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

Oliver Lemon (Curatore)

Olivier Pietquin (Curatore)

Springer-Verlag New York Inc., 2012

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.

• Curatore: Oliver Lemon, Olivier Pietquin
• Editore: Springer-Verlag New York Inc.
• Anno: 2012
• Rilegatura: Hardback
• Pagine: 178 p.
• Dimensioni: 235 x 155 mm
• EAN: 9781461448020

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