Augmented Cepstral Normalization for Robust Speech Recognition

Alex Acero and Xuedong Huang

Abstract

We proposed an augmented cepstral mean normalization algorithm that differentiates noise and speech during normalization, and computes a different mean for each. The new procedure reduced the error rate slightly for the case of sameenvironment testing, and significantly reduced the error rate by 25% when an environmental mismatch exists over the case of standard cepstral mean normalization.

Details

Publication typeInproceedings
Published inProc. of the IEEE Workshop on Automatic Speech Recognition
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