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Robust Adaptation to Non-Native Accents in Automatic Speech Recognition
     

Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

by Silke Goronzy
 

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

Overview

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.

Product Details

ISBN-13:
9783540003250
Publisher:
Springer Berlin Heidelberg
Publication date:
01/11/2008
Series:
Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence Series , #2560
Edition description:
2002
Pages:
146
Product dimensions:
9.21(w) x 6.14(h) x 0.35(d)

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