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Sixth International Conference on Spoken Language Processing (ICSLP 2000)
Beijing, China
October 16-20, 2000 |
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Acoustic Modelling Using Modular/Ensemble Combinations of Heterogeneous Neural Networks
Christos A. Antoniou, T. Jeff Reynolds
Department of Computer Science,
University of Essex,
Colchester, UK
We have been investigating for some time the use of
modular/ensemble neural networks to model phones, a
commonly chosen acoustic unit for speech. We have
demonstrated the advantage of using separately trained
MLPs to estimate each phone's probability, posterior on a
sequence of feature vectors representing the expression of
the phone over some window in time. In this paper we show
how MLPs trained on different feature vectors, derived from
different pre-processing techniques, may be combined to
produce better estimates of phone posteriors and hence
lower word error rates. We also show how calculated
broad-class posterior probabilities may be used to provide
contextual information to train further nets. The combination
of these techniques results in significant improvements for
phone classification and word error rates on the TIMIT
corpus.
Full Paper
Bibliographic reference.
Antoniou, Christos A. / Reynolds, T. Jeff (2000):
"Acoustic modelling using modular/ensemble combinations of heterogeneous neural networks",
In ICSLP-2000, vol.1, 282-285.