Probabilistic Class Histogram Equalization Based on Posterior Mean Estimation for Robust Speech Recognition

Abstract

In this letter, we propose a new probabilistic class histogram equalization technique for noise robust speech recognition. To cope with the sparse data problem which is common in the case of short test data, the proposed histogram equalization technique employs the posterior mean estimator, a kind of the Bayesian estimator, for test CDF. Experiments on the Aurora-4 framework showed that the proposed method produces performance improvement over the conventional maximum likelihood estimation-based approach.