Robustness evaluation of wavelet based features for continuous speech recognition Online publication date: Sat, 03-Jul-2010
by Omar Farooq, Sekharjit Datta
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 9, No. 1, 2010
Abstract: This article evaluates robustness of admissible wavelet packet based features for continuous speech recognition. The recognition accuracy is compared with the standard mel frequency cepstral coefficients (MFCC) under clean and noisy environment. This is carried out by adding white Gaussian and speech noise to the phonemes of the TIMIT database to generate different levels of signal to noise ratio. Further, a wavelet based denoising technique is proposed as a front-end for noise reduction. Soft and hard thresholding techniques are used with one-level and two-level wavelet based denoising. The speech recogniser with Continuous Density Hidden Markov's Model is used to model the phonemes for the word recognition task. The recognition performance achieved with denoising of the input speech shows improvement as compared to without denoising of both the MFCC and the wavelet based features.
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