Wavelet-based denoising for robust feature extraction for speech recognition

A new pre-processing stage based on wavelet denoising is proposed to extract robust features in the presence of additive white Gaussian noise. Recognition performance is compared with the commonly used Mel frequency cepstral coefficients with and without this preprocessing stage. The word recognition accuracy is found to improve using the proposed technique by 2 to 28% for signal-to-noise ratio in the range of 20 to 0 dB.