Overview
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 2560)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (13 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
Speech recognition technology is being increasingly employed in human-machine interfaces. A remaining problem however is the robustness of this technology to non-native accents, which still cause considerable difficulties for current systems.
In this book, methods to overcome this problem are described. A speaker adaptation algorithm that is capable of adapting to the current speaker with just a few words of speaker-specific data based on the MLLR principle is developed and combined with confidence measures that focus on phone durations as well as on acoustic features. Furthermore, a specific pronunciation modelling technique that allows the automatic derivation of non-native pronunciations without using non-native data is described and combined with the previous techniques to produce a robust adaptation to non-native accents in an automatic speech recognition system.
Editors and Affiliations
Bibliographic Information
Book Title: Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
Editors: Silke Goronzy
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/3-540-36290-8
Publisher: Springer Berlin, Heidelberg
-
eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag Berlin Heidelberg 2002
Softcover ISBN: 978-3-540-00325-0Published: 19 December 2002
eBook ISBN: 978-3-540-36290-6Published: 01 July 2003
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XI, 146
Topics: Artificial Intelligence, Signal, Image and Speech Processing, Mathematical Logic and Formal Languages, User Interfaces and Human Computer Interaction