Anxiety disorders impact millions globally, yet traditional diagnosis relies on clinical interviews, while machine learning models struggle with overfitting due to limited data. Large-scale data collection remains costly and time-consuming, restricting accessibility. To address this, we introduce the Hyperbolic Curvature Few-Shot Learning Network (HCFSLN), a novel Few-Shot Learning (FSL) framework for multimodal anxiety detection, integrating speech, physiological signals, and video data. HCFSLN enhances feature separability through hyperbolic embeddings, cross-modal attention, and an adaptive gating network, enabling robust classification with minimal data. We collected a multimodal anxiety dataset from 108 participants and benchmarked HCFSLN against six FSL baselines, achieving 88% accuracy, outperforming the best baseline by 14%. These results highlight the effectiveness of hyperbolic space for modeling anxiety-related speech patterns and demonstrate FSL's potential for anxiety classification.

翻译：焦虑症在全球范围内影响数百万人，然而传统诊断依赖于临床访谈，而机器学习模型因数据有限而面临过拟合问题。大规模数据采集仍然成本高昂且耗时，限制了可及性。为解决这一问题，我们提出了双曲曲率小样本学习网络（HCFSLN），这是一种用于多模态焦虑检测的新型小样本学习框架，整合了语音、生理信号和视频数据。HCFSLN通过双曲嵌入、跨模态注意力和自适应门控网络增强特征可分性，从而在数据量极少的情况下实现鲁棒分类。我们收集了来自108名参与者的多模态焦虑数据集，并将HCFSLN与六种小样本学习基线方法进行基准测试，取得了88%的准确率，优于最佳基线方法14%。这些结果突显了双曲空间在建模焦虑相关语音模式方面的有效性，并证明了小样本学习在焦虑分类中的潜力。