1. A method of automatic speech recognition to convert speech signal into text using one or more processors comprising: A) segmenting the speech signal into pitch-synchronous frames, wherein for voiced sections each said frame is a single pitch period; B) for each frame, equalizing the two ends of the waveform using an ends-matching program; C) generating an amplitude spectrum of each said frame using Fourier analysis; D) transforming each said amplitude spectrum into a timbre vector using Laguerre functions; E) performing acoustic decoding to find a list of most likely phonemes or sub-phoneme units for each said timbre vector by comparing with a timbre vector database; F) decoding the sequence of the list of the most likely phonemes or sub-phoneme units using a language-model database to find out the most likely text; wherein the segmenting of the speech-signal is based on an analysis of the speech signals using an asymmetric window which includes: a) conducting, for a speaker, a test to find the best size of the asymmetric window; b) convoluting the speech-signal with the said asymmetric window to form a profile function; c) picking up the maxima in the said profile function as segmentation points; d) extending the segmentation points to unvoiced sections.
1. A method of automatic speech recognition to convert speech signal into text using one or more processors comprising: A) segmenting the speech signal into pitch-synchronous frames, wherein for voiced sections each said frame is a single pitch period; B) for each frame, equalizing the two ends of the waveform using an ends-matching program; C) generating an amplitude spectrum of each said frame using Fourier analysis; D) transforming each said amplitude spectrum into a timbre vector using Laguerre functions; E) performing acoustic decoding to find a list of most likely phonemes or sub-phoneme units for each said timbre vector by comparing with a timbre vector database; F) decoding the sequence of the list of the most likely phonemes or sub-phoneme units using a language-model database to find out the most likely text; wherein the segmenting of the speech-signal is based on an analysis of the speech signals using an asymmetric window which includes: a) conducting, for a speaker, a test to find the best size of the asymmetric window; b) convoluting the speech-signal with the said asymmetric window to form a profile function; c) picking up the maxima in the said profile function as segmentation points; d) extending the segmentation points to unvoiced sections. 3. The method in claim 1 , wherein the acoustic decoding comprises distinguishing different voiced phonemes by computing a timbre distance between each said timbre vector and the timbre vectors of different voiced phonemes in the timbre vector database.