Application of noise robust MDT speech recognition on the SPEECON and SpeechDat-Car databases

We show that the recognition accuracy of an MDT recognizer which performs well on artificially noisified data, deteriorates rapidly under realistic noisy conditions (using multiple microphone recordings from the SPEECON/SpeechDat-Car databases) and is outperformed by a commercially available recognizer which was trained using a multi-condition paradigm. Analysis of the recognition results indicates that the recording channels with the lowest SNRs where the MDT recognizer fails most, are also the channels which suffer most from room reverberation. Despite the channel compensation measures we took, it appears difficult to maintain the restorative power of MDT in such non-additive noise conditions.