Title :
Robust speech recognition using adaptively denoised wavelet coefficients
Author :
Akyol, Emrah ; Erzin, Engin ; Tekalp, A. Murat
Author_Institution :
Koc Univ., Istanbul, Turkey
Abstract :
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.
Keywords :
acoustic noise; adaptive signal processing; hidden Markov models; random noise; signal denoising; speech processing; speech recognition; wavelet transforms; HMM; MFCC feature vectors; adaptive denoising; best basis; denoised wavelet coefficients; hard thresholding; isolated digits; robust speech recognition; speech processing; speech recognition; wavelet transform; Additive noise; Frequency; Noise reduction; Noise robustness; Signal processing; Spatial databases; Speech processing; Speech recognition; Wavelet coefficients; Wavelet transforms;
Conference_Titel :
Signal Processing and Communications Applications Conference, 2004. Proceedings of the IEEE 12th
Print_ISBN :
0-7803-8318-4
DOI :
10.1109/SIU.2004.1338549