What decisions have you made: Automatic decision detection in conversational speech

Pei-Yun Hsueh, Johanna D. Moore

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study addresses the problem of automatically detecting decisions in conversational speech. We formulate the problem as classifying decision-making units at two levels of granularity: dialogue acts and topic segments. We conduct an empirical analysis to determine the characteristic features of decision-making dialogue acts, and trainMaxEnt models using these features for the classification tasks. We find that models that combine lexical, prosodic, contextual and topical features yield the best results on both tasks, achieving 72% and 86% precision, respectively. The study also provides a quantitative analysis of the relative importance of the feature types.
Original languageEnglish
Title of host publicationProceedings of the Annual conference of the North American Chapter of the Association for Computational Linguistics 2007 (NAACL-HLT)
PublisherAssociation for Computational Linguistics
Pages25-32
Number of pages8
Publication statusPublished - Apr 2007

Fingerprint

Dive into the research topics of 'What decisions have you made: Automatic decision detection in conversational speech'. Together they form a unique fingerprint.

Cite this