Presentation 1995/11/16
Variable-Order Statistical Language Modeling for Continuous Speech Recognition
Hirokazu Masataki, Shoichi Matsunaga, Yoshinori Sagisaka,
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Abstract(in English) In this paper, we propose a variable-order N-gram that describe training corpus efficiently by limited number of parameters. Starting from POS bigrams, the proposed scheme creates variable-order N-grams by splitting a POS into finer groups and by adding frequent consecutive word sequences as word-classes. This word-class splitting and consecutive word grouping are carried out incrementally by minimizing the total entropy. Experiments showed that the perplexity of the proposed model for the test corpus is lower than conventional trigram and that this model requires quite smaller number of statistical parameters. By applying this model to speech recognition, we get a better recognition rate than conventionalfixed N-grams.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Continuous Speech Recognition / Statistical Language Modeling / N-gram / perplexity
Paper # SP95-73
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Committee SP
Conference Date 1995/11/16(1days)
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Registration To Speech (SP)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Variable-Order Statistical Language Modeling for Continuous Speech Recognition
Sub Title (in English)
Keyword(1) Continuous Speech Recognition
Keyword(2) Statistical Language Modeling
Keyword(3) N-gram
Keyword(4) perplexity
1st Author's Name Hirokazu Masataki
1st Author's Affiliation ATR Interpreting Telecommunications Research Laboratories()
2nd Author's Name Shoichi Matsunaga
2nd Author's Affiliation ATR Interpreting Telecommunications Research Laboratories
3rd Author's Name Yoshinori Sagisaka
3rd Author's Affiliation ATR Interpreting Telecommunications Research Laboratories
Date 1995/11/16
Paper # SP95-73
Volume (vol) vol.95
Number (no) 355
Page pp.pp.-
#Pages 6
Date of Issue