The effectiveness of histogram equalization on environmental model adaptation

In this paper, we introduce a new histogram equalization-based environmental model adaptation method for robust speech recognition in noise environments. The proposed method adapts initially-trained acoustic mean models of a speech recognizer into the environmentally matched models. The covariance models are adapted by using utterance-level local covariance matrices. We performed a series of experiments based on the Aurora2 framework to examine the effectiveness of the proposed environmental model adaptation technique. In both clean and multi-condition trainings, the proposed approach achieved substantial performance improvements over the baseline speech recognizers.