Interaction style detection based on Fused Cross-Correlation Model in spoken conversation

In recent years, much attention has been given to dialogue strategy design to achieve intelligent speech-based human-computer interaction. Since speakers generally express their intents in different Interaction Styles (ISs), the responses of a spoken dialogue system should be versatile instead of invariable and planned. This paper presents an approach to automatic detection of a user's IS using a Fused Cross-Correlation Model (FCCM). As IS generally involves high level psychological meaning, cross-correlation among various psychological factors including emotion, personality trait, and IS is thus considered for IS detection modeling. The Bayes' theorem is then used to integrate the cross-correlation into the IS detector for enhancing the IS detection accuracy. Experiments show a promising result of the proposed approach.