講演名 | 2004/12/13 Robust Acoustic Modeling for Speech Recognition , |
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抄録(英) | 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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キーワード(英) | acoustic modeling / information criterion / distance measure / MDL / SMAP |
資料番号 | NLC2004-42,SP2004-82 |
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研究会 | SP |
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開催期間 | 2004/12/13(から1日開催) |
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申込み研究会 | Speech (SP) |
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本文の言語 | ENG |
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タイトル(英) | Robust Acoustic Modeling for Speech Recognition |
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キーワード(1)(和/英) | / acoustic modeling |
第 1 著者 氏名(和/英) | / Koichi SHINODA |
第 1 著者 所属(和/英) | Department of Computer Science, Tokyo Institute of Technology |
発表年月日 | 2004/12/13 |
資料番号 | NLC2004-42,SP2004-82 |
巻番号(vol) | vol.104 |
号番号(no) | 541 |
ページ範囲 | pp.- |
ページ数 | 6 |
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