We describe two methods that aim at normalizing acoustic vectors at the filterbank level such that the test data distribution matches the training data distribution. They enhance the histogram normalization technique proposed earlier by taking care of the variable silence fraction for each speaker, and by rotating the feature space. We report a number of recognition tests under minor (different microphones in training and test, telephone data) and major (office vs. car recordings) mismatch conditions. Both methods give superior performance to the basic histogram normalization approach. The overall improvements in word error rate (WER) range between 6% and 85% relative.

In this work, the RWTH automatic speech recognition systems for English and German for the second Quaero evaluation campaign 2009 are presented. The systems are designed to transcribe web data, European parliament plenary sessions and broadcast news data. Another challenge in the 2009 evaluation is that almost no in-domain training data is provided and the test data contains a large variety of speech types. The RWTH participates for the English and German languages with the best results for German and competitive results for the English. Contributing to the enhancements are the systematic use of hierarchical neural network based posterior features, system combination, speaker adaptation, cross speaker adaptation, domain dependent modeling and the usage of additional training data.