A method of generating feature vectors representing speech sounds of at least part of an input audio signal, the method comprising: sampling a plurality of predetermined time-domain windows of the input audio signal as the audio signal evolves over time; and for each of said windows: - performing a frequency-domain signal analysis on the window to extract oscillator peaks therefrom, the oscillator peaks corresponding to harmonic resonances in the speech, the extracted oscillator peaks thereby forming a spectral representation of the sample; - renormalizing the extracted oscillator peaks to eliminate variations in the fundamental frequency and time duration of a speech sound occurring in the window; - normalizing the power of the renormalized extracted oscillator peaks; and - forming the renormalized and power normalized extracted oscillator peaks into a feature vector for the window, for discriminating speech sounds in speech recognition. wherein the renormalizing comprises, for each of said windows : i) identifying a harmonically related group of the extracted oscillator peaks, each oscillator peak of the identified group being located in a frequency bin dictated by the spectral representation of the window; and ii) shifting the oscillator peaks to consecutive frequency bins beginning with bin 1, while maintaining the relative ordering of the identified oscillator peaks from the spectral representation of the window, wherein the extracted renormalized oscillator peaks for each window analyzed are distributed into slots of a comparator stack to form a common coordinate system by which to directly compare the feature vector for each window to feature vectors representing known speech sounds stored in a library for purposes of facilitating the identification of a closest match therebetween, wherein the comparator stack has n slots among which the oscillator peaks for the identified group are distributed evenly, the oscillator peak occupying a highest frequency bin number always being located in the n th slot. sampling a plurality of predetermined time-domain windows of the input audio signal as the audio signal evolves over time; and for each of said windows: - performing a frequency-domain signal analysis on the window to extract oscillator peaks therefrom, the oscillator peaks corresponding to harmonic resonances in the speech, the extracted oscillator peaks thereby forming a spectral representation of the sample; - renormalizing the extracted oscillator peaks to eliminate variations in the fundamental frequency and time duration of a speech sound occurring in the window; - normalizing the power of the renormalized extracted oscillator peaks; and - forming the renormalized and power normalized extracted oscillator peaks into a feature vector for the window, for discriminating speech sounds in speech recognition. - performing a frequency-domain signal analysis on the window to extract oscillator peaks therefrom, the oscillator peaks corresponding to harmonic resonances in the speech, the extracted oscillator peaks thereby forming a spectral representation of the sample; - renormalizing the extracted oscillator peaks to eliminate variations in the fundamental frequency and time duration of a speech sound occurring in the window; - normalizing the power of the renormalized extracted oscillator peaks; and - forming the renormalized and power normalized extracted oscillator peaks into a feature vector for the window, for discriminating speech sounds in speech recognition. i) identifying a harmonically related group of the extracted oscillator peaks, each oscillator peak of the identified group being located in a frequency bin dictated by the spectral representation of the window; and ii) shifting the oscillator peaks to consecutive frequency bins beginning with bin 1, while maintaining the relative ordering of the identified oscillator peaks from the spectral representation of the window, <EOS>