Robust speech features based on LPC using weighted arcsin transform
To increase the discriminating ability of the speech feature based on linear predictive coding (LPC) and increase its noise robustness, an SNR-dependent arcsin transform is applied to the autocorrelation sequence (ACS) of each analysis frame in a speech signal. Moreover, each component in the ACS is also weighted by the normalized reciprocal of the average magnitude difference function (AMDF) for emphasizing its peak structure. Experimental results for the task of Mandarin digit recognition indicate that the LPC speech feature employing the proposed scheme is more robust than some widely used LPC-based approaches over a wide range of SNR values.
linear predictive coding snr-dependent arcsin transform autocorrelation sequence average magnitude difference function