An EM Algorithm for Training Wideband Acoustic Models from Mixed-Bandwidth Training Data

Mike Seltzer and Alex Acero

Abstract

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.

Details

Publication typeInproceedings
Published inProc. of the IEEE Workshop on Automatic Speech Recognition and Understanding
PublisherInstitute of Electrical and Electronics Engineers, Inc.
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