Data-Driven Methods for Adaptive Spoken Dialogue Systems - Computational Learning for Conversational Interfaces (Paperback, 2012 ed.)

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.

General

Imprint:	Springer-Verlag New York
Country of origin:	United States
Release date:	November 2014
First published:	2012
Editors:	Oliver Lemon • Olivier Pietquin
Dimensions:	235 x 155 x 10mm (L x W x T)
Format:	Paperback
Pages:	178
Edition:	2012 ed.
ISBN-13:	978-1-4899-9283-3
Categories:	Books > Language & Literature > Language & linguistics > Computational linguistics Books > Computing & IT > General theory of computing > Data structures Books > Computing & IT > Computer programming > Algorithms & procedures Books > Computing & IT > Applications of computing > Computer modelling & simulation Books > Computing & IT > Social & legal aspects of computing > Human-computer interaction Books > Professional & Technical > Electronics & communications engineering > Electronics engineering > Applied optics > General
LSN:	1-4899-9283-9
Barcode:	9781489992833

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