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.
Figures & tables
Fig. 1 : Proposed approach inspired from PHQ-8 based clinial practice
Fig. 2 : Block diagram of the overall framework
Method
RMSE
MAE
CCC
Zhang et al. [ 19 ]
6.78
5.77
–
Fan et al. [ 17 ]
5.91
4.39
0.43
Yin et al. [ 20 ]
5.50
–
0.44
Zhao et al. [ 21 ]
5.78
5.13
–
Makiuchi et al. [ 22 ]
6.11
–
0.40
Han et al. [ 23 ]
6.29
5.38
–
TABLE I : Comparison with prior works on EDAIC
Symptom
RMSE
MAE
No Interest
0.891
0.638
Depressed
0.728
0.571
Sleep
1.104
0.903
Tired
1.005
0.842
Appetite
1.008
0.800
Failure
0.903
0.734
TABLE II : Sypmtom-wise Performance Metrics
τ setting
RMSE
MAE
CCC
No τ
5.36
4.22
0.55
Learnable τ (global)
5.31
4.17
0.54
Learnable τ ( per symptom)
5.15
4.13
0.52
TABLE III : Performance comparison with different τ settings
Fig. 3 : Visualization of attention heatmap and text alignment of the sentences marked with ’x’
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China · University of Chinese Academy of Sciences · Key Laboratory of Biomedical Imaging Science and System, Chinese Academy of Sciences, China