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Data-Driven Methods for Adaptive Spoken Dialogue Systems

Computational Learning for Conversational Interfaces

Data-Driven Methods for Adaptive Spoken Dialogue Systems by Oliver Lemon
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Data driven methods have long been used in Automatic Speech Recognition (ASR) and Text-To-Speech (TTS) synthesis and have more recently been introduced for dialogue management, spoken language understanding, and Natural Language Generation. Machine learning is now present “end-to-end” in Spoken Dialogue Systems (SDS). However, these techniques require data collection and annotation campaigns, which can be time-consuming and expensive, as well as dataset expansion by simulation. In this book, we provide an overview of the current state of the field and of recent advances, with a specific focus on adaptivity.
Springer New York; October 2012
183 pages; ISBN 9781461448037
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Title: Data-Driven Methods for Adaptive Spoken Dialogue Systems
Author: Oliver Lemon; Olivier Pietquin
 
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