Aggregate a Posteriori Linear Regression Adaptation of Hidden Markov Models

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概要

• 論文の詳細を見る

• We present a rapid and discriminative speaker adaptation algorithm for hidden Markov model (HMM) based speech recognition. The adaptation is based on the linear regression framework. Attractively, we estimate the regression matrices from speaker-specific adaptation data according to the aggregate a posteriori criterion, which is expressed in a form of classification error function. The aggregate a posteriori linear regression (AAPLR)is proposed to achieve discriminative adaptation so that the classification errors of adaptation data are minimized. The superiority of AAPLR to maximum a posteriori linear regression (MAPLR) is demonstrated. Different from minimum classification error linear repression (MCELR),AAPLR has closed-form solution to fulfill rapid adaptation. Experimental results reveal that AAPLR speaker adaptation does improve speech recognition performance with moderate computational cost compared to the maximum likelihood linear regression (MLLR), MAPLR and MCELR.

• 一般社団法人情報処理学会の論文
• 2004-12-20

著者

• Huang Chih-hsien

  Department Of Computer Science And Information Engineering National Cheng Kung University

• Chien Jen-Tzung

  Department of Computer Science and Information Engineering National Cheng Kung University

• Huang Chin-Hsien

  Department of Computer Science and Information Engineering National Cheng Kung University

関連論文

• Aggregate a Posteriori Linear Regression Adaptation of Hidden Markov Models
• Aggregate a Posteriori Linear Regression Adaptation of Hidden Markov Models
• Aggregate a Posteriori Linear Regression Adaptation of Hidden Markov Models

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