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This paper proposes a new channel normalization algorithm called parametric cepstral mean normalization (PCMN) to increase robust- ness of speech recognition to varying acoustic conditions. Rather than using a simple average of input speech features as channel es- timate, as done in the traditional CMN, PCMN weighs the running average of input speech frames in a frequency dependent manner. These weights are jointly optimized together with parameters of the acoustic model training. Experimental results show that, in contrast to traditional CMN, which degrades performance on clean data, PCMN provides 5% relative improvement on clean data, while also providing 11.2% relative improvement on far-field test data. We also propose an adaptive version of PCMN, called aPCMN, where both input speech features and channel estimates have weights. These weights are computed at run time and they change dynamically based on the input speech. aPCMN provides 13.0% relative im- provement on far-field test set, while still maintaining 5% relative improvement on clean data.

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