Information density and overlap in spoken dialogue

Dethlefs, Nina; Hastie, Helen; Cuayáhuitl, Heriberto; Yu, Yanchao; Rieser, Verena; Lemon, Oliver

Abstract

Incremental dialogue systems are often perceived as more responsive and natural because they are able to address phenomena of turn-taking and overlapping speech, such as backchannels or barge-ins. Previous work in this area has often identified distinctive prosodic features, or features relating to syntactic or semantic completeness, as marking appropriate places of turn-taking. In a separate strand of work, psycholinguistic studies have established a connection between information density and prominence in language—the less expected a linguistic unit is in a particular context, the more likely it is to be linguistically marked. This has been observed across linguistic levels, including the prosodic, which plays an important role in predicting overlapping speech.

In this article, we explore the hypothesis that information density (ID) also plays a role in turn-taking. Specifically, we aim to show that humans are sensitive to the peaks and troughs of information density in speech, and that overlapping speech at ID troughs is perceived as more acceptable than overlaps at ID peaks. To test our hypothesis, we collect human ratings for three models of generating overlapping speech based on features of: (1) prosody and semantic or syntactic completeness, (2) information density, and (3) both types of information. Results show that over 50% of users preferred the version using both types of features, followed by a preference for information density features alone. This indicates a clear human sensitivity to the effects of information density in spoken language and provides a strong motivation to adopt this metric for the design, development and evaluation of turn-taking modules in spoken and incremental dialogue systems.

• Journal Article Type: Article
• Acceptance Date: Nov 2, 2015
• Online Publication Date: Nov 10, 2015
• Publication Date: 2016-05
• Deposit Date: Jun 27, 2023
• Journal: Computer speech & language
• Print ISSN: 0885-2308
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 37
• Pages: 82-97
• DOI: https://doi.org/10.1016/j.csl.2015.11.001