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Text-Independent Speaker Identification Using Gaussian Mixture Models Based on Multi-Space Probability Distribution
https://nitech.repo.nii.ac.jp/records/4967
https://nitech.repo.nii.ac.jp/records/49672df4428d-f00c-42c2-9fcb-eb9cc00b191b
名前 / ファイル | ライセンス | アクション |
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本文_fulltext (947.1 kB)
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Copyright (c) 2001 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 | ||||||
言語 | en | |||||
タイトル | Text-Independent Speaker Identification Using Gaussian Mixture Models Based on Multi-Space Probability Distribution | |||||
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 |
Miyajima, Chiyomi
× Miyajima, Chiyomi× Hattori, Yosuke× 徳田, 恵一× Masuko, Takashi× Kobayashi, Takao× Kitamura, Tadashi |
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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 | |||||
著者別名 | ||||||
姓名 | 北村, 正 | |||||
書誌情報 |
IEICE transactions on information and systems 巻 E84-D, 号 7, p. 847-855, 発行日 2001-07-01 |
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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 presents a new approach to modeling speech spectra and pitch for text-independent speaker identification using Gaussian mixture models based on multi-space probability distribution (MSD-GMM). MSD-GMM allows us to model continuous pitch values of voiced frames and discrete symbols for unvoiced frames in a unified framework. Spectral and pitch features are jointly modeled by a two-stream MSD-GMM. We derive maximum likelihood (ML) estimation formulae and minimum classification error (MCE) training procedure for MSD-GMM parameters. The MSD-GMM speaker models are evaluated for text-independent speaker identification tasks. The experimental results show that the MSD-GMM can efficiently model spectral and pitch features of each speaker and outperforms conventional speaker models. The results also demonstrate the utility of the MCE training of the MSD-GMM parameters and the robustness for the inter-session variability. | |||||
フォーマット | ||||||
内容記述タイプ | Other | |||||
内容記述 | application/pdf |