Presentation 2014-12-15
Investigation of Deep Neural Network and Cross-adaptation for Voice Activity Detection in Meeting Speech
Akihiro NAKADANI, Longbiao WANG, Atsuhiko KAI,
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Abstract(in English) In voice activity detection(VAD), performance largely decreases under the influence of noise and reverberation. In this paper, we focus on a VAD technique with deep neural network(DNN) framework and propose the environmental adaptation methods of the VAD model. As for the unsupervised adaptation of such discriminative models, utilizing erroneous identification result as target signal often reproduces an error and degrades the performance. As for unsupervised adaptation techniques in ASR systems, cross adaptation method using different types of models, has been proposed. Our cross-adaptation method improves the VAD performance by using recognition output of GMM and SVM which are different from DNN in terms of error tendency for unsupervised adaptation and achieves a robust VAD system capable of adapting the noisy and reverberant environment.
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Keyword(in English) Voice activity detection(VAD) / Deep neural network(DNN) / Cross-adaptation / Noisy and reverberant speech / Environmental adaptation
Paper # SP2014-107
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Committee SP
Conference Date 2014/12/8(1days)
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Registration To Speech (SP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Investigation of Deep Neural Network and Cross-adaptation for Voice Activity Detection in Meeting Speech
Sub Title (in English)
Keyword(1) Voice activity detection(VAD)
Keyword(2) Deep neural network(DNN)
Keyword(3) Cross-adaptation
Keyword(4) Noisy and reverberant speech
Keyword(5) Environmental adaptation
1st Author's Name Akihiro NAKADANI
1st Author's Affiliation Shizuoka University()
2nd Author's Name Longbiao WANG
2nd Author's Affiliation Nagaoka University of Technology
3rd Author's Name Atsuhiko KAI
3rd Author's Affiliation Shizuoka University
Date 2014-12-15
Paper # SP2014-107
Volume (vol) vol.114
Number (no) 365
Page pp.pp.-
#Pages 6
Date of Issue