In order to enhance automatic speech recognition performance in adverse conditions, Gabor features motivated by physiological measurements in the primary auditory cortex were optimized and evaluated. In the Aurora 2 experimental setup such localized, spectro-temporal filters combined with a Tandem system yield robust performance with a feature set size of 30. Improved results can be obtained when using a Hanning window instead of a cut-off Gaussian envelope due to better modulation frequency characteristics. An analysis of complementarity of Gabor and MFCC features shows that errors could be reduced by 55% with a perfect classifier. In a real world scenario, a relative WER reduction of 15% compared to a competitive baseline is achieved by combining the feature types, indicating the potential of this class of physiologically motivated features.
Bibliographic reference. Meyer, Bernd T. / Kollmeier, Birger (2008): "Optimization and evaluation of Gabor feature sets for ASR", In INTERSPEECH-2008, 906-909.