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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.
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '85. (Volume:10 )
Date of Conference: Apr 1985