Enhancement of single channel speech quality and intelligibility in multiple noise conditions using wiener filter and deep CNN.

Nowadays, deep neural network has become the prime approach for enhancing speech signals as it yields good results compared to the traditional methods. This paper describes the transformation in the enhanced speech signal by applying the deep convolutional neural network (Deep CNN), which can model nonlinear relationships and compare it with the Wiener filtering method, which is the best technique for speech enhancement among the traditional methods. Denoising is performed in the frequency domain and converted back to the time domain to analyze performance metrics such as speech quality and speech intelligibility. The speech quality is analyzed based on the signal to noise ratio (SNR) and perceptual evaluation of speech quality (PESQ). Speech intelligibility is analyzed by short-time objective intelligibility (STOI). Both the methods evaluated the denoised speech, and the analysis made on the results shows that the SNR of the conventional Wiener filtering method is much improved when compared with Deep CNN. However, the PESQ and STOI of Deep CNN-based enhanced speech outperform the Wiener filtering method. The performance metrics indicate that Deep CNN achieves better results than the conventional technique. [ABSTRACT FROM AUTHOR]