Quantile based histogram equalization for noise robust large vocabulary speech recognition

The noise robustness of automatic speech recognition systems can be improved by reducing an eventual mismatch between the training and test data distributions during feature extraction. Based on the quantiles of these distributions the parameters of transformation functions can be reliably estimated with small amounts of data. This paper will give a detailed review of quantile equalization applied to the Mel scaled filter bank, including considerations about the application in online systems and improvements through a second transformation step that combines neighboring filter channels. The recognition tests have shown that previous experimental observations on small vocabulary recognition tasks can be confirmed on the larger vocabulary Aurora 4 noisy Wall Street Journal database. The word error rate could be reduced from 45.7% to 25.5% (clean training) and from 19.5% to 17.0% (multicondition training).