Detection of task-incomplete dialogs based on utterance-and-behavior tag N-gram for spoken dialog systems

Sunao Hara, Norihide Kitaoka, Kazuya Takeda

Research output: Contribution to journalConference article


We propose a method of detecting "task incomplete" dialogs in spoken dialog systems using N-gram-based dialog models. We used a database created during a field test in which inexperienced users used a client-server music retrieval system with a spoken dialog interface on their own PCs. In this study, the dialog for a music retrieval task consisted of a sequence of user and system tags that related their utterances and behaviors. The dialogs were manually classified into two classes: the dialog either completed the music retrieval task or it didn't. We then detected dialogs that did not complete the task, using N-gram probability models or a Support Vector Machine with N-gram feature vectors trained using manually classified dialogs. Offline and on-line detection experiments were conducted on a large amount of real data, and the results show that our proposed method achieved good classification performance.

Original languageEnglish
Pages (from-to)1305-1308
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - Dec 1 2011
Event12th Annual Conference of the International Speech Communication Association, INTERSPEECH 2011 - Florence, Italy
Duration: Aug 27 2011Aug 31 2011



  • Breakdowns in dialog
  • N-gram
  • Spoken dialog system
  • Task incomplete dialog detection

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modelling and Simulation

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