25 October 1994 Channel normalization using pole-filtered cepstral mean subtraction
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Abstract
In this paper, we introduce a new methodology called Pole Filtering to remove the residual effects of speech from the cepstral mean channel estimate, for extracting features robust to transmission channel degradations. The approach is based on filtering the eigenmodes of speech that are more susceptible to convolutional distortions caused by transmission channels. Poles and their corresponding eigenmodes for a frame of speech are investigated when there is a channel mismatch for speaker identification systems. Linear Predictive (LP) cepstra of speech has been found to be a useful feature set for recognition systems. The relation between the LP cepstral coefficients and eigenmodes of speech has been exploited to develop a robust feature set. In this paper an algorithm based on Pole-filtering has been developed to improve the cepstral features for channel normalization. Experiments are presented in speaker identification using speech in the TIMIT database processed through a telephone channel simulator and on the San Diego portion of the KING database. The technique is shown to offer improved recognition accuracy under cross channel scenarios when compared to conventional methods.
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Devang K. Naik, Devang K. Naik, Richard J. Mammone, Richard J. Mammone, } "Channel normalization using pole-filtered cepstral mean subtraction", Proc. SPIE 2277, Automatic Systems for the Identification and Inspection of Humans, (25 October 1994); doi: 10.1117/12.191872; https://doi.org/10.1117/12.191872
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