experimental results show that in text dependent SUSR, significant speaker-specific information is present at phoneme level. The similar properties of phonemes can be used such that accurate speech recognition is not required, rather Phoneme Categories can be used effectively for SUSR. Also, it is shown that vowels contain large amount of speaker information, which remains undisturbed when VC are employed. © 2012 IEEE.

关键词： speech recognition

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Short utterance speaker recognition a research Agenda

Short utterance speaker recognition a research Agenda

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2012 International Conference on Systems and Informatics, ICSAI 2012

作者： Fatima, Nakhat Zheng, Thomas Fang Department of Computer Science and Technology Center for Speech and Language Technologies Tsinghua University 100084 Beijing China

ISBN: (纸本)9781467301992

Short Utterance Speaker Recognition (SUSR) is an important area of speaker recognition when only small amount of speech data is available for testing and training. We list the most commonly used state-of-the-art methods of speaker recognition and the significance of prosodic speaker recognition. A short survey of SUSR is hereby conducted, highlighting various methodologies when using short utterances to recognize speakers. We also specify future research directions in the field SUSR which, together with modern technologies and the ongoing research in prosodic speaker recognition, can lead to better results in speaker recognition. © 2012 IEEE.

关键词： Phoneme Categories Prosodic Speaker Recognition Short Utterance Speaker Recognition

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Adaptation transforms of auto-associative neural networks as features for speaker verification

Adaptation transforms of auto-associative neural networks as...

引用

Speaker and language Recognition Workshop, Odyssey 2012

作者： Thomas, Samuel Mallidi, Sri Harish Ganapathy, Sriram Hermansky, Hynek Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University Baltimore United States Human Language Technology Center of Excellence Johns Hopkins University Baltimore United States

ISBN: (纸本)9789810730932

We present a new approach of using Auto-Associative Neural Networks (AANNs) in the conventional GMM speaker verification framework with i-vector feature extraction and PLDA modeling. In this technique, an i-vector feature extractor is trained using adaptation parameters from a mixture of AANNs. In order to model parts of each speaker's acoustic space, a training objective function based on posterior probabilities of broad phonetic classes is used. The AANN based i-vectors are fused with GMM based i-vectors and a joint PLDA model is trained. The proposed approach provides promising results and significant gains when combined with baseline systems on the telephone conditions of NIST SRE 2010 and the recently concluded IARPA BEST 2011 speaker evaluations. © Odyssey 2012 - Speaker and language Recognition Workshop. All rights reserved.

关键词： speech recognition

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Multilingual MLP features for low-resource LVCSR systems

Multilingual MLP features for low-resource LVCSR systems

引用

International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Samuel Thomas Sriram Ganapathy Hynek Hermansky Center for Language and Speech Processing Department of Electrical and Computer Engineering Johns Hopkins University USA

We introduce a new approach to training multilayer perceptrons (MLPs) for large vocabulary continuous speech recognition (LVCSR) in new languages which have only few hours of annotated in-domain training data (for example, 1 hour of data). In our approach, large amounts of annotated out-of-domain data from multiple languages are used to train multilingual MLP systems without dealing with the different phoneme sets for these languages. Features extracted from these MLP systems are used to train LVCSR systems in the low-resource language similar to the Tandem approach. In our experiments, the proposed features provide a relative improvement of about 30% in an low-resource LVCSR setting with only one hour of training data.

关键词： Abstracts Nonhomogeneous media Vocabulary speech Hidden Markov models Switches

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Multilevel speech intelligibility for robust speaker recognition

Multilevel speech intelligibility for robust speaker recogni...

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International Conference on Acoustics, speech, and Signal processing (ICASSP)

作者： Sridhar Krishna Nemala Mounya Elhilali Department of Electrical and Computer Engineering Center for Speech and Language Processing Johns Hopkins University Baltimore MD USA

In the real world, natural conversational speech is an amalgam of speech segments, silences and environmental/ background and channel effects. Labeling the different regions of an acoustic signal according to their information levels would greatly benefit all automatic speech processing tasks. In the current work, we propose a novel segmentation approach based on a perception-based measure of speech intelligibility. Unlike segmentation approaches based on various forms of voice-activity detection (VAD), the proposed parsing approach exploits higher-level perceptual information about signal intelligibility levels. This labeling information is integrated into a novel multilevel framework for automatic speaker recognition task. The system processes the input acoustic signal along independent streams reflecting various levels of intelligibility and then fusing the decision scores from the multiple steams according to their intelligibility contribution. Our results show that the proposed system achieves significant improvements over standard baseline and VAD-based approaches, and attains a performance similar to the one obtained with oracle speech segmentation information.

关键词： speech Multilevel systems speech recognition Acoustics Speaker recognition speech processing NIST

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Emotion identification for evaluation of synthesized emotional speech

引用

6th International Conference on speech Prosody 2012, SP 2012

作者： Steidl, Stefan Polzehl, Tim Timothy Bunnell, H. Dou, Ying Muthukumar, Prasanna Kumar Perry, Daniel Prahallad, Kishore Vaughn, Callie Black, Alan W. Metze, Florian Computer Science Department 5 University Erlangen-Nuremberg Germany Deutsche Telekom Laboratories/Technische Universität Berlin Germany Nemours Biomedical Research Wilmington United States Center for Speech and Language Processing Johns Hopkins University Baltimore United States Language Technologies Institute Carnegie Mellon University Pittsburgh United States University of California Los Angeles United States International Institute of Information Technology Hyderabad India Computer Science Department Oberlin College Oberlin United States

ISBN: (纸本)9787560848693

In this paper, we propose to evaluate the quality of emotional speech synthesis by means of an automatic emotion identification system. We test this approach using five different parametric speech synthesis systems, ranging from plain non-emotional synthesis to full re-synthesis of pre-recorded speech. We compare the results achieved with the automatic system to those of human perception tests. While preliminary, our results indicate that automatic emotion identification can be used to assess the quality of emotional speech synthesis, potentially replacing time consuming and expensive human perception tests.

关键词： speech synthesis

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Grouping of Handwritten Bangla Basic Characters, Numerals and Vowel Modifiers for Multilayer Classification

Grouping of Handwritten Bangla Basic Characters, Numerals an...

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International Workshop on Frontiers in Handwriting Recognition

作者： Khondker Nayef Reza Mumit Khan Center for Research on Bangla Language Processing Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

For better performance in multilayer or hierarchical classification of handwritten text, appropriate grouping of similar symbols is very important. Here we aim to develop a reliable grouping schema for the similar looking basic characters, numerals and vowel modifiers of Bangla language. We experimented with thickened and thinned segmented handwritten text to compare which type of image is better for which group. For classification we chose Support Vector Machine (SVM) as it outperforms other classifiers in this field. We used both “one against one” and “one against all” strategies for multiclass SVM and compared their performance.

关键词： Support vector machines Handwriting recognition Character recognition Accuracy Training Compounds

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Short Utterance Speaker Recognition A research Agenda

Short Utterance Speaker Recognition A research Agenda

引用

International Conference on Systems and Informatics (ICSAI)

作者： Nakhat Fatima Thomas Fang Zheng Center for Speech and Language Technologies Division of Technical Innovation and Development Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

关键词： Speaker recognition speech Hidden Markov models speech recognition Acoustics Training Humans

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Vowel-category based Short Utterance Speaker Recognition

Vowel-category based Short Utterance Speaker Recognition

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International Conference on Systems and Informatics (ICSAI)

关键词： Speaker recognition speech speech recognition Training Testing Feature extraction Hidden Markov models

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