The short-time modified coherence representation and its application for noisy speech recognition

A technique for robust spectral representation of all-pole sequences is proposed. It is shown that the autocorrelation of an all-pole sequence, obtained by passing white noise through an all-pole filter 1/A(z), is an all-pole sequence of the form 1/ A²(z). The short-time modified coherence (SCM) representation, proposed here, is an all-pole modeling of the autocorrelation sequence followed by a spectral shaper. The spectral shaper, essentially a square root operator in the frequency domain, compensates for the inherent spectral distortion introduced by the autocorrelation operation on the autocorrelation sequence of the signal. The properties of the SMC representation, especially its robustness to additive white noise, are analyzed. Initial implementation of the SMC in a speaker-dependent isolated-word recognizer shows a considerable improvement over the standard linear predictive coding (LPC) representation. The SMC recognizer achieved an improvement in recognition accuracy equivalent to an increase in input SNR of approximately 13 dB, as compared to the LPC recognizer