Robust speech recognition using adaptively denoised wavelet coefficients

The existence of additive noise affects the performance of speech recognition in real environments. We propose a new set of feature vectors for robust speech recognition using denoised wavelet coefficients. The use of wavelet coefficients in speech processing is motivated by the ability of the wavelet transform to capture both time and frequency information and the non-stationary behaviour of speech signals. We use one set of noisy data, such as data with car noise, and we use hard thresholding in the best basis for denoising. We use isolated digits as our database in our HMM based speech recognition system. A performance comparison of hard thresholding denoised wavelet coefficients and MFCC feature vectors is presented.