Bayesian learning of confidence measure function for generation of utterances and motions in object manipulation dialogue task

Komei Sugiura, Naoto Iwahashi, Hideki Kashioka, Satoshi Nakamura

Research output: Contribution to journalConference article

7 Citations (Scopus)

Abstract

This paper proposes a method that generates motions and utterances in an object manipulation dialogue task. The proposed method integrates belief modules for speech, vision, and motions into a probabilistic framework so that a user's utterances can be understood based on multimodal information. Responses to the utterances are optimized based on an integrated confidence measure function for the integrated belief modules. Bayesian logistic regression is used for the learning of the confidence measure function. The experimental results revealed that the proposed method reduced the failure rate from 12% down to 2.6% while the rejection rate was less than 24%.

Original languageEnglish
Pages (from-to)2483-2486
Number of pages4
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publication statusPublished - 2009 Nov 26
Externally publishedYes
Event10th Annual Conference of the International Speech Communication Association, INTERSPEECH 2009 - Brighton, United Kingdom
Duration: 2009 Sep 62009 Sep 10

Keywords

  • Bayesian logistic regression
  • Confidence
  • Multimodal spoken dialogue system
  • Robot language acquisition

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Sensory Systems

Fingerprint Dive into the research topics of 'Bayesian learning of confidence measure function for generation of utterances and motions in object manipulation dialogue task'. Together they form a unique fingerprint.

  • Cite this