Invited: Robust Acoustic Modeling for Speech Recognition (国際ワークショップ"Beyond HMM")
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概要
- 論文の詳細を見る
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.
- 一般社団法人情報処理学会の論文
- 2004-12-20
著者
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SHINODA Koichi
Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Instit
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Shinoda Koichi
Department Of Computer Science Tokyo Institute Of Technology
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Shinoda Koichi
Department Of Computer Science Graduate School Of Information Science And Engineering Tokyo Institut
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