Abstract
Even though there exist various techniques to improve the recognition rates of SI systems, state-of-the-art SD systems still yield higher recognition rates than SI ones. If provided with the same amount of training data, they can achieve an average word error rate a factor of two or three lower than the SI system [Woo99]. But to train SD systems, large amounts of speaker specific speech data are needed and it is often not feasible to collect this data. Hence the use of speaker adaptation methods is appealing for solving this problem, since they promise to achieve SD performance, but require only a small fraction of speaker-specific data.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
(2002). Speaker Adaptation. In: Goronzy, S. (eds) Robust Adaptation to Non-Native Accents in Automatic Speech Recognition. Lecture Notes in Computer Science(), vol 2560. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36290-8_6
Download citation
DOI: https://doi.org/10.1007/3-540-36290-8_6
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00325-0
Online ISBN: 978-3-540-36290-6
eBook Packages: Springer Book Archive