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Data-Driven Methods for Adaptive Spoken Dialogue Systems

Computational Learning for Conversational Interfaces

By Oliver Lemon , Olivier Pietquin

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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.

Full Description

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  • ISBN13: 978-1-4614-4802-0
  • 188 Pages
  • User Level: Science
  • Publication Date: October 20, 2012
  • Available eBook Formats: PDF
Full 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.
Table of Contents

Table of Contents

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

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    1. Distributed User Interfaces: Usability and Collaboration


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