💻 computer science
Speech-Text: A Dual-Modal System for Mental Health Detection using Machine Learning and Deep Learning
This paper proposes a dual-modal machine learning framework that integrates textual (TF-IDF) and audio (MFCC) features to detect depression, achieving high accuracy (up to 99.38% with XGBoost on audio data) and demonstrating the potential of multimodal learning for scalable, non-invasive mental health diagnostics.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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