**5. Conclusion**

In this chapter, we will detail a new speech enhancement technique based on Lifting Wavelet Transform (*LWT*) and Artifitial Neural Network (*ANN*). This technique also uses the *MMSE* Estimate of Spectral Amplitude. It consists at the first step in applying the *LWT* to the noisy speech signal in order to obtain two noisy details coefficients, *cD*<sup>1</sup> and *cD*<sup>2</sup> and one approximation coefficient, *cA*2. After that, *cD*<sup>1</sup> and *cD*<sup>2</sup> are denoised by soft thresholding and for their thresholding, we need to use suitable thresholds, *thrj*, 1≤*j*≤2. Those thresholds, *thrj*, 1≤*j* ≤2, are determined by using an Artificial Neural Network (*ANN*). The soft thresholding of those coefficients, *cD*<sup>1</sup> and *cD*2, is performed in order to obtain two denoised coefficients, *cDd*<sup>1</sup> and *cDd*2. Then the denoising technique based on *MMSE* Estimate of Spectral Amplitude is applied to the noisy approximation *cA*<sup>2</sup> in order to obtain a denoised coefficient, *cAd*2. Finally, the enhanced speech signal is obtained from the application of the inverse of *LWT*, *LWT*�<sup>1</sup> to *cDd*1, *cDd*<sup>2</sup> and *cAd*2. The performance of the proposed speech enhancement technique is justified by the computations of the Signal to Noise Ratio (*SNR*), Segmental *SNR* (*SSNR*) and Perceptual Evaluation of Speech Quality (*PESQ*).

*Deep Learning Applications*
