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Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

By: Silke Goronzy

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Paperback | 19 December 2002

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164 Pages

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23.39 x 15.6 x 0.89

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Audio Processing Language & Linguistics Reference, Dictionaries & Guides Medicine Medicine in General Mathematics Mathematical Foundation Mathematical Logic Operating Systems

Engineering & Technology Electronics & Communications Engineering Electronics Engineering Artificial Intelligence Natural Language & Machine Translation Other Technologies & Applied Sciences

Introduction	p. 1
ASR: An Overview	p. 7
Pre-processing of the Speech Data	p. 15
Stochastic Modelling of Speech	p. 21
Knowledge Bases of an ASR System	p. 31
Speaker Adaptation	p. 37
Confidence Measures	p. 57
Pronunciation Adaptation	p. 79
Future Work	p. 105
Summary	p. 109
Bibliography	p. 113
Index	p. 125
Glossary	p. 127
Databases and Experimental Settings	p. 131
MLLR Results	p. 135
Phoneme Inventory	p. 139
Table of Contents provided by Blackwell. All Rights Reserved.

Robust Adaptation to Non-Native Accents in Automatic Speech Recognition

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