1. A method comprising: characterizing, by a computing device, a speech input based on a dynamic noise adaptation (DNA) model reflecting effects of background noise; characterizing the speech input based on a null noise DNA model reflecting a null noise mismatch condition; performing Bayesian model averaging using a first weighting of the DNA model and a second weighting of the null noise DNA model; re-weighting the DNA model and the null noise DNA model by adjusting the first weighting to increase a probability of the DNA model predicted by the Bayesian model averaging when the DNA model is more likely than the null noise DNA model to best characterize the speech input; and performing recognition of the speech input using the re-weighted DNA model and the re-weighted null noise DNA model.
1. A method comprising: characterizing, by a computing device, a speech input based on a dynamic noise adaptation (DNA) model reflecting effects of background noise; characterizing the speech input based on a null noise DNA model reflecting a null noise mismatch condition; performing Bayesian model averaging using a first weighting of the DNA model and a second weighting of the null noise DNA model; re-weighting the DNA model and the null noise DNA model by adjusting the first weighting to increase a probability of the DNA model predicted by the Bayesian model averaging when the DNA model is more likely than the null noise DNA model to best characterize the speech input; and performing recognition of the speech input using the re-weighted DNA model and the re-weighted null noise DNA model. 18. The method of claim 1 , wherein the DNA model includes a speech model, a noise model, a channel model, and an interaction model that describes how the speech model, the noise model, and the channel model combine to generate the speech input.