Unsupervised Phoneme Model Training Based on the Sufficient HMM Statistics from Selected Speakers

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概要

• 論文の詳細を見る

• This paper describes an efficient method of unsupervised speaker adaptation. This method is based on (1) selecting a subset of speakers who are acoustically close to a test speaker, and (2) calculating adapted model parameters according to the previously stored sufficient statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are necessary for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers' data, a quick adaptation can be done. Experimental results show that the proposed method attains better improvement than MLLR from the speaker-independent model.

• 社団法人電子情報通信学会の論文
• 2002-03-01

著者

• Shikano Kiyohiro

  Institute Of Science And Technology

• Yoshizawa Shinichi

  Matsushita Electric Industrial Co. Ltd.:laboratories Of Image Information Science And Technology

• Baba Akira

  Laboratories Of Image Information Science And Technology:matsushita Electric Works Ltd.

• Lee Akinobu

  Institute Of Science And Technology

• MATSUNAMI Kanako

  Institute of Science and Technology

• MERA Yuichiro

  Institute of Science and Technology

• YAMADA Miichi

  Institute of Science and Technology

関連論文

• Elderly Acoustic Models for Large Vocabulary Continuous Speech Recognition
• Unsupervised Phoneme Model Training Based on the Sufficient HMM Statistics from Selected Speakers

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