Presentation 2004/12/13
Robust Acoustic Modeling for Speech Recognition
Koichi SHINODA,
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Abstract(in English) While Hidden Markov Models (HMMs) have been successfully applied to automatic speech recognition, they are not still robust enough against differences in speakers, speaking-styles, and environmental noises. To tackle this problem, we need to study the inner structure of speech by using large corpus and rich computational power. In this direction, the model size tends to be increase and hence the data insufficiency problem becomes more serious. In this paper, we focus on robust modeling against data insufficiency. Approaches based on information criteria such as Minimum Description Length and structural approaches in which models are changed according to the amount of data availabl are discussed.. While these techniques have been important for HMM research, it will be more important in the research beyond HMM.
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Keyword(in English) acoustic modeling / information criterion / distance measure / MDL / SMAP
Paper # NLC2004-42,SP2004-82
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Committee NLC
Conference Date 2004/12/13(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Robust Acoustic Modeling for Speech Recognition
Sub Title (in English)
Keyword(1) acoustic modeling
Keyword(2) information criterion
Keyword(3) distance measure
Keyword(4) MDL
Keyword(5) SMAP
1st Author's Name Koichi SHINODA
1st Author's Affiliation Department of Computer Science, Tokyo Institute of Technology()
Date 2004/12/13
Paper # NLC2004-42,SP2004-82
Volume (vol) vol.104
Number (no) 538
Page pp.pp.-
#Pages 6
Date of Issue