Abstract
Linear channel compensation in speech recognition typically involves estimating an additive shift in the cepstral domain. This paper explores both Bayesian and maximum likelihood techniques to transform either the features or the model parameters. Experiments on the Macrophone corpus show error rate reductions of up to 16% over cepstral mean subtraction for short utterances.
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