Pole-filtered cepstral mean subtraction

The paper introduces a new methodology to remove the residual effects of speech from the cepstral mean used for channel normalization. The approach is based on filtering the eigenmodes of speech that are more susceptible to convolutional distortions caused by transmission channels. The filtering of linear prediction (LP) poles and their corresponding eigenmodes for a speech segment are investigated when there is a channel mismatch for speaker identification systems. An algorithm based on pole-filtering has been developed to improve the commonly employed cepstral mean subtraction. Experiments are presented in speaker identification using speech in the TIMIT database and on the San Diego portion of the KING database. The new technique is shown to offer improved recognition accuracy under cross channel scenarios when compared to conventional methods