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Introduction to special issue on machine learning for adaptivity in spoken dialogue systems

Introduction to special issue on machine learning for adaptivity in spoken dialogue systems Introduction to Special Issue on Machine Learning for Adaptivity in Spoken Dialogue Systems OLIVER LEMON, Heriot-Watt University OLIVIER PIETQUIN, Sup lec/UMI 2958 (GeorgiaTech - CNRS) e ACM Reference Format: Lemon, O. and Pietquin, O. 2011. Introduction to special issue on machine learning for adaptivity in spoken dialogue systems. ACM Trans. Speech Lang. Process. 7, 3, Article 3 (May 2011), 3 pages. DOI = 10.1145/1966407.1966408 http://doi.acm.org/10.1145/1966407.1966408 œI know I ™ve made some very poor decisions recently, but I can give you my complete assurance that my work will be back to normal. I ™ve still got the greatest enthusiasm and con dence in the mission. And I want to help you.  (HAL-9000 in 2001 A Space Odyssey) In the 1960s, AI researchers were predicting that machines capable of spoken dialogue behavior, somewhat like the preceding example, would be possible within a few decades. Fifty years later, we are still confronted with very dif cult problems in AI, but we have powerful new tools with which to address the spoken dialogue problem. The research landscape in spoken dialogue systems has undergone signi cant changes over the past decade. This transformation has been the result of new momentum and fresh http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Speech and Language Processing (TSLP) Association for Computing Machinery

Introduction to special issue on machine learning for adaptivity in spoken dialogue systems

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISSN
1550-4875
DOI
10.1145/1966407.1966408
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Abstract

Introduction to Special Issue on Machine Learning for Adaptivity in Spoken Dialogue Systems OLIVER LEMON, Heriot-Watt University OLIVIER PIETQUIN, Sup lec/UMI 2958 (GeorgiaTech - CNRS) e ACM Reference Format: Lemon, O. and Pietquin, O. 2011. Introduction to special issue on machine learning for adaptivity in spoken dialogue systems. ACM Trans. Speech Lang. Process. 7, 3, Article 3 (May 2011), 3 pages. DOI = 10.1145/1966407.1966408 http://doi.acm.org/10.1145/1966407.1966408 œI know I ™ve made some very poor decisions recently, but I can give you my complete assurance that my work will be back to normal. I ™ve still got the greatest enthusiasm and con dence in the mission. And I want to help you.  (HAL-9000 in 2001 A Space Odyssey) In the 1960s, AI researchers were predicting that machines capable of spoken dialogue behavior, somewhat like the preceding example, would be possible within a few decades. Fifty years later, we are still confronted with very dif cult problems in AI, but we have powerful new tools with which to address the spoken dialogue problem. The research landscape in spoken dialogue systems has undergone signi cant changes over the past decade. This transformation has been the result of new momentum and fresh

Journal

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

Published: May 1, 2011

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