Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces -
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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 t ... Full description
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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.
More Information
| Publisher | Springer US |
|---|---|
| Release year | 2012 |
| Cover type | Hardcover |
| EAN | 9781461448020 |