Phase autocorrelation (PAC) derived robust speech features

We introduce a new class of noise robust acoustic features derived from a new measure of autocorrelation, and explicitly exploiting the phase variation of the speech signal frame over time. This family of features, referred to as "phase autocorrelation" (PAC) features, include PAC spectrum and PAC MFCC (Mel-frequency cepstral coefficient), among others. In regular autocorrelation based features, the correlation between two signal segments (signal vectors), separated by a particular time interval k, is calculated as a dot product of these two vectors. In our proposed PAC approach, the angle between the two vectors is used as a measure of correlation. Since dot product is usually more affected by noise than the angle, PAC-features are expected to be more robust to noise. This is indeed significantly confirmed by the presented experimental results. The experiments were conducted on the Numbers 95 database, on which "stationary" (car) and "non -stationary" (factory) Noisex 92 noises were added with varying SNR. In most of the cases, without any specific tuning, PAC-MFCC features perform better.