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A Bayesian Approach to Hidden Semi-Markov Model Based Speech Synthesis
https://nitech.repo.nii.ac.jp/records/3401
https://nitech.repo.nii.ac.jp/records/3401deba10fe-a4ca-402e-981d-f48a62f4fa12
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (248.3 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2012-11-07 | |||||
タイトル | ||||||
言語 | en | |||||
タイトル | A Bayesian Approach to Hidden Semi-Markov Model Based Speech Synthesis | |||||
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_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Hashimoto, Kei
× Hashimoto, Kei× 南角, 吉彦× Tokuda, Keiichi |
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著者別名 | ||||||
言語 | en | |||||
言語 | ja | |||||
言語 | ja-Kana | |||||
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 | ||||||
言語 | en | |||||
言語 | ja | |||||
言語 | ja-Kana | |||||
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 |
INTERSPEECH 2009 10th Annual Conference of the International Speech Communication Association p. 1751-1754, 発行日 2009 |
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出版者 | ||||||
出版者 | International Speech Communication Association | |||||
著者版フラグ | ||||||
出版タイプ | AM | |||||
出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | This paper proposes a Bayesian approach to hidden semi-Markov model (HSMM) based speech synthesis. Recently, hiddenMarkov model (HMM) based speech synthesis based on theBayesian approach was proposed. The Bayesian approach is astatistical technique for estimating reliable predictive distributionsby treating model parameters as random variables. In theBayesian approach, all processes for constructing the system arederived from one single predictive distribution which exactlyrepresents the problem of speech synthesis. However, there isan inconsistency between training and synthesis: although thespeech is synthesized from HMMs with explicit state durationprobability distributions, HMMs are trained without them. Inthis paper, we introduce an HSMM, which is an HMM withexplicit state duration probability distributions, into the HMMbasedBayesian speech synthesis system. Experimental resultsshow that the use of HSMM improves the naturalness of thesynthesized speech. | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | Brighton, United KingdomSeptember 6-10, 2009 | |||||
関連サイト | ||||||
識別子タイプ | URI | |||||
関連識別子 | http://www.isca-speech.org/archive/interspeech_2009/i09_1751.html | |||||
関連名称 | http://www.isca-speech.org/archive/interspeech_2009/i09_1751.html | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |