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Bayesian Context Clustering Using Cross Validation for Speech Recognition
https://nitech.repo.nii.ac.jp/records/5510
https://nitech.repo.nii.ac.jp/records/55107beb1c0f-c97a-4b88-9152-9c406ae44c7c
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (463.1 kB)
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Copyright2011 IEICE http://search.ieice.org/index.html
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Item type | 学術雑誌論文 / Journal Article(1) | |||||
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公開日 | 2012-11-07 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | Bayesian Context Clustering Using Cross Validation for Speech Recognition | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||
資源タイプ | journal article | |||||
著者 |
Hashimoto, Kei
× Hashimoto, Kei× Zen, Heiga× 南角, 吉彦× Lee, Akinobu× 徳田, 恵一 |
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著者別名 | ||||||
姓名 | 橋本, 佳 | |||||
著者別名 | ||||||
姓名 | Nankaku, Yoshihiko | |||||
言語 | en | |||||
姓名 | 南角, 吉彦 | |||||
言語 | ja | |||||
姓名 | ナンカク, ヨシヒコ | |||||
言語 | ja-Kana | |||||
著者別名 | ||||||
姓名 | 李, 晃伸 | |||||
著者別名 | ||||||
姓名 | Tokuda, Keiichi | |||||
言語 | en | |||||
姓名 | 徳田, 恵一 | |||||
言語 | ja | |||||
姓名 | トクダ, ケイイチ | |||||
言語 | ja-Kana | |||||
書誌情報 |
IEICE transactions on information and systems 巻 E94-D, 号 3, p. 668-678, 発行日 2011-03-01 |
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出版者 | ||||||
出版者 | Institute of Electronics, Information and Communication Engineers | |||||
書誌レコードID(NCID) | ||||||
収録物識別子タイプ | NCID | |||||
収録物識別子 | AA10826272 | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | This paper proposes Bayesian context clustering using cross validation for hidden Markov model (HMM) based speech recognition. The Bayesian approach is a statistical technique for estimating reliable predictive distributions by treating model parameters as random variables. The variational Bayesian method, which is widely used as an efficient approximation of the Bayesian approach, has been applied to HMM-based speech recognition, and it shows good performance. Moreover, the Bayesian approach can select an appropriate model structure while taking account of the amount of training data. Since prior distributions which represent prior information about model parameters affect estimation of the posterior distributions and selection of model structure (e.g., decision tree based context clustering), the determination of prior distributions is an important problem. However, it has not been thoroughly investigated in speech recognition, and the determination technique of prior distributions has not performed well. The proposed method can determine reliable prior distributions without any tuning parameters and select an appropriate model structure while taking account of the amount of training data. Continuous phoneme recognition experiments show that the proposed method achieved a higher performance than the conventional methods. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |