Speech enhancement using modified wiener filter based MMSE and speech presence probability estimation

In the present-day communications speech signals get contaminated due to various sorts of noises that degrade the speech quality and adversely impacts speech recognition performance. To overcome these issues, a novel approach for speech enhancement using Modified Wiener filtering is developed and power spectrum computation is applied for degraded signal to obtain the noise characteristics from a noisy spectrum. In next phase, MMSE technique is applied where Gaussian distribution of each signal i.e. original and noisy signal is analyzed. The Gaussian distribution provides spectrum estimation and spectral coefficient parameters which can be used for probabilistic model formulation. Moreover, a-priori-SNR computation is also incorporated for coefficient updation and noise presence estimation which operates similar to the conventional VAD. However, conventional VAD scheme is based on the hard threshold which is not capable to derive satisfactory performance and a soft-decision based threshold is developed for improving the performance of speech enhancement. An extensive simulation study is carried out using MATLAB simulation tool on NOIZEUS speech database and a comparative study is presented where proposed approach is proved better in comparison with existing technique