DBN Based Joint Dialogue Act Recognition of Multiparty Meetings

Alfred Dielmann, Steve Renals

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

Abstract

Joint dialogue act segmentation and classification of the new AMI meeting corpus has been performed through an integrated framework based on a switching dynamic Bayesian network and a set of continuous features and language models. The recognition process is based on a dictionary of 15 DA classes tailored for group decision-making. Experimental results show that a novel interpolated factored language model results in a low error rate on the automatic segmentation task, and thus good recognition results can be achieved on AMI multiparty conversational speech
Original languageEnglish
Title of host publication2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages133-136
Number of pages4
Volume4
ISBN (Electronic)1-4244-0728-1
ISBN (Print)1-4244-0727-3
DOIs
Publication statusPublished - 2007
Event2007 IEEE International Conference on Acoustics, Speech and Signal Processing - Hawaii Convention Center, Honolulu, Hawaii, United States
Duration: 15 Apr 200720 Apr 2007

Conference

Conference2007 IEEE International Conference on Acoustics, Speech and Signal Processing
CountryUnited States
CityHonolulu, Hawaii
Period15/04/0720/04/07

Keywords

  • DA
  • DBN
  • Interpolated FLM
  • AMI

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