Data-Driven Methods For Adaptive Spoken Dialogue Systems: Computational Learning For Conversational Interfaces

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 ?nd-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: Oliver Lemon, Olivier Pietquin • Publisher: Springer • Publication Date: Nov 09, 2014 • Number of Pages: 188 pages • Language: English • Binding: Paperback • ISBN-10: 1489992839 • ISBN-13: 9781489992833