The field of speech processing is witnessing significant advancements with the integration of deep learning techniques and innovative data augmentation strategies. Researchers are exploring new ways to improve speech emotion recognition, lexical stress analysis, and voice pathology detection. The use of hybrid models, such as CNN-LSTM frameworks, and techniques like Layerwise Relevance Propagation, are enabling more accurate and robust speech processing systems. Additionally, the application of machine learning in healthcare is leading to the development of noninvasive diagnostic tools for voice disorders and COVID-19 detection. Noteworthy papers include: EmoAugNet, which achieves high accuracy in speech emotion recognition using a hybrid CNN-LSTM framework and data augmentation. The work on lexical stress analysis, which reveals the ability of deep learning models to acquire distributed cues to stress from naturally occurring data. The research on voice pathology detection using phonation data and machine learning, which offers a noninvasive diagnostic tool for early detection of voice pathologies.