Voice Tone Emotion Recognition for the Depressed: A review

Abstract

This study investigates the application of voice tone emotion recognition for individuals with depression, utilizing advanced machine learning techniques. The research focuses on analyzing voice tone to detect emotional states, providing a non-invasive method to monitor and potentially diagnose depression. Audio data from participants were preprocessed through noise reduction and normalization, followed by feature extraction, including pitch, tone, and frequency. The extracted features were then analyzed using a combination of traditional machine learning and deep learning models. Our model achieved an accuracy of 85% in emotion recognition, with high precision and recall for neutral and happy tones. The findings highlight the model's potential in enhancing mental health monitoring and interventions. By enabling continuous, remote assessment of emotional states, this technology can aid in early detection, personalized therapy, and timely interventions for depression. The study also addresses existing challenges, such as variability in speech patterns and ethical considerations, proposing future research directions to improve accuracy and applicability. This research underscores the transformative impact of emotion recognition technology in mental health, offering promising applications in telehealth and personalized care for individuals with depression. Keywords: Voice tone, emotion recognition, depression, mental health, machine learning.