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Data-Driven Methods for Adaptive Spoken Dialogue Systems

Data-Driven Methods for Adaptive Spoken Dialogue Systems
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Computational Learning for Conversational Interfaces
Oliver Lemon
Springer New York
 eBook
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Artikel-Nr:
9781461448037
Veröffentl:
2012
Einband:
eBook
Seiten:
178
Autor:
Oliver Lemon
eBook Typ:
PDF
eBook Format:
Reflowable eBook
Kopierschutz:
Digital Watermark [Social-DRM]
Sprache:
Englisch
Systemvoraussetzungen
Beschreibung:

Based on dialogue systems freely available for academic use, this book covers state-of-the-art research in data-driven, machine-learning approaches to developing spoken conversational interfaces, collating the data from the EU’s groundbreaking CLASSIC project.

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.

Chapter 1. Conversational Interfaces.- Chapter 2. Developing Dialogue Managers from Limited Amounts of Data.- Chapter 3. Data-Driven Methods for Spoken Language Understanding.- Chapter 4. User Simulation in the Development of Statistical Spoken Dialogue Systems.- Chapter 5. Optimisation for POMDP-based Spoken Dialogue Systems.- Chapter 6. Statistical Approaches to Adaptive Natural Language Generation.- Chapter 7. Metrics and Evaluation of Spoken Dialogue Systems.- Chapter 8. Data-Driven Methods in Industrial Spoken Dialog Systems.- Chapter 9. Future Research Directions.

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