Abstract

Noise robustness has long been an active area of research that captures significant interest from speech recognition researchers and developers. In this chapter, with a focus on the problem of uncertainty handling in robust speech recognition, we use the Bayesian framework as a common thread for connecting, analyzing, and categorizing a number of popular approaches to the solutions pursued in the recent past. The topics covered in this chapter include 1) Bayesian decision rules with unreliable features and unreliable model parameters; 2) principled ways of computing feature uncertainty using structured speech distortion models; 3) use of a phase factor in an advanced speech distortion model for feature compensation; 4) a novel perspective on model compensation as a special implementation of the general Bayesian predictive classification rule capitalizing on model parameter uncertainty; 5) taxonomy of noise compensation techniques using two distinct axes, feature vs. model domain and structured vs. unstructured transformation; and 6) noise-adaptive training as a hybrid feature-model compensation framework and its various forms of extension.

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