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Data-Driven Methods for Adaptive Spoken Dialogue Systems

Computational Learning for Conversational Interfaces

  • Book
  • © 2012

Overview

Editors:
  1. Oliver Lemon

    1. , Mathematics and Computer Science, Heriot Watt University, Edinburgh, United Kingdom

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  2. Olivier Pietquin

    1. , Metz Campus - IMS Research Group, SUPELEC, Metz, France

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    You can also search for this editor in PubMed   Google Scholar

  • One of the first books to specifically address adaptive techniques used in dialogue system development
  • Practical examples developed by the editors and colleagues will be included
  • The book will be based on dialogue systems freely available for academic use

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Conversational Interfaces

    • Oliver Lemon
    Pages 1-4

  3. Developing Dialogue Managers from Limited Amounts of Data

    • Verena Rieser, Oliver Lemon
    Pages 5-17

  4. Data-Driven Methods for Spoken Language Understanding

    • James Henderson, Filip Jurčíček
    Pages 19-38

  5. User Simulation in the Development of Statistical Spoken Dialogue Systems

    • Simon Keizer, Stéphane Rossignol, Senthilkumar Chandramohan, Olivier Pietquin
    Pages 39-73

  6. Optimisation for POMDP-Based Spoken Dialogue Systems

    • Milica Gašić, Filip Jurčíček, Blaise Thomson, Steve Young
    Pages 75-101

  7. Statistical Approaches to Adaptive Natural Language Generation

    • Oliver Lemon, Srini Janarthanam, Verena Rieser
    Pages 103-130

  8. Metrics and Evaluation of Spoken Dialogue Systems

    • Helen Hastie
    Pages 131-150

  9. Data-Driven Methods in Industrial Spoken Dialog Systems

    • Roberto Pieraccini, David Suendermann
    Pages 151-171

  10. Conclusion and Future Research Directions

    • Olivier Pietquin
    Pages 173-177
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Keywords

About this book

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.

Editors and Affiliations

  • , Mathematics and Computer Science, Heriot Watt University, Edinburgh, United Kingdom

    Oliver Lemon

  • , Metz Campus - IMS Research Group, SUPELEC, Metz, France

    Olivier Pietquin

About the editors

Oliver Lemon is a Reader and head of the Interaction Lab in the school of Mathematical and Computer Sciences at Heriot Watt University, Edinburgh. Dr. Lemon is currently serving as the Program Chair for SIGDial 2010 and as a member of the Program Committee of INLG 2010. He is also on the Editorial Board of the new journal "Dialogue & Discourse". Prof. Pietquin and Dr. Lemon were co-chairs of the special session "Machine learning for adaptivity in spoken dialogue systems" at the InterSpeech 2009 conference, which inspired the development of this book.

Olivier Pietquin is an Associate Professor at the Ecole Superieure d'Electricite (Supelec, France), where he founded and currently heads the "Information, Multimodality & Signal" (IMS) research group. He is an elected member of the IEEE Speech and Language Technical Committee. Prof. Pietquin has four patents and has been published in over 45 journal articles, edited books, and conference proceedings.

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