Data-Driven Methods for Adaptive Spoken Dialogue Systems

Computational Learning for Conversational Interfaces

Nonfiction, Computers, Advanced Computing, Programming, User Interfaces, Reference & Language, Language Arts, Linguistics, General Computing
Cover of the book Data-Driven Methods for Adaptive Spoken Dialogue Systems by , Springer New York
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Author: ISBN: 9781461448037
Publisher: Springer New York Publication: October 20, 2012
Imprint: Springer Language: English
Author:
ISBN: 9781461448037
Publisher: Springer New York
Publication: October 20, 2012
Imprint: Springer
Language: English

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.

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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.

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