Optimization and evaluation of Gabor feature sets for ASR
Bernd T. Meyer, Birger Kollmeier · 2008
In order to enhance automatic speech recognition performance in adverse conditions, Gabor features motivated by physiolog-ical measurements in the primary auditory cortex were opti-mized and evaluated. In the Aurora 2 experimental setup such localized, spectro-temporal filters combined with a Tandem sys-tem yield robust performance with a feature set size of 30. Im-proved 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 rela-tive WER reduction of 15 % compared to a competitive baseline is achieved by combining the feature types, indicating the po-tential of this class of physiologically motivated features. Index Terms: spectro temporal features, automatic speech recognition, Gabor features 1.