Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces (2012)
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- Synopsis
- 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.
- Copyright:
- 2012
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9781461448037
- Related ISBNs:
- 9781461448020
- Publisher:
- Springer New York
- Date of Addition:
- 12/31/20
- Copyrighted By:
- Springer New York, New York, NY
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Technology, Language Arts
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.
- Edited by:
- Oliver Lemon
- Edited by:
- Olivier Pietquin
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- by Oliver Lemon and Olivier Pietquin
- in Nonfiction
- in Computers and Internet
- in Technology
- in Language Arts