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Parameter Sharing in Mixture of Factor Analyzers for Speaker Identification
https://nitech.repo.nii.ac.jp/records/5234
https://nitech.repo.nii.ac.jp/records/523494163940-fdc1-417f-9121-57b995b7cb99
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (556.6 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 | Parameter Sharing in Mixture of Factor Analyzers for Speaker Identification | |||||
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 |
Yamamoto, Hiroyoshi
× Yamamoto, Hiroyoshi× 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. 418-424, 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 parameter tying structures of a mixture of factor analyzers (MFA) and discriminative training of MFA for speaker identification. The parameters of factor loading matrices or diagonal matrices are shared in different mixtures of MFA. Then, minimum classification error (MCE) training is applied to the MFA parameters to enhance the discrimination ability. The result of a text-independent speaker identification experiment shows that MFA outperforms the conventional Gaussian mixture model (GMM) with diagonal or full covariance matrices and achieves the best performance when sharing the diagonal matrices, resulting in a relative gain of 26% over the GMM with diagonal covariance matrices. The improvement is more significant especially in sparse training data condition. The recognition performance is further improved by MCE training with an additional gain of 3% error reduction. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |