This book addresses the problems of spoken dialogue system design and especially automatic learning of optimal strategies for man-machine dialogues. Besides the description of the learning methods, this text proposes a framework for realistic simulation of human-machine dialogues based on probabilistic techniques, which allows automatic evaluation and unsupervised learning of dialogue strategies. This framework relies on stochastic modelling of modules composing spoken dialogue systems as well as on user modelling. Special care has been taken to build models that can either be hand-tuned or learned from generic data.
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.
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