Presentation 2004/12/13
Speaker Recognition without Feature Extraction Process
Tomoko MATSUI, Kunio TANABE,
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Abstract(in English) By employing the dual Penalized Logistic Regression Machine (dPLRM), this paper explores a speaker identification method which does not require feature extraction process depending on a prior knowledge. The induction machine can discover implicitly speaker characteristics relevant to discrimination only from a set of training data by the mechanism of the kernel regression. Our text-independent speaker identification experiments with training data uttered by 1 0 male speakers in three different sessions show that the proposed method is competitive with the conventional Gaussian mixture model (GMM)-based method with 26-dimensional Mel-frequency cepstrum (MFCC) feature even though our method handle directly coarse data of 256-dimensional log-power spectrum. It is also shown that our method outperforms the GMM-based method especially as the amount of training data becomes smaller.
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Keyword(in English) Speaker recognition / Speaker identification / Kernel regression / dual Penalized Logistic Regression Machine / implicit feature extraction
Paper # NLC2004-54,SP2004-94
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Committee NLC
Conference Date 2004/12/13(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Speaker Recognition without Feature Extraction Process
Sub Title (in English)
Keyword(1) Speaker recognition
Keyword(2) Speaker identification
Keyword(3) Kernel regression
Keyword(4) dual Penalized Logistic Regression Machine
Keyword(5) implicit feature extraction
1st Author's Name Tomoko MATSUI
1st Author's Affiliation The Institute of Statistical Mathematics()
2nd Author's Name Kunio TANABE
2nd Author's Affiliation The Institute of Statistical Mathematics
Date 2004/12/13
Paper # NLC2004-54,SP2004-94
Volume (vol) vol.104
Number (no) 538
Page pp.pp.-
#Pages 6
Date of Issue