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北海道大学サステナビリティ・ウィーク2009  >
2009年アジア太平洋信号情報処理連合学会アニュアルサミット・国際会議  >

Component reduction technique for covariance matrix of multidimensional Gaussian distribution in Speech Recognition

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タイトル: Component reduction technique for covariance matrix of multidimensional Gaussian distribution in Speech Recognition
著者: Seyoshi, Eiichi 著作を一覧する
Yamamoto, Kazumasa 著作を一覧する
Nakagawa, Seiichi 著作を一覧する
発行日: 2009年10月 4日
出版者: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference, International Organizing Committee
誌名: Proceedings : APSIPA ASC 2009 : Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference
開始ページ: 644
終了ページ: 647
抄録: Recently, speech recognition systems are in practical use as an input device of a car navigation system, etc. However, since the computational and storage resources are usually limited for a car navigation system, these systems are required to achieve high recognition performance with the limited resource. In this paper, we propose a method that reduces the components of full covariance matrix considering only the dominant correlation components between static and dynamic feature parameters. The recognition experiment was performed by using the conventional and the proposed method, and both were compared. From the continuous syllable recognition result, we confirmed the effectiveness of proposed component reduction technique considering the correlation between parameters.
記述: APSIPA ASC 2009: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference. 4-7 October 2009. Sapporo, Japan. Poster session: Automatic Speech Recognition (6 October 2009).
資料タイプ: proceedings
URI: http://hdl.handle.net/2115/39778
出現コレクション:2009年アジア太平洋信号情報処理連合学会アニュアルサミット・国際会議 (2009 APSIPA Annual Summit and Conference)

 

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