Speaker adaptation and adaptive training for jointly optimised tandem systems
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Publication Date
2018Journal Title
Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Conference Name
Interspeech 2018
ISSN
2308-457X
ISBN
978-1-5108-7221-9
Publisher
ISCA
Volume
2018-September
Pages
872-876
Type
Conference Object
Metadata
Show full item recordCitation
Wang, Y., Zhang, C., Gales, M., & Woodland, P. (2018). Speaker adaptation and adaptive training for jointly optimised tandem systems. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, 2018-September 872-876. https://doi.org/10.21437/Interspeech.2018-2432
Abstract
Speaker independent (SI) Tandem systems trained by joint optimisation
of bottleneck (BN) deep neural networks (DNNs) and
Gaussian mixture models (GMMs) have been found to produce
similar word error rates (WERs) to Hybrid DNN systems. A
key advantage of using GMMs is that existing speaker adaptation
methods, such as maximum likelihood linear regression
(MLLR), can be used which to account for diverse speaker
variations and improve system robustness. This paper investigates
speaker adaptation and adaptive training (SAT) schemes
for jointly optimised Tandem systems. Adaptation techniques
investigated include constrained MLLR (CMLLR) transforms
based on BN features for SAT as well as MLLR and parameterised
sigmoid functions for unsupervised test-time adaptation.
Experiments using English multi-genre broadcast (MGB3) data
show that CMLLR SAT yields a 4% relative WER reduction
over jointly trained Tandem and Hybrid SI systems, and further
reductions in WER are obtained by system combination.
Identifiers
External DOI: https://doi.org/10.21437/Interspeech.2018-2432
This record's URL: https://www.repository.cam.ac.uk/handle/1810/286180
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