Data-Driven Methods for Adaptive Spoken Dialogue Systems
Computational Learning for Conversational Interfaces| By: | Oliver Lemon; Olivier Pietquin |
| Publisher: | Springer Nature |
| Print ISBN: | 9781461448020 |
| eText ISBN: | 9781461448037 |
| Edition: | 1 |
| Copyright: | 2012 |
| Format: | Reflowable |
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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.