Clinically Inspired Symptom-Guided Depression Detection from Emotion-Aware Speech Representations
arXiv:2602.15578v1 Announce Type: new Abstract: Depression manifests through a diverse set of symptoms such as sleep disturbance, loss of interest, and concentration difficulties. However, most existing works treat depression prediction either as a binary label or an overall severity score without explicitly modeling symptom-specific information. This limits their ability to provide symptom-level analysis relevant to clinical screening. To address this, we propose a symptom-specific and clinically inspired framework for depression severity estimation from speech. Our approach uses a symptom-guided cross-attention mechanism that aligns PHQ-8 questionnaire items with emotion-aware speech representations to identify which segments of a participant's speech are more important to each symptom. To account for differences in how symptoms are expressed over time, we introduce a learnable symptom-specific parameter that adaptively controls the sharpness of attention distributions. Our results
arXiv:2602.15578v1 Announce Type: new Abstract: Depression manifests through a diverse set of symptoms such as sleep disturbance, loss of interest, and concentration difficulties. However, most existing works treat depression prediction either as a binary label or an overall severity score without explicitly modeling symptom-specific information. This limits their ability to provide symptom-level analysis relevant to clinical screening. To address this, we propose a symptom-specific and clinically inspired framework for depression severity estimation from speech. Our approach uses a symptom-guided cross-attention mechanism that aligns PHQ-8 questionnaire items with emotion-aware speech representations to identify which segments of a participant's speech are more important to each symptom. To account for differences in how symptoms are expressed over time, we introduce a learnable symptom-specific parameter that adaptively controls the sharpness of attention distributions. Our results on EDAIC, a standard clinical-style dataset, demonstrate improved performance outperforming prior works. Further, analyzing the attention distributions showed that higher attention is assigned to utterances containing cues related to multiple depressive symptoms, highlighting the interpretability of our approach. These findings outline the importance of symptom-guided and emotion-aware modeling for speech-based depression screening.
Executive Summary
This study presents a symptom-specific and clinically inspired framework for depression severity estimation from speech. The proposed approach utilizes a symptom-guided cross-attention mechanism and a learnable symptom-specific parameter to adaptively control attention distributions. Experimental results on EDAIC demonstrate improved performance compared to prior works. The study's findings highlight the importance of symptom-guided and emotion-aware modeling for speech-based depression screening. The approach's interpretability is demonstrated through attention distributions, which show higher attention to utterances containing cues related to multiple depressive symptoms. This study contributes to the development of more effective and personalized depression screening tools.
Key Points
- ▸ Proposes a symptom-specific and clinically inspired framework for depression severity estimation from speech
- ▸ Utilizes a symptom-guided cross-attention mechanism and a learnable symptom-specific parameter
- ▸ Demonstrates improved performance on EDAIC compared to prior works
- ▸ Highlights the importance of symptom-guided and emotion-aware modeling for speech-based depression screening
Merits
Strength in clinical relevance
The study's approach is grounded in clinical practice and models symptom-specific information, making it more relevant to clinical screening and diagnosis.
Improved performance
The proposed framework outperforms prior works on EDAIC, demonstrating its potential for improved depression screening accuracy.
Interpretability
The study's attention distributions provide insights into the segments of speech that are most relevant to each symptom, enhancing the approach's interpretability.
Demerits
Limited generalizability
The study's results are based on a single dataset (EDAIC), and it is unclear whether the proposed framework generalizes to other datasets or populations.
Dependence on annotated data
The study's success relies on the availability of annotated data, which may not be feasible or practical in all settings.
Need for further validation
While the study demonstrates improved performance, further validation is necessary to confirm the effectiveness of the proposed framework in real-world clinical settings.
Expert Commentary
This study makes a significant contribution to the field of speech-based depression screening by proposing a symptom-specific and clinically inspired framework. The proposed approach demonstrates improved performance and interpretability, which are crucial for effective clinical screening and diagnosis. However, the study's limitations, such as limited generalizability and dependence on annotated data, need to be addressed in future research. Furthermore, the study's findings have significant implications for policymakers and clinicians, emphasizing the need for investment in speech-based mental health screening tools and the integration of AI-driven approaches into clinical practice.
Recommendations
- ✓ Recommendation 1: Future research should focus on validating the proposed framework in diverse populations and settings to enhance its generalizability.
- ✓ Recommendation 2: The study's findings highlight the need for policymakers to invest in the development of more effective speech-based mental health screening tools and to integrate AI-driven approaches into clinical practice.
