<![CDATA[Mental health issues such as depression, anxiety, and stress continue to increase globally and are recognized as critical factors that influence social functioning, productivity, and overall quality of life. Conventional mental health services are often limited by barriers including high cost, geographical distance, and persistent stigma that discourage individuals from seeking timely help. The digital era provides an alternative through the integration of technology into mental health counseling, offering greater accessibility, flexibility, and anonymity. Nevertheless, a key limitation of many digital counseling platforms lies in their inability to fully capture and respond to the emotional nuances of users during interactions. This study aims to address that gap by developing a speech-based emotion detection framework designed to be integrated into digital counseling environments. The proposed methodology includes the collection and preprocessing of speech samples, feature extraction using acoustic parameters, and training machine learning models to classify emotions in real time. Experimental results demonstrate that this approach significantly improves the accuracy of emotion detection, enabling digital counseling systems to provide more adaptive and personalized support. Beyond counseling, the research highlights the broader applicability of speech emotion recognition in education, telemedicine, and interactive digital assistants, all of which benefit from improved sensitivity to human emotions. These findings underscore the potential of artificial intelligence to strengthen digital mental health interventions, ensuring services that are not only more efficient and inclusive but also capable of fostering long-term emotional well-being in diverse populations.]]>
Copyrights © 2025
