Combining neighboring filter channels to improve quantile based histogram equalization

A mismatch between the training data and the test condition of an automatic speech recognition system usually deteriorates the recognition performance. Quantile based histogram equalization can increase the system´s robustness by approximating the cumulative density function of the current signal and then reducing an eventual mismatch based on this estimate. In a first step each output of the mel scaled filter bank can be transformed independent from the others. This paper describes an improved version of the algorithm that combines neighboring filter channels. On several databases recorded in real car environment the recognition error rates could be significantly reduced with this new approach.