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

Ambre Marie; Ilias Maoudj; Guillaume Dardenne; Gwenolé Quellec

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arXiv · 2505.13069

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

Ambre Marie·Ilias Maoudj·Guillaume Dardenne·Gwenolé Quellec

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Abstract

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.

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Ambre Marie, Ilias Maoudj, Guillaume Dardenne, Gwenolé Quellec. 2025-05-19. Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset. https://arxiv.org/abs/2505.13069

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