用语音数据自动预测自闭症儿童社交沟通严重程度
Developing an End-to-End Framework for Predicting the Social Communication Severity Scores of Children with Autism Spectrum Disorder
- 端到端框架：语音识别+微调语言模型处理原始语音
- 与人工评分相关性达0.6566，具备客观评估潜力
- 适合临床辅助诊断与早期干预研究者参考
自闭症谱系障碍（ASD）是一种影响个体沟通能力和社交互动的终身疾病。由于其特征性行为对基础发育阶段有深远影响，早期诊断与干预至关重要。然而，标准化诊断工具存在局限，亟需发展客观、精准的诊断方法。本文提出一种端到端框架，仅通过原始语音数据即可自动预测儿童自闭症的社交沟通严重程度。该框架首先使用自闭症儿童语音数据微调自动语音识别模型，再结合微调的预训练语言模型生成最终评分。实验结果显示，该方法与人工评分的皮尔逊相关系数达到0.6566，展现出作为可访问、客观的ASD评估工具的潜力。
原文摘要 · Abstract (English)
Autism Spectrum Disorder (ASD) is a lifelong condition that significantly influencing an individual's communication abilities and their social interactions. Early diagnosis and intervention are critical due to the profound impact of ASD's characteristic behaviors on foundational developmental stages. However, limitations of standardized diagnostic tools necessitate the development of objective and precise diagnostic methodologies. This paper proposes an end-to-end framework for automatically predicting the social communication severity of children with ASD from raw speech data. This framework incorporates an automatic speech recognition model, fine-tuned with speech data from children with ASD, followed by the application of fine-tuned pre-trained language models to generate a final prediction score. Achieving a Pearson Correlation Coefficient of 0.6566 with human-rated scores, the proposed method showcases its potential as an accessible and objective tool for the assessment of ASD.
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