Robust speech recognition using adaptive noise threshold estimation and wavelet shrinkage

We propose an improved noise reduction method for robust speech recognition based on a perceptually statistical wavelet filtering algorithm. Perceptual noise thresholds are estimated from the universal thresholds for each critical wavelet subband. Fast changes of background noise are tracked adaptively by improving our statistical percentile filtering method. Smoothed wavelet shrinkage is applied to enhance noisy wavelet coefficients. Performance of the proposed denoising algorithm is evaluated in terms of recognition performance under adverse noisy conditions such as car and factory environments. Furthermore, it is compared to recent speech enhancement methods embedded in different state-of-the-art speech recognizers. Overall results indicate that almost similar recognition performance is obtained on the AURORA3 SPEECHDAT-Car corpus as compared to the HTK recognizer using the advanced front-end while there is an improvement when testing with the Loquendo recognizer on the SNOW-Factory corpus.