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Optimizing the turn-taking behavior of task-oriented spoken dialog systems

Optimizing the turn-taking behavior of task-oriented spoken dialog systems Optimizing the Turn-Taking Behavior of Task-Oriented Spoken Dialog Systems ANTOINE RAUX, Honda Research Institute USA MAXINE ESKENAZI, Carnegie Mellon University Even as progress in speech technologies and task and dialog modeling has allowed the development of advanced spoken dialog systems, the low-level interaction behavior of those systems often remains rigid and inef cient. Based on an analysis of human-human and human-computer turn-taking in naturally occurring task-oriented dialogs, we de ne a set of features that can be automatically extracted and show that they can be used to inform ef cient end-of-turn detection. We then frame turn-taking as decision making under uncertainty and describe the Finite-State Turn-Taking Machine (FSTTM), a decision-theoretic model that combines data-driven machine learning methods and a cost structure derived from Conversation Analysis to control the turn-taking behavior of dialog systems. Evaluation results on CMU Let ™s Go, a publicly deployed bus information system, con rm that the FSTTM signi cantly improves the responsiveness of the system compared to a standard threshold-based approach, as well as previous data-driven methods. Categories and Subject Descriptors: H.5.2 [Information Interfaces and Presentation]: User Interfaces; I.2.7 [Arti cial Intelligence]: Natural Language Processing General Terms: Experimentation, Human Factors, Algorithms Additional Key Words http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Optimizing the turn-taking behavior of task-oriented spoken dialog systems

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2012 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/2168748.2168749
Publisher site
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Abstract

Optimizing the Turn-Taking Behavior of Task-Oriented Spoken Dialog Systems ANTOINE RAUX, Honda Research Institute USA MAXINE ESKENAZI, Carnegie Mellon University Even as progress in speech technologies and task and dialog modeling has allowed the development of advanced spoken dialog systems, the low-level interaction behavior of those systems often remains rigid and inef cient. Based on an analysis of human-human and human-computer turn-taking in naturally occurring task-oriented dialogs, we de ne a set of features that can be automatically extracted and show that they can be used to inform ef cient end-of-turn detection. We then frame turn-taking as decision making under uncertainty and describe the Finite-State Turn-Taking Machine (FSTTM), a decision-theoretic model that combines data-driven machine learning methods and a cost structure derived from Conversation Analysis to control the turn-taking behavior of dialog systems. Evaluation results on CMU Let ™s Go, a publicly deployed bus information system, con rm that the FSTTM signi cantly improves the responsiveness of the system compared to a standard threshold-based approach, as well as previous data-driven methods. Categories and Subject Descriptors: H.5.2 [Information Interfaces and Presentation]: User Interfaces; I.2.7 [Arti cial Intelligence]: Natural Language Processing General Terms: Experimentation, Human Factors, Algorithms Additional Key Words

Journal

ACM Transactions on Speech and Language Processing (TSLP) – Association for Computing Machinery

Published: May 1, 2012

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