Barge-in effects in Bayesian dialogue act recognition and simulation

Heriberto Cuayahuitl, Nina Dethlefs, Helen Hastie, Oliver Lemon

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

4 Citations (Scopus)

Abstract

Dialogue act recognition and simulation are traditionally considered separate processes. Here, we argue that both can be fruitfully treated as interleaved processes within the same probabilistic model, leading to a synchronous improvement of performance in both. To demonstrate this, we train multiple Bayes Nets that predict the timing and content of the next user utterance. A specific focus is on providing support for barge-ins. We describe experiments using the Let's Go data that show an improvement in classification accuracy (+5%) in Bayesian dialogue act recognition involving barge-ins using partial context compared to using full context. Our results also indicate that simulated dialogues with user barge-in are more realistic than simulations without barge-in events.

Original languageEnglish
Title of host publication2013 IEEE Workshop on Automatic Speech Recognition and Understanding
Place of PublicationNew York
PublisherIEEE
Pages102-107
Number of pages6
Publication statusPublished - 2013
Event2013 IEEE Workshop on Automatic Speech Recognition and Understanding - Olomouc, United Kingdom
Duration: 8 Dec 201313 Dec 2013

Conference

Conference2013 IEEE Workshop on Automatic Speech Recognition and Understanding
Abbreviated titleASRU 2013
CountryUnited Kingdom
CityOlomouc
Period8/12/1313/12/13

Keywords

  • spoken dialogue systems
  • dialogue act recognition
  • dialogue simulation
  • Bayesian nets
  • barge-in
  • USER SIMULATION

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