NDLI: Modeling the intonation of discourse segments for improved online dialog ACT tagging

Content Provider	IEEE Xplore Digital Library
Author	Sridhar, V.K.R. Narayanan, S. Bangalore, S.
Copyright Year	2008
Description	Author affiliation: Speech Anal. & Interpretation Lab., Univ. of Southern California, Los Angeles, CA (Sridhar, V.K.R.; Narayanan, S.)
Abstract	Prosody is an important cue for identifying dialog acts. In this paper, we show that modeling the sequence of acoustic- prosodic values as n-gram features with a maximum entropy model for dialog act (DA) tagging can perform better than conventional approaches that use coarse representation of the prosodic contour through acoustic correlates of prosody. We also propose a discriminative framework that exploits preceding context in the form of lexical and prosodic cues from previous discourse segments. Such a scheme facilitates online DA tagging and offers robustness in the decoding process, unlike greedy decoding schemes that can potentially propagate errors. Using only lexical and prosodic cues from 3 previous utterances, we achieve a DA tagging accuracy of 72% compared to the best case scenario with accurate knowledge of previous DA tag, which results in 74% accuracy.
Starting Page	5033
Ending Page	5036
File Size	202895
Page Count	4
File Format	PDF
ISBN	9781424414833
ISSN	15206149
DOI	10.1109/ICASSP.2008.4518789
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-03-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Tagging Decoding Hidden Markov models Entropy Viterbi algorithm Robustness Context modeling Speech recognition Speech analysis Laboratories discourse context dialog act tagging prosody maximum entropy model discriminative modeling
Content Type	Text
Resource Type	Article

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