# Clinically Inspired Symptom-Guided Depression Detection from Emotion-Aware Speech Representations

arXiv: https://arxiv.org/abs/2602.15578 · HTML: https://mlsanity.com/paper/2602.15578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 17 Feb 2026.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022)
- [Robust wav2vec 2.0: Analyzing Domain Shift in Self-Supervised Pre-Training](https://mlsanity.com/paper/2104.01027/) (2021)
- [MFCC-based Recurrent Neural Network for Automatic Clinical Depression Recognition and Assessment from Speech](https://mlsanity.com/paper/1909.07208/) (2019)
- [Self-supervised representations in speech-based depression detection](https://mlsanity.com/paper/2305.12263/) (2023)
- [Multimodal Depression Classification Using Articulatory Coordination Features And Hierarchical Attention Based Text Embeddings](https://mlsanity.com/paper/2202.06238/) (2022)
- [Self-Supervised Embeddings for Detecting Individual Symptoms of Depression](https://mlsanity.com/paper/2406.17229/) (2024)
- [Robust and Explainable Depression Identification from Speech Using Vowel-Based Ensemble Learning Approaches](https://mlsanity.com/paper/2410.18298/) (2024)

## Built on by (most cited first, 1 of 1)

- [MERID: Multimodal Exploration via Recursive Self-Improvement Agents for Major Depression Analysis](https://mlsanity.com/paper/2609.36235/) (2026)

## Abstract (arXiv)

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.