Revolutionizing PTSD Detection and Emotion Recognition through Novel Speech-Based Machine and Deep Learning Algorithms

Abstract

Early and accurate detection of Post-Traumatic Stress Disorder (PTSD) is crucial for effective treatment. However, traditional methods relying on self-reporting and clinical interviews are subjective and time-consuming. This paper proposes a novel approach for revolutionizing PTSD detection and emotion recognition through advanced speech analysis. Speech, rich with emotional cues, offers valuable insights into an individual's mental state. Existing methods often struggle to capture the subtle emotional variations associated with PTSD. We address this by introducing a speech-based framework that utilizes machines and deep learning algorithms. This framework extracts and interprets emotional signatures present in speech patterns using novel feature extraction techniques and deep learning architectures specifically designed for PTSD-related cues. The proposed approach is evaluated on a comprehensive speech dataset, with performance measured by established metrics like accuracy, precision, and recall. This research demonstrates that the synergistic combination of machine and deep learning algorithms with speech analysis significantly improves objective and efficient PTSD detection. This approach has the potential to be applied in clinical settings and other situations where early identification of PTSD is crucial.