评估主流语音识别模型在老年患者临床对话中的表现
Out of the Box, into the Clinic? Evaluating State-of-the-Art ASR for Clinical Applications for Older Adults
- 对比通用多语言与专为老年人优化的荷兰语语音模型
- 通用模型表现优于微调模型，且压缩后可提升速度与精度平衡
- 发现高错误率输入场景，为临床应用提供改进方向
语音控制界面可在临床环境中辅助老年人，例如通过聊天机器人实现交互，但针对少数群体的可靠语音识别仍是瓶颈。本研究评估了当前最先进的语音识别（ASR）模型在荷兰老年群体语言使用上的表现，这些用户与专为老年群体设计的Welzijn.AI聊天机器人互动。我们对比了通用多语言模型与针对老年人荷兰语微调的模型，并考察其处理速度。结果表明，通用多语言模型优于微调模型，说明现代ASR模型具备良好的泛化能力，无需特定适配即可在真实场景中使用。此外，裁剪通用模型有助于在准确率与速度间取得更好平衡。然而，研究也识别出导致高词错误率的输入模式，并将其置于实际应用场景中进行分析。
原文摘要 · Abstract (English)
Voice-controlled interfaces can support older adults in clinical contexts -- with chatbots being a prime example -- but reliable Automatic Speech Recognition (ASR) for underrepresented groups remains a bottleneck. This study evaluates state-of-the-art ASR models on language use of older Dutch adults, who interacted with the Welzijn.AI chatbot designed for geriatric contexts. We benchmark generic multilingual ASR models, and models fine-tuned for Dutch spoken by older adults, while also considering processing speed. Our results show that generic multilingual models outperform fine-tuned models, which suggests recent ASR models can generalise well out of the box to real-world datasets. Moreover, our results indicate that truncating generic models is helpful in balancing the accuracy-speed trade-off. Nonetheless, we also find inputs which cause a high word error rate and place them in context.
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