Impact of ASR N-Best Information on Bayesian Dialogue Act Recognition

Heriberto Cuayáhuitl, Nina Dethlefs, Helen Hastie, Oliver Lemon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

A challenge in dialogue act recognition is the mapping from noisy user inputs to dialogue acts. In this paper we describe an approach for re-ranking dialogue act hypotheses based on Bayesian classifiers that incorporate dialogue history and Automatic Speech Recognition (ASR) N-best information. We report results based on the Let’s Go dialogue corpora that show (1) that including ASR N-best information results in improved dialogue act recognition performance (+7% accuracy), and (2) that competitive results can be obtained from as early as the first system dialogue act, reducing the need to wait for subsequent system dialogue acts.
Original languageEnglish
Title of host publicationProceedings of the SIGDIAL 2013 Conference
PublisherAssociation for Computational Linguistics
Pages314-318
Number of pages5
ISBN (Print)9781937284954
Publication statusPublished - 22 Aug 2013
Event14th Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Metz, France
Duration: 22 Aug 201324 Aug 2013
Conference number: 14

Conference

Conference14th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Abbreviated titleSIGDIAL 2013
Country/TerritoryFrance
CityMetz
Period22/08/1324/08/13

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