Detecting topic-oriented speaker stance in conversational speech

Catherine Lai, Beatrice Alex, Johanna D. Moore, Leimin Tian, Tatsuro Hori, Gianpiero Francesca

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Being able to detect topics and speaker stances in conversations is a key requirement for developing spoken language understanding systems that are personalized and adaptive. In this work, we explore how topic-oriented speaker stance is expressed in conversational speech. To do this, we present a new set of topic and stance annotations of the CallHome corpus of spontaneous dialogues. Specifically, we focus on six stances-positivity, certainty, surprise, amusement, interest, and comfort-which are useful for characterizing important aspects of a conversation, such as whether a conversation is going well or not. Based on this, we investigate the use of neural network models for automatically detecting speaker stance from speech in multi-turn, multi-speaker contexts. In particular, we examine how performance changes depending on how input feature representations are constructed and how this is related to dialogue structure. Our experiments show that incorporating both lexical and acoustic features is beneficial for stance detection. However, we observe variation in whether using hierarchical models for encoding lexical and acoustic information improves performance, suggesting that some aspects of speaker stance are expressed more locally than others. Overall, our findings highlight the importance of modelling interaction dynamics and non-lexical content for stance detection.

Original languageEnglish
Title of host publicationINTERSPEECH 2019
EditorsGernot Kubin, Thomas Hain, Bjorn Schuller, Dina El Zarka, Petra Hodl
Place of PublicationSA Australia
PublisherInternational Speech Communication Association
Pages46-50
Number of pages5
DOIs
Publication statusPublished - 2019
EventINTERSPEECH Conference 2019 - Graz, Austria
Duration: 15 Sep 201919 Sep 2019
Conference number: 20th
https://www.isca-speech.org/archive/Interspeech_2019/index.html

Conference

ConferenceINTERSPEECH Conference 2019
Abbreviated titleINTERSPEECH 2019
CountryAustria
CityGraz
Period15/09/1919/09/19
Internet address

Keywords

  • Affective computing
  • Computational paralinguistics
  • Spoken dialogue
  • Spoken language understanding
  • Stance

Cite this

Lai, C., Alex, B., Moore, J. D., Tian, L., Hori, T., & Francesca, G. (2019). Detecting topic-oriented speaker stance in conversational speech. In G. Kubin, T. Hain, B. Schuller, D. El Zarka, & P. Hodl (Eds.), INTERSPEECH 2019 (pp. 46-50). International Speech Communication Association. https://doi.org/10.21437/Interspeech.2019-2632