Patent ID: 6292776
Filing Date: 2001-09-18
Classification: G10L

Abstract:
A training method for a speech recognizer comprising the steps of:receiving a band limited voice input utterance that is time varying;transforming said utterance using a fast fourier transform process to a frequency domain spectrum;forwarding said frequency domain spectrum to a plurality of mel filter banks, at least one of said plurality of mel filter banks having a plurality of sub-bands filtering said frequency spectrum;transforming an output of each of said plurality of mel-filter banks using an inverse discrete fourier transform process to obtain a processed speech output that is time varying from each of said mel-filter banks and an additional time varying output for each sub-band above one for each mel-filter bank;analyzing each output of each of time varying outputs of each inverse discrete fourier transform process using a respective linear prediction cepstral analysis to produce an individual feature vector output corresponding to each inverse discrete fourier transform output;appending said individual feature vectors forming a grand feature vector;conditioning said grand feature vector and removing any bias from said grand feature vector using a bias remover;performing MSE/GPD training on said grand feature vector after the bias is removed;building HMMs from said MSE/GPD training; andextracting a bias removal codebook of size four from the mean vectors of said HMMs for use with said bias removal in said signal conditioning of the grand feature vector.