Towards Personalization of Spoken Dialogue System Communication Strategies
Abstract
This study examines the effects of 3 conversational traits - Register, Explicitness, and Misunderstandings - on user satisfaction and the perception of specific subjective features for Virtual Home Assistant spoken dialogue systems. Eight different system profiles were created, each representing a different combination of these 3 traits. We then utilized a novel Wizard of Oz data collection tool and recruited participants who interacted with the 8 different system profiles, and then rated the systems on 7 subjective features. Surprisingly, we found that systems which made errors were preferred overall, with the statistical analysis revealing error-prone systems were rated higher than systems which made no errors for all 7 of the subjective features rated. There were also some interesting interaction effects between the 3 conversational traits, such as implicit confirmations being preferred for systems employing a "conversational" Register, while explicit confirmations were preferred for systems employing a "formal" Register, even though there was no overall main effect for Explicitness. This experimental framework offers a fine-grained approach to the evaluation of user satisfaction which looks towards the personalization of communication strategies for spoken dialogue systems.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 01, 2020
- Accession Number
- AD1154311
Entities
People
- Carla Gordon
- David R Traum
- Killirroi Georgila
- Volodymyr Yanov
Organizations
- University of Southern California