10th Annual Conference of the International Speech Communication Association

Brighton, United Kingdom
September 6-10, 2009

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

NICT, Japan

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 userís 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%.

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Bibliographic reference.  Sugiura, Komei / Iwahashi, Naoto / Kashioka, Hideki / Nakamura, Satoshi (2009): "Bayesian learning of confidence measure function for generation of utterances and motions in object manipulation dialogue task", In INTERSPEECH-2009, 2483-2486.