A pre and post processing technique is proposed to enhance the speech signal of highly non-stationary noisy speech.  The purpose of this research has been to build on current speech enhancement algorithms to produce an improved algorithm for enhancement of speech contaminated with non-stationary babble type noise. The pre processing involves two stages. In stage one, the variance of the noisy speech spectrum is reduced by utilizing the Discrete or Prolate Spheroidal Sequence (DPSS) multi-taper algorithm plus a Controlled Forward Moving Average (CFMA) technique. We introduced the CFMA algorithm to smooth and reduce variance of the estimated non-stationary noise spectrum. In the second stage the noisy speech power spectrum is de-noised by applying Stein’s Unbiased Risk Estimator (SURE) wavelet thresholding technique. In the third layer, use is made of a noise estimation algorithm with rapid adaptation for a highly non-stationary noise environment.  The noise estimate is updated in three frequency sub-bands, by…

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