Speech Enhancement: Traditional and Deep Learning Techniques

Speech enhancement techniques, including traditional methods and deep learning approaches, are essential in enhancing the quality of speech signals impaired by noise. Noise is separated from speech significantly with the help of neural network architectures, such as DNNs, RNNs, and GANs. Metrics like SNR and PESQ are used to evaluate speech enhancement systems. While traditional methods like spectral subtraction and Wiener filtering are still relevant, deep learning offers more advanced alternatives. This paper overviews speech enhancement techniques, highlighting deep learning architectures such as TCNN, DCCRN, and SEGAN. Overall, deep understanding shows promise in reducing noise and improving speech quality, holding potential for various applications.