NDLI: Role play dialogue topic model for language model adaptation in multi-party conversation speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Masumura, R. Oba, T. Masataki, H. Yoshioka, O. Takahashi, S.
Copyright Year	2014
Description	Author affiliation: NTT Media Intell. Labs., NTT Corp., Tokyo, Japan (Masumura, R.; Oba, T.; Masataki, H.; Yoshioka, O.; Takahashi, S.)
Abstract	This paper introduces an unsupervised language model adaptation technique for multi-party conversation speech recognition. The use of topic models provides one of the most accurate frameworks for unsupervised language model adaptation since they can inject long-range topic information into language models. However, conventional topic models are not suitable for multi-party conversation because they assume that each speech set has each different topic. In a multi-party conversation, each speaker will share the same conversation topic and each speaker utterance will depend on both topic and speaker role. Accordingly, this paper proposes new concept of the “role play dialogue topic model” to utilize multiparty conversation attributes. The proposed topic model can share the topic distribution among each speaker and can also consider both topic and speaker role. The proposed topic model based adaptation realizes a new framework that sets multiple recognition hypotheses for each speaker and simultaneously adapts a language model for each speaker role. We use a call center dialogue data set in speech recognition experiments to show the effectiveness of the proposed method.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	4873
Ending Page	4877
File Size	131279
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854528
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Speech recognition Speech Hidden Markov models Indexes Probability Semantics topic model Unsupervised language model adaptation multi-party conversation speech recognition
Content Type	Text
Resource Type	Article

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