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
| 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.
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.
