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.
| Leidėjas: | Springer New York |
| Išleidimo metai: | 2014 |
| Knygos puslapių skaičius: | 188 |
| ISBN-10: | 1489992839 |
| ISBN-13: | 9781489992833 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Computational and corpus linguistics |
Parašykite atsiliepimą apie „Data-Driven Methods for Adaptive Spoken Dialogue Systems: Computational Learning for Conversational Interfaces“
