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

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%. Index Terms: multimodal spoken dialogue system, robot language acquisition, confidence, Bayesian logistic regression