Probabilistic class histogram equalization for robust speech recognition

Abstract

In this letter, a probabilistic class histogram equalization method is proposed to compensate for an acoustic mismatch in noise robust speech recognition. The proposed method aims not only to compensate for the acoustic mismatch between training and test environments but also to reduce the limitations of the conventional histogram equalization. It utilizes multiple class-specific reference and test cumulative distribution functions, classifies noisy test features into their corresponding classes by means of soft classification with a Gaussian mixture model, and equalizes the features by using their corresponding class-specific distributions. Experiments on the Aurora 2 task confirm the superiority of the proposed approach in acoustic feature compensation.