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

Abstract

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.