Feature space normalization in adverse acoustic conditions

We study the effect of different feature space normalization techniques in adverse acoustic conditions. Recognition tests are reported for cepstral mean and variance normalization, histogram normalization, feature space rotation, and vocal tract length normalization on a German isolated word recognition task with large acoustic mismatch. The training data was recorded in a clean office environment and the test data in cars. Speech recognition failed completely without normalization on the highway dataset, whereas the word error rate could be reduced to 17% using an online setup and to 10% with an offline setup.