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
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
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Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces
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Product Details
| ISBN-13: | 9781461448037 |
|---|---|
| Publisher: | Springer-Verlag New York, LLC |
| Publication date: | 10/20/2012 |
| Sold by: | Barnes & Noble |
| Format: | eBook |
| Pages: | 178 |
| File size: | 1 MB |
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