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{
"item_title" : "Data-Driven Methods for Adaptive Spoken Dialogue Systems",
"item_author" : [" Oliver Lemon", "Olivier Pietquin "],
"item_description" : "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 for Adaptive Spoken Dialogue Systems : Computational Learning for Conversational Interfaces
by Oliver Lemon and Olivier Pietquin
Other Available Formats
Overview
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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Details
- ISBN-13: 9781489992833
- ISBN-10: 1489992839
- Publisher: Springer
- Publish Date: November 2014
- Dimensions: 9.21 x 6.14 x 0.4 inches
- Shipping Weight: 0.6 pounds
- Page Count: 178
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