Presentation 2000/6/15
SP2000-14 Dictation of Multiparty Conversation Using MLLR Speaker Adaptation and Statistical Turn Taking Model
Noriyuki Murai, Tetsunori Kobayashi,
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Abstract(in English) A new speech decoder dealing with multiparty conversation is proposed. Multiparty conversation denotes a situation in which many speakers talk each other. In such a situation, the system has to recognize not only the word sequence of the input speech but also the speaker of each part of them. We propose the method utilizing not only acoustic model and language model, which are the resources of conventional single-user speech decodre, but also stochastic turn taking model and speakers individual models using MLLR speaker adaptation to recognize speech. This framework realizes simultaneous maximum likelihood estimation of spoken word sequence and the speaker sequence. Experimental results using TV sports news show that the proposed method reduce the word error rate by 29.5% and speaker error rate by 89.7% compared to the conventional method.
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Keyword(in English) multiparty conversation / stochastic turn taking model / speaker individuality / GMM / MLLR
Paper # SP2000-14
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Committee SP
Conference Date 2000/6/15(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) SP2000-14 Dictation of Multiparty Conversation Using MLLR Speaker Adaptation and Statistical Turn Taking Model
Sub Title (in English)
Keyword(1) multiparty conversation
Keyword(2) stochastic turn taking model
Keyword(3) speaker individuality
Keyword(4) GMM
Keyword(5) MLLR
1st Author's Name Noriyuki Murai
1st Author's Affiliation Dept.EECE, Waseda University()
2nd Author's Name Tetsunori Kobayashi
2nd Author's Affiliation Dept.EECE, Waseda University
Date 2000/6/15
Paper # SP2000-14
Volume (vol) vol.100
Number (no) 136
Page pp.pp.-
#Pages 8
Date of Issue