Robust speech analysis by lag-weighted linear prediction

This study introduces an approach for linear predictive spectrum analysis based on emphasizing selected time-domain properties in the analyzed signal in combination with a stabilization operation. A stable weighted linear predictive method based on a novel autocorrelation-based weighting scheme is described and its spectral properties are demonstrated. The robustness of the proposed method is compared with conventional techniques in terms of an Euclidean MFCC distortion measure in different additive noise conditions. In the experimental evaluation, the novel speech analysis technique outperforms the other evaluated methods.