We have proposed earlier a noise adaptive speech recognition approach for recognizing speech corrupted by nonstationary noise and channel distortion. In this paper, we extend this approach. Instead of maximum likelihood estimation of environment parameters (as done in our previous work), the present method estimates environment parameters within the Bayesian framework that is capable of incorporating prior knowledge of the environment. Experiments are conducted on a database that contains digit utterances contaminated by channel distortion and nonstationary noise. Results show that this method performs better than the previous methods.