Affect and Personality Aided Modeling of Transcribed Speech for Depression Severity Estimation

Automatic detection of depression has gained significant attention due to its potential for early diagnosis and intervention. We propose a novel method that seamlessly integrates emotion, sentiment, and personality features as distinct yet interconnected components within a unified transformer-based architecture for depression severity estimation. Our key contribution lies in a joint cross-attention technique, which adeptly fuses the information gleaned from these different text representations, allowing for the nuanced interplay between them to be effectively captured. This technique not only enables the model to comprehend intricate interdependencies but also enhances the model's ability to discern subtle contextual cues within the textual data. We undertake a comprehensive experimental environment to meticulously evaluate the discrete components comprising the architecture. The resultant findings gleaned from these experiments substantiate the self-contained efficacy of the envisioned architecture. Finally, we compare our method with state-of-the-art studies utilizing different combinations of audial, visual, and textual modalities. The final results demonstrate that our method achieves promising results in automatic depression severity estimation. This study underscores the potential of text-driven analysis in mental health assessment, opening avenues for more effective, accessible, and non-intrusive depression severity estimation tools.