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Deterministic Annealing EM Algorithm in Acoustic Modeling for Speaker and Speech Recognition
https://nitech.repo.nii.ac.jp/records/5231
https://nitech.repo.nii.ac.jp/records/52316772d736-2f61-4333-a8ba-340314b0ddf8
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (694.8 kB)
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Copyright(c)2005 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 | Deterministic Annealing EM Algorithm in Acoustic Modeling for Speaker and 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 |
Itaya, Yohei
× Itaya, Yohei× Zen, Heiga× 南角, 吉彦× Miyajima, Chiyomi× 徳田, 恵一× Kitamura, Tadashi |
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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 | 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 | |||||
著者別名 | ||||||
姓名 | 北村, 正 | |||||
書誌情報 |
IEICE transactions on information and systems 巻 E88-D, 号 3, p. 425-431, 発行日 2005-03-01 |
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出版者 | ||||||
出版者 | Institute of Electronics, Information and Communication Engineers | |||||
ISSN | ||||||
収録物識別子タイプ | ISSN | |||||
収録物識別子 | 09168532 | |||||
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 investigates the effectiveness of the DAEM (Deterministic Annealing EM) algorithm in acoustic modeling for speaker and speech recognition. Although the EM algorithm has been widely used to approximate the ML estimates, it has the problem of initialization dependence. To relax this problem, the DAEM algorithm has been proposed and confirmed the effectiveness in artificial small tasks. In this paper, we applied the DAEM algorithm to practical speech recognition tasks: speaker recognition based on GMMs and continuous speech recognition based on HMMs. Experimental results show that the DAEM algorithm can improve the recognition performance as compared to the standard EM algorithm with conventional initialization algorithms, especially in the flat start training for continuous speech recognition. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |