Comb filter decomposition feature extraction for robust automatic speech recognition

Abstract

This thesis discusses the issues of Automatic Speech Recognition in presence of additive white noise. Comb Filter Decomposition (CFD), a new method for approximating the magnitude of the speech spectrum in terms of its harmonics is proposed. Three feature extraction methods from CFD coefficients are introduced. The performance of the method and resulting features are evaluated using simulated recognition systems with Hidden Markov Model classifiers and conditions of additive white noise under varying Signal to Noise ratios. The results are compared with the performance of the existing robust feature extraction methods. The results show that the proposed method has a good potential for Automatic Speech Recognition under noisy conditions.