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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...
In this paper an enhanced noise reduction for robust speech recognition is implemented by means of a perceptual wavelet filtering algorithm. The psychoacoustic model is applied to map the universal thresholds to the perceptually universal thresholds for each critical wavelet subband. By improving our quantile filtering method, the change of noise level is tracked more adaptively. The denoising algorithm...
In this paper we address the application of a denoising algorithm based on wavelet packet decomposition and quantile noise estimation to noise suppression for automatic speech recognition. The denoising algorithm is adapted to suit the different requirements in machine recognition, as compared to human perception, and is tested in combination with state-of-the-art speech recognition systems. The results...
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