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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.22153 |
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| _version_ | 1866915516376940544 |
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| author | Li, Jialun Jiang, Weitao Cui, Ziyun Duan, Yinan Qu, Diyang Zhang, Chao Chen, Runsen Lei, Chang Wu, Wen |
| author_facet | Li, Jialun Jiang, Weitao Cui, Ziyun Duan, Yinan Qu, Diyang Zhang, Chao Chen, Runsen Lei, Chang Wu, Wen |
| contents | Suicide risk among adolescents remains a critical public health concern, and speech provides a non-invasive and scalable approach for its detection. Existing approaches, however, typically focus on one single speech assessment task at a time. This paper, for the first time, investigates cross-task approaches that unify diverse speech suicide risk assessment tasks within a single model. Specifically, we leverage a speech large language model as the backbone and incorporate a mixture of DoRA experts (MoDE) approach to capture complementary cues across diverse assessments dynamically. The proposed approach was tested on 1,223 participants across ten spontaneous speech tasks. Results demonstrate that MoDE not only achieves higher detection accuracy than both single-task specialised models and conventional joint-tuning approaches, but also provides better confidence calibration, which is especially important for medical detection tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_22153 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Cross-Task Suicide Risk Detection via Speech LLM Li, Jialun Jiang, Weitao Cui, Ziyun Duan, Yinan Qu, Diyang Zhang, Chao Chen, Runsen Lei, Chang Wu, Wen Audio and Speech Processing Suicide risk among adolescents remains a critical public health concern, and speech provides a non-invasive and scalable approach for its detection. Existing approaches, however, typically focus on one single speech assessment task at a time. This paper, for the first time, investigates cross-task approaches that unify diverse speech suicide risk assessment tasks within a single model. Specifically, we leverage a speech large language model as the backbone and incorporate a mixture of DoRA experts (MoDE) approach to capture complementary cues across diverse assessments dynamically. The proposed approach was tested on 1,223 participants across ten spontaneous speech tasks. Results demonstrate that MoDE not only achieves higher detection accuracy than both single-task specialised models and conventional joint-tuning approaches, but also provides better confidence calibration, which is especially important for medical detection tasks. |
| title | Towards Cross-Task Suicide Risk Detection via Speech LLM |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.22153 |