用语音识别与文本分析，提前预测痴呆症严重程度。
Dementia Detection using Multi-modal Methods on Audio Data
- 结合Whisper语音转写与RoBERTa模型生成认知评分
- 在PROCESS_V1数据集上达到2.6911的RMSE误差
- 适合医疗辅助诊断与早期筛查研究者使用
痴呆症是一种导致认知能力逐渐衰退的神经退行性疾病，全球范围内研究众多。其主要症状之一是言语表达困难，而早期检测有助于延缓病情恶化。本文提出一种基于音频数据的多模态方法，利用Whisper模型将患者语音文件转为文本，再通过RoBERTa回归模型生成MMSE评分，以评估认知受损程度。实验采用PROCESS_V1数据集（源自PROCESS Grand Challenge 2025），模型取得2.6911的RMSE，比基准模型降低约10%。
原文摘要 · Abstract (English)
Dementia is a neurodegenerative disease that causes gradual cognitive impairment, which is very common in the world and undergoes a lot of research every year to prevent and cure it. It severely impacts the patient's ability to remember events and communicate clearly, where most variations of it have no known cure, but early detection can help alleviate symptoms before they become worse. One of the main symptoms of dementia is difficulty in expressing ideas through speech. This paper attempts to talk about a model developed to predict the onset of the disease using audio recordings from patients. An ASR-based model was developed that generates transcripts from the audio files using Whisper model and then applies RoBERTa regression model to generate an MMSE score for the patient. This score can be used to predict the extent to which the cognitive ability of a patient has been affected. We use the PROCESS_V1 dataset for this task, which is introduced through the PROCESS Grand Challenge 2025. The model achieved an RMSE score of 2.6911 which is around 10 percent lower than the described baseline.
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