1. In a voice recognition system, a method comprising the steps of: sampling a voice signal; applying a Fourier transform to a plurality of frame intervals of the sampled voice signal to generate spectral data having a spectral envelope for each of the plurality of frame intervals; subdividing the spectral data for each of the plurality of frame intervals into a plurality of bands; filtering out the spectral envelope from the spectral data of each of the plurality of frame intervals to generate filtered spectral data for each of the plurality of frame intervals; applying a Fourier transform to the filtered spectral data for each of the plurality of bands to generate an autocorrelation function for each of the plurality of bands; measuring a value of the magnitude of the autocorrelation function for each of the plurality of bands, whereby the value is a measure of a degree of voiceness for each of the plurality of bands; applying the degree of voiceness for each of the plurality of bands to a corresponding plurality of phoneme models; and deriving a textual equivalent of speech from the voice signal by searching through a phoneme library according to predictions made by the corresponding plurality of phoneme models.
1. In a voice recognition system, a method comprising the steps of: sampling a voice signal; applying a Fourier transform to a plurality of frame intervals of the sampled voice signal to generate spectral data having a spectral envelope for each of the plurality of frame intervals; subdividing the spectral data for each of the plurality of frame intervals into a plurality of bands; filtering out the spectral envelope from the spectral data of each of the plurality of frame intervals to generate filtered spectral data for each of the plurality of frame intervals; applying a Fourier transform to the filtered spectral data for each of the plurality of bands to generate an autocorrelation function for each of the plurality of bands; measuring a value of the magnitude of the autocorrelation function for each of the plurality of bands, whereby the value is a measure of a degree of voiceness for each of the plurality of bands; applying the degree of voiceness for each of the plurality of bands to a corresponding plurality of phoneme models; and deriving a textual equivalent of speech from the voice signal by searching through a phoneme library according to predictions made by the corresponding plurality of phoneme models. 3. The method as recited in claim 1, wherein the value of the magnitude of the autocorrelation function is a peak magnitude.