Recognition of noisy speech using cumulant-based linear prediction analysis

The use of cumulant-based LP (linear prediction) analysis for speech recognition in the presence of noise is proposed. This method assumes the speech signal to be non-Gaussian. It is shown that cepstral coefficients derived by this method are quite insensitive to additive Gaussian noise which can be white or colored. The performance of a recognizer based on these estimates is compared to the performance of one that uses LP estimates derived from the autocorrelation function. It is found that at low SNR (below about 20 dB) the cumulant-based estimates outperform the autocorrelation-based estimates. At higher SNRs the reverse is true. The reasons for this behavior are not yet understood. However, it is shown that, by combining the two estimates, one can achieve recognition accuracy that is better than that of the conventional recognizer at all SNRs