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data-driven methods for adaptive spoken dialogue systems: computational learning for conversational interfaces
Oliver Lemon
(Illustrated by)
·
Olivier Pietquin
(Illustrated by)
·
Springer
· Hardcover
data-driven methods for adaptive spoken dialogue systems: computational learning for conversational interfaces - Lemon, Oliver ; Pietquin, Olivier
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Synopsis "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 "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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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
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