We present a new method for speech denoising and robust

speech recognition. Using the framework of probabilistic models

allows us to integrate detailed speech models and models

of realistic non-stationary noise signals in a principled manner.

The framework transforms the denoising problem into a problem

of Bayes-optimal signal estimation, producing minimum mean

square error estimators of desired features of clean speech from

noisy data. We describe a fast and efficient implementation of

an algorithm that computes these estimators. The effectiveness

of this algorithm is demonstrated in robust speech recognition

experiments, using the Wall Street Journal speech corpus and

Microsoft Whisper large-vocabulary continuous speech recognizer.

Results show significantly lower word error rates than

those under noisy-matched condition. In particular, when the

denoising algorithm is applied to the noisy training data and

subsequently the recognizer is retrained, very low error rates are

obtained.

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In  Proc. of the Eurospeech Conference

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