Investigations on the combination of four algorithms to increase the noise robustness of a DSR front-end for real world car data

This paper shows how the noise robustness of a MFCC feature extraction front-end can be improved by integrating four noise robustness algorithms:a spectral attenuation, a noise level normalisation, a cepstral mean normalization and a frame dropping algorithm. The algorithms were tested separately and in varying combinations on three real world car data sets with different amounts of mismatch between the training and the testing conditions. It was shown that although the algorithms partly have similar effects none of them is completely redundant. Every algorithm can contribute to a further improvement of the recognition results so the best results can be achieved by a combination of all four of them. A relative reduction of the word error rate of up to 57% is achieved.