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.
Cite as: Kleinschmidt, M., Gelbart, D. (2002) Improving word accuracy with Gabor feature extraction. Proc. 7th International Conference on Spoken Language Processing (ICSLP 2002), 25-28, doi: 10.21437/ICSLP.2002-5
@inproceedings{kleinschmidt02_icslp,
author={Michael Kleinschmidt and David Gelbart},
title={{Improving word accuracy with Gabor feature extraction}},
year=2002,
booktitle={Proc. 7th International Conference on Spoken Language Processing (ICSLP 2002)},
pages={25--28},
doi={10.21437/ICSLP.2002-5}
}