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Speech recognition in the F-16 cockpit using principal spectral components

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2 Author(s)
Rajasekaran, P. ; Texas Instruments Inc., Dallas, Texas, USA ; Doddington, G.R.

A modification of the usual LPC speaker-dependent speech recognition algorithms yielded significantly improved recognition performance in an F-16 fighter cockpit environment.The LPC model is first transformed into spectral amplitudes using asimulated filter bank. Statistically optimum linear transformation of the filter bank amplitudes to "principal spectral components" (PSC) provides a set of uncorrelated features. These features are rank ordered and the least significant features are discarded. The data base used for experiments consisted of 5 male speakers uttering a 70-word vocabulary ten times for training in 85 dBA noise level, and 3 times for test in each of 97, 106 and 112 dBA noise levels. The PSC method yielded about half the number of substitutions of the standard LPC method.

Published in:

Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '85.  (Volume:10 )

Date of Conference:

Apr 1985