Deep autoencoder with gated convolutional neural networks for improving speech quality in secured communications

In this study, we introduce a speech enhancement method to improve the quality of decrypted speech signals from hand-talk devices, which are highly susceptible to security attacks. Ensuring high-quality decrypted speech is essential because traditional speech enhancement methods struggle with artifacts only present during speech due to the encryption process applied selectively. This situation limits the effectiveness of traditional methods, which assume distortion is constant and can be estimated during silent periods. Our solution involves a deep-learning approach that employs a gated convolutional neural network (GCNN). Unlike typical convolutional neural networks (CNNs) that excel in processing spatial data but falter with temporal changes, our GCNN integrates a gating mechanism to enhance handling of temporal dynamics in speech data. This method directly maps distorted speech to its clean counterpart, bypassing the need for explicit noise estimation. Our experiments indicate that this deep-learning method significantly outperforms traditional speech enhancement techniques and conventional CNNs in several key evaluation metrics, offering a promising advancement in decrypted speech quality enhancement.