Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866910968458510336 |
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| author | Marie, Ambre Maoudj, Ilias Dardenne, Guillaume Quellec, Gwenolé |
| author_facet | Marie, Ambre Maoudj, Ilias Dardenne, Guillaume Quellec, Gwenolé |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_13069 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset Marie, Ambre Maoudj, Ilias Dardenne, Guillaume Quellec, Gwenolé Computation and Language Machine Learning Sound Audio and Speech Processing I.2.7; I.5.1 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. |
| title | Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset |
| topic | Computation and Language Machine Learning Sound Audio and Speech Processing I.2.7; I.5.1 |
| url | https://arxiv.org/abs/2505.13069 |