Advancements in Speech-Based Emotion Recognition and PTSD Detection through Machine and Deep Learning Techniques: A Comprehensive Survey

This comprehensive survey delves into the intersection of Machine Learning (ML) and Deep Learning (DL) with speech analysis, showcasing significant strides in detecting and diagnosing Post-Traumatic Stress Disorder (PTSD) through speech-based emotion recognition. By leveraging advanced computational techniques, researchers can identify nuanced speech patterns indicative of PTSD, offering a non-invasive, objective, and scalable diagnostic tool. Despite promising advancements, challenges such as data variability, ethical concerns, and the need for generalizable models persist. The survey highlights the importance of interdisciplinary collaboration, ethical diligence, and the integration of multimodal data to enhance diagnostic accuracy and patient care. Looking forward, it points to a future where speech analysis could revolutionize mental health diagnostics, making it more accessible, personalized, and stigma-free. This work serves as a seminal reference in the field, urging continued innovation and research to fully harness the potential of ML and DL in transforming mental health diagnostics and treatment for PTSD.