Speech Model Compensation Method Using Vector Taylor Series

摘要

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a model adaptation method for noise speech recognition that can be used in speech recognition in automobiles, speech recognition in mobile communication terminals, and other noise environments. A first step of inputting a noise speech feature vector so as to compensate for static and dynamic variables of the hidden Markov model in the log spectral region using the vector taylor series approximation method; A second step of predicting the mean and the variance of the noise according to the noise model prediction method shown in the statistical linear approximation method using the vector taylor series or the statistical linear approximation method in the log spectral region; A third step of converting the clean speech model of the cepstrum domain, the static parameters and the dynamic parameters into the log spectral domain; A fourth step of compensating the mean and variance for the static and dynamic models; A fifth step of converting the mean and variance of the compensated noise in the log spectral region back into the spectral region; And a sixth step of performing recognition using the mean and variance of the compensated noise, and the spectral feature vector of the noise speech.