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A Context Clustering Technique for Average Voice Models
https://nitech.repo.nii.ac.jp/records/5124
https://nitech.repo.nii.ac.jp/records/5124d000d284-5f08-4837-b661-7c9aced79c24
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
本文_fulltext (773.0 kB)
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Copyright (c) 2003 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 | 2012-05-24 | |||||||||||||||||||||
| タイトル | ||||||||||||||||||||||
| タイトル | A Context Clustering Technique for Average Voice Models | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
| 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 |
Yamagishi, Junichi
× Yamagishi, Junichi
× Tamura, Masatsune
× Masuko, Takashi
× 徳田, 恵一
× Kobayashi, Takao
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| 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 |
en : IEICE transactions on information and systems 巻 E86-D, 号 3, p. 534-542, 発行日 2003-03-01 |
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| 出版者 | ||||||||||||||||||||||
| 出版者 | Institute of Electronics, Information and Communication Engineers | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
| ISSN | ||||||||||||||||||||||
| 収録物識別子タイプ | ISSN | |||||||||||||||||||||
| 収録物識別子 | 0916-8532 | |||||||||||||||||||||
| item_10001_source_id_32 | ||||||||||||||||||||||
| 収録物識別子タイプ | NCID | |||||||||||||||||||||
| 収録物識別子 | AA10826272 | |||||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||||
| 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 describes a new context clustering technique for average voice model, which is a set of speaker independent speech synthesis units. In the technique, we first train speaker dependent models using multi-speaker speech database, and then construct a decision tree common to these speaker dependent models for context clustering. When a node of the decision tree is split, only the context related questions which are applicable to all speaker dependent models are adopted. As a result, every node of the decision tree always has training data of all speakers. After construction of the decision tree, all speaker dependent models are clustered using the common decision tree and a speaker independent model, i.e., an average voice model is obtained by combining speaker dependent models. From the results of subjective tests, we show that the average voice models trained using the proposed technique can generate more natural sounding speech than the conventional average voice models. | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
