Presentation 2006/12/15
Phoneme Recognition Based on Fisher Weight Map to Local Features
Shunsuke KATO, Tetsuya TAKIGUCHI, Yasuo ARIKI,
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Abstract(in English) In this paper, we propose a new feature extraction method based on higher-order local auto-correlation (HLAC) and Fisher weight map (FWM). Widely used MFCC features lack temporal dynamics. To solve this problem, 35 types of local auto-correlation features are computed within two-dimensional local regions. These local features are accumulated over more global regions by weighting high scores on the discriminative areas where the typical features among all phonemes are well expressed. This score map is called Fisher weight map. We verified the effectiveness of the HLAC and FWM through total phoneme recognition.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Local auto-correlation feature / Fisher weight map / Local feature / Phoneme recognition
Paper # NLC2006-62,SP2006-118
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Committee NLC
Conference Date 2006/12/15(1days)
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Registration To Natural Language Understanding and Models of Communication (NLC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Phoneme Recognition Based on Fisher Weight Map to Local Features
Sub Title (in English)
Keyword(1) Local auto-correlation feature
Keyword(2) Fisher weight map
Keyword(3) Local feature
Keyword(4) Phoneme recognition
1st Author's Name Shunsuke KATO
1st Author's Affiliation Graduated School of Science and Technology, Kobe University()
2nd Author's Name Tetsuya TAKIGUCHI
2nd Author's Affiliation Graduated School of Science and Technology, Kobe University
3rd Author's Name Yasuo ARIKI
3rd Author's Affiliation Graduated School of Science and Technology, Kobe University
Date 2006/12/15
Paper # NLC2006-62,SP2006-118
Volume (vol) vol.106
Number (no) 442
Page pp.pp.-
#Pages 6
Date of Issue