1. A speech-recognition method that improves computer function by improving machine-recognition of a series of words in an electronic waveform derived from an utterance, which waveform is expressed as a time series of samples, each sample including data defining a frequency spectrum, comprising automatically with a computer recognizing words in the utterance by: (a) using the series of frequency spectra to divide the waveform into time segments during which the frequency spectra are stable, herein referred to as posture spectra beats, and time segments during which the frequency spectra are changing between consecutive posture spectra beats, herein referred to as changing spectra beats; (b) calculating a beat prominence for each of the beats detected in part (a), wherein beat prominence depends on beat duration and mean amplitude for the portion of the waveform having amplitude above an amplitude threshold; (c) identifying syllables within the waveform by comparing the sequence of detected beats and associated beat prominences to first prestored data indicating, for each of a predefined set of possible syllables, expected patterns of posture spectra beats and changing spectra beats, and their associated beat prominences; (d) for each identified syllable, calculating a syllable prominence from at least some beat prominences of the beats within the syllable; (e) identifying words within the waveform by comparing the sequence of identified syllables and associated syllable prominences to second prestored data indicating, for each of a predefined set of possible words, expected patterns of syllables and their associated syllable prominences; and (f) storing electronically in an output buffer, in a form suitable for use in a speech-recognition application, a series of data entries indicating words identified within the waveform derived from the utterance.
1. A speech-recognition method that improves computer function by improving machine-recognition of a series of words in an electronic waveform derived from an utterance, which waveform is expressed as a time series of samples, each sample including data defining a frequency spectrum, comprising automatically with a computer recognizing words in the utterance by: (a) using the series of frequency spectra to divide the waveform into time segments during which the frequency spectra are stable, herein referred to as posture spectra beats, and time segments during which the frequency spectra are changing between consecutive posture spectra beats, herein referred to as changing spectra beats; (b) calculating a beat prominence for each of the beats detected in part (a), wherein beat prominence depends on beat duration and mean amplitude for the portion of the waveform having amplitude above an amplitude threshold; (c) identifying syllables within the waveform by comparing the sequence of detected beats and associated beat prominences to first prestored data indicating, for each of a predefined set of possible syllables, expected patterns of posture spectra beats and changing spectra beats, and their associated beat prominences; (d) for each identified syllable, calculating a syllable prominence from at least some beat prominences of the beats within the syllable; (e) identifying words within the waveform by comparing the sequence of identified syllables and associated syllable prominences to second prestored data indicating, for each of a predefined set of possible words, expected patterns of syllables and their associated syllable prominences; and (f) storing electronically in an output buffer, in a form suitable for use in a speech-recognition application, a series of data entries indicating words identified within the waveform derived from the utterance. 7. The method of claim 1 wherein the series of frequency spectra is limited to profiled waveforms in a frequency band defined by a high bounding frequency and a low bounding frequency, and further comprising normalizing the series of frequency spectra.