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 Japanese) | (See Japanese page) |
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 |
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Conference Date | 2006/12/15(1days) |
Place (in Japanese) | (See Japanese page) |
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Registration To | Natural Language Understanding and Models of Communication (NLC) |
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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 |
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