Managing out-of-grammar utterances by topic estimation with domain extensibility in multi-domain spoken dialogue systems

Kazunori Komatani, Satoshi Ikeda, Tetsuya Ogata, Hiroshi G. Okuno

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Spoken dialogue systems must inevitably deal with out-of-grammar utterances. We address this problem in multi-domain spoken dialogue systems, which deal with more tasks than a single-domain system. We defined a topic by augmenting a domain about which users want to find more information, and we developed a method of recovering out-of-grammar utterances based on topic estimation, i.e., by providing a help message in the estimated domain. Moreover, domain extensibility, that is, the ability to add new domains to the system, should be inherently retained in multi-domain systems. To estimate domains without sacrificing extensibility, we collected documents from the Web as training data. Since the data contained a certain amount of noise, we used latent semantic mapping (LSM), which enables robust topic estimation by removing the effects of noise from the data. Experimental results showed that our method improved topic estimation accuracy by 23.2 points for data including out-of-grammar utterances.

Original languageEnglish
Pages (from-to)863-870
Number of pages8
JournalSpeech Communication
Volume50
Issue number10
DOIs
Publication statusPublished - 2008 Oct 1
Externally publishedYes

Keywords

  • Domain extensibility
  • Multi-domain spoken dialogue system
  • Out-of-grammar utterance
  • Topic estimation

ASJC Scopus subject areas

  • Software
  • Modelling and Simulation
  • Communication
  • Language and Linguistics
  • Linguistics and Language
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

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