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Full Covariance State Duration Modeling for HMM-Based Speech Synthesis
https://nitech.repo.nii.ac.jp/records/3453
https://nitech.repo.nii.ac.jp/records/34531ef7bfc4-a7db-422e-ade2-38d9c3bcbc47
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (207.3 kB)
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c2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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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 | Full Covariance State Duration Modeling for HMM-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 |
Lu, Heng
× Lu, Heng× Wu, Yi Jian× Dai, Li Rong× Wang, Ren hua |
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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 |
ICASSP 2009. IEEE International Conference on Acoustics, Speech and Signal Processing, 2009. p. 4033-4036, 発行日 2009-04-24 |
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出版者 | ||||||
出版者 | Institute of Electrical and Electronics Engineers | |||||
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 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | http://dx.doi.org/10.1109/ICASSP.2009.4960513 | |||||
関連名称 | 10.1109/ICASSP.2009.4960513 | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | This paper proposes a state duration modeling method using full covariance matrix for HMM-based speech synthesis. In this method, a full covariance matrix instead of the conventional diagonal covariance matrix is adopted in the multi-dimensional Gaussian distribution to model the state duration of each context-dependent phoneme. At synthesis stage, the state durations are predicted using the clustered context-dependent distributions with full covariance matrices. Experimental results show that the synthesized speech using full-covariance state duration models is more natural than the conventional method when we change the speaking rate of synthesized speech. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |