Robust feature extraction for alphabet recognition

Abstract

Spectral/temporal segment features are adapted for isolated word recognition and tested with the entire English alphabet set using Hidden Markov Models. The ISOLET database from OGI and the HTK toolkit from Cambridge university were used to test our feature extraction technique. With our feature set we were able to achieve 97.3% recognition accuracy on test data with one pass using a whole word based recognizer. Gaussian noise was also added to evaluate robustness of the feature set. We were able to obtain recognition accuracies of 49.6% and 84.3% at SNR of -10dB and 0dB, respectively. Linear discriminant analysis was also applied to the initial feature set for a number of feature configurations and noise levels but, generally, the performance was not improved. We conclude that the initial feature computations used are both very efficient (best results obtained with 50 total features) and robust in the presence of noise.