Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset

The 1st SpeechWellness Challenge conveys the need for speech-based suicide risk assessment in adolescents. This study investigates a multimodal approach for this challenge, integrating automatic transcription with WhisperX, linguistic embeddings from Chinese RoBERTa, and audio embeddings from WavLM. Additionally, handcrafted acoustic features -- including MFCCs, spectral contrast, and pitch-related statistics -- were incorporated. We explored three fusion strategies: early concatenation, modality-specific processing, and weighted attention with mixup regularization. Results show that weighted attention provided the best generalization, achieving 69% accuracy on the development set, though a performance gap between development and test sets highlights generalization challenges. Our findings, strictly tied to the MINI-KID framework, emphasize the importance of refining embedding representations and fusion mechanisms to enhance classification reliability.

@article{marie2025_2505.13069,
  title={ Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset },
  author={ Ambre Marie and Ilias Maoudj and Guillaume Dardenne and Gwenolé Quellec },
  journal={arXiv preprint arXiv:2505.13069},
  year={ 2025 }
}