Improving word accuracy with Gabor feature extraction

A novel type of feature extraction for automatic speech recognition is investigated. Two-dimensional Gabor functions, with varying extents and tuned to different rates and directions of spectro-temporal modulation, are applied as filters to a spectro-temporal representation provided by mel spectra. The use of these functions is motivated by findings in neurophysiology and psychoacoustics. Data-driven parameter selection was used to obtain Gabor feature sets, the performance of which is evaluated on the Aurora 2 and 3 datasets both on their own and in combination with the Qualcomm-OGI-ICSI Aurora proposal. The Gabor features consistently provide performance improvements.