A dialogue behavior control model for expressing a characters of humanoid robots

Abstract

This paper addresses character expression for humanoid robots that play a given social role such as a lab guide or a counselor via spoken dialogue so that the character matches to the social role. While most conventional methods of character expression aim to change the style of utterance texts, this study focuses on dialogue features that may affect the impression of spoken dialogue. Specifically, we use five features: Utterance amount, backchannel frequency, backchannel variety, filler frequency, and switching pause length. We adopt three character traits of extroversion, emotional instability, and politeness for a character expression, and investigate the relationship with the dialogue features. A statistical analysis of subjective evaluations shows that the dialogue features except for the backchannel variety are related to either of the traits. By using the subjective evaluation scores on the relevant traits, we can train models to control the dialogue features and behaviors according to the desired character. An experimental evaluation demonstrates the feasibility of character expression with regard to the traits of extroversion and politeness.