Acoustic model adaptation based on coarse/fine training of transfer vectors and its application to a speaker adaptation task

Shinji Watanabe, Atsushi Nakamura

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

In this paper, we propose a novel adaptation technique based on coarse/fine training of transfer vectors. We focus on transfer vector estimation of a Gaussian mean from an initial model to an adapted model. The transfer vector is decomposed into a direction vector and a scaling factor. By using tied-Gaussian class (coarse class) estimation for the direction vector, and by using individual Gaussian class (fine class) estimation for the scaling factor, we can obtain accurate transfer vectors with a small number of parameters. Simple training algorithms for transfer vector estimation are analytically derived using the variational Bayes, maximum a posteriori (MAP) and maximum likelihood methods. Speaker adaptation experiments show that our proposals clearly improve speech recognition performance for any amount of adaptation data, compared with conventional MAP adaptation.

本文言語English
ホスト出版物のタイトル8th International Conference on Spoken Language Processing, ICSLP 2004
出版社International Speech Communication Association
ページ2933-2936
ページ数4
出版ステータスPublished - 2004
外部発表はい
イベント8th International Conference on Spoken Language Processing, ICSLP 2004 - Jeju, Jeju Island, Korea, Republic of
継続期間: 2004 10 42004 10 8

Other

Other8th International Conference on Spoken Language Processing, ICSLP 2004
CountryKorea, Republic of
CityJeju, Jeju Island
Period04/10/404/10/8

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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