In this paper, a new method based on the class-based histogram equalization to
compensate the acoustic mismatch between training and test conditions of speech
recognizers is proposed. The proposed method improves the speech recognition
accuracy in noisy environments by reducing two limitations of the conventional
histogram equalization: The discrepancy of phonetic class distributions between
training and test speech data, and the non-monotonic transformation caused by
the acoustic mismatch in the histogram equalization-based feature domain. The
algorithm employs multiple class-specific reference and test cumulative
distribution functions, classifies the feature vector for each frame into its
corresponding class using the k-means clustering method, and equalizes each
feature coefficient by using the corresponding class reference and test
distributions. The experiments on the Aurora 2 task proved the effectiveness of
the proposed method by reducing averaged error rates by 19% over the
conventional histogram equalization method and by 60% over the
mel-cepstral-based features, respectively.