- 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
- 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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