Data-Driven Methods for Adaptive Spoken Dialogue Systems

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.

Author Biography

Oliver Lemon is a Reader and head of the Interaction Lab in the school of Mathematical and Computer Sciences at Heriot Watt University, Edinburgh. Dr. Lemon is currently serving as the Program Chair for SIGDial 2010 and as a member of the Program Committee of INLG 2010. He is also on the Editorial Board of the new journal “Dialogue & Discourse”. Prof. Pietquin and Dr. Lemon were co-chairs of the special session “Machine learning for adaptivity in spoken dialogue systems” at the InterSpeech 2009 conference, which inspired the development of this book. Olivier Pietquin is an Associate Professor at the Ecole Superieure d’Electricite (Supelec, France), where he founded and currently heads the “Information, Multimodality & Signal” (IMS) research group. He is an elected member of the IEEE Speech and Language Technical Committee. Prof. Pietquin has four patents and has been published in over 45 journal articles, edited books, and conference proceedings.