Mike Seltzer and Alex Acero
One serious difficulty in the deployment of wideband speech
recognition systems for new tasks is the expense in both time and
cost of obtaining sufficient training data. A more economical approach
is to collect telephone speech and then restrict the application
to operate at the telephone bandwidth. However, this generally
results in suboptimal performance compared to a wideband
recognition system. In this paper, we propose a novel EM algorithm
in which wideband acoustic models are trained using a
small amount of wideband speech augmented by a larger amount
of narrowband speech. Experiments performed using wideband
speech and telephone speech demonstrate that the proposed mixedbandwidth
training algorithm results in significant improvements
in recognition accuracy over conventional training strategies when
the amount of wideband data is limited.
In Proc. of the IEEE Workshop on Automatic Speech Recognition and Understanding
Publisher Institute of Electrical and Electronics Engineers, Inc.
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