Personality Traits, Speech and Adaptive In-Vehicle Voice Output

Abstract

The interaction between human and machine is supposed to become more and more intuitive and natural. One prerequisite to achieve this is the ability of a spoken dialogue system to react flexibly to the individual requirements of a user, e.g., by means of adaptive voice output. In this context, a user's personality, which is directly reflected in his or her language style, represents a valuable framework to attribute linguistic differences in spoken language and design user-adaptive voice output. The need for such efficient, personalized communication becomes particularly safety-relevant in situations where speech-based interaction is required to be performed in parallel with a prioritized primary task, e.g., when driving a car. For this purpose, we performed data collection in a driving simulator and investigated user speech during the primary task of driving with a focus on the syntactic level. Our results revealed five syntactic complexity factors to be considered in the generation of voice output, which indicated significant differences in the spoken language of six different personality type clusters. This analysis serves as a basis for future work towards user and situation-adaptive voice output in dual-task environments.