Training Wideband Acoustic Models using Mixed-Bandwidth Training Data via Feature Bandwidth Extension

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 sub-optimal performance. In this paper, we propose a

new algorithm for training wideband acoustic models that requires

only a small amount of wideband speech augmented by a larger

amount of narrowband speech. The algorithm operates by first

converting the narrowband features to wideband features through

a process called Feature Bandwidth Extension. The bandwidthextended

features are then combined with available wideband data

to train the acoustic models using a modified version of the conventional

forward-backward algorithm. Experiments performed

using wideband speech and telephone speech demonstrate that the

proposed mixed-bandwidth training algorithm results in significant

improvements in recognition accuracy over conventional training

strategies when the amount of wideband data is limited.

2005-mseltzer-icassp.pdf
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In  Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing

Publisher  Institute of Electrical and Electronics Engineers, Inc.
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