抄録
In this paper, we propose a method for constructing sharedstate triphone HMMs (SST-HMMs) within a practical Bayesian framework. In our method, Bayesian model selection criterion is derived for SST-HMM based on the Variational Bayesian approach. The appropriate phonetic decision tree structure of SST-HMM is found by using the criterion according to a given data set. This criterion, unlike the conventional MDL criterion, is applicable even in the case of insufficient amounts of data. We conduct two experiments on speaker independent word recognition in order to prove the effectiveness of the proposed method. The first experiment demonstrates that the Bayesian approach is valid for determining the tree structure. The second experiment demonstrates that the Bayesian criterion can design SST-HMMs with higher recognition performance than the MDL criterion when dealing with small amounts of data.
本文言語 | English |
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ページ | 2669-2672 |
ページ数 | 4 |
出版ステータス | Published - 2002 |
外部発表 | はい |
イベント | 7th International Conference on Spoken Language Processing, ICSLP 2002 - Denver, United States 継続期間: 2002 9月 16 → 2002 9月 20 |
Other
Other | 7th International Conference on Spoken Language Processing, ICSLP 2002 |
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国/地域 | United States |
City | Denver |
Period | 02/9/16 → 02/9/20 |
ASJC Scopus subject areas
- 言語および言語学
- 言語学および言語