用多模态语音特征评估青少年自杀风险，融合文本与音频信息提升识别准确率。
Suicide Risk Assessment Using Multimodal Speech Features: A Study on the SW1 Challenge Dataset
- 融合语音转录、语言与音频嵌入，结合手工提取的声学特征
- 加权注意力融合策略在开发集上达到69%准确率，但测试集表现有差距
- 适合关注心理健康监测与多模态机器学习的研究者
首届SpeechWellness挑战赛凸显了针对青少年进行基于语音的自杀风险评估的重要性。本研究探索了一种多模态方法，整合WhisperX自动转录、中文RoBERTa语言嵌入和WavLM音频嵌入，并引入了MFCC、谱对比度及与音高相关的统计特征等手工声学特征。比较了三种融合策略：早期拼接、模态特异性处理以及带mixup正则化的加权注意力机制。结果显示，加权注意力策略泛化能力最佳，在开发集上达到69%准确率，但开发集与测试集间存在性能差距，反映出泛化挑战。研究严格基于MINI-KID框架，强调需优化嵌入表示与融合机制以提高分类可靠性。
原文摘要 · Abstract (English)
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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