Presentation 1997/9/12
Word Spotting Using Variable Length Segment Prototypes
Takashi IKEDA, Shoji KAJITA, Kazuya TAKEDA, Fumitada ITAKURA,
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Abstract(in English) In speech recognition using Hidden Markov Models (HMM), time warping is realized by state transitions. In this case, we match incoming speech sequences using identical statics during the interval in which the HMM stays in the same state by way of a self-transition (self-transition interval). Therefore, HMM does not take into account local dynamics of speech in a self-transition interval. In this paper VLSP (Variable Length Segment Prototypes) is proposed as a basic statistical representation of 'local' speech dynamics. The effectiveness of using VSLP is confirmed through word spotting experiments where the proposed method improves the word detection accuracy by 10~20%.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) spotting / duration / 'local' dynamic / segment / false alarm
Paper # SP97-49
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Conference Information
Committee SP
Conference Date 1997/9/12(1days)
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Paper Information
Registration To Speech (SP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Word Spotting Using Variable Length Segment Prototypes
Sub Title (in English)
Keyword(1) spotting
Keyword(2) duration
Keyword(3) 'local' dynamic
Keyword(4) segment
Keyword(5) false alarm
1st Author's Name Takashi IKEDA
1st Author's Affiliation Graduate School of Engineering, Nagoya University()
2nd Author's Name Shoji KAJITA
2nd Author's Affiliation Graduate School of Engineering, Nagoya University
3rd Author's Name Kazuya TAKEDA
3rd Author's Affiliation Graduate School of Engineering, Nagoya University
4th Author's Name Fumitada ITAKURA
4th Author's Affiliation Graduate School of Engineering, Nagoya University
Date 1997/9/12
Paper # SP97-49
Volume (vol) vol.97
Number (no) 249
Page pp.pp.-
#Pages 6
Date of Issue