在自闭症的语音识别中使用自动化技术和数据挖掘
Rongjie Mao1,2, Yuncheng Zhu3
1Department of Child and Adolescent Psychiatry, Shanghai Hongkou Mental Health Center, Shanghai, China.
Brain and behavior
|January 28, 2026
概括
自动语音分析为早期自闭症谱系障碍 (ASD) 识别提供了一种可扩展的方法. 人工智能和数据挖掘方面的进步是有希望的,但广泛的临床应用仍然面临挑战.
科学领域:
- 语音分析 语音分析
- 人工智能的人工智能
- 生物标志物 生物标志物
背景情况:
- 早期识别自闭症谱系障碍 (ASD) 对于更好的结果至关重要.
- 语音分析为ASD检测提供了一个非侵入性的生物标志物来源.
- 手动语音分析耗时,需要自动化解决方案.
研究的目的:
- 审查基于语音的ASD评估的方法进步.
- 专注于自动化工具,数据挖掘方法和临床翻译.
- 涵盖从1994年到2025年的各种任务和环境的发展.
主要方法:
- 审查过的自动化工具链 (例如,LENA,Prat,Whisper,wav2vec 2.0).
- 检查了从回归到深度学习的机器学习方法 (CNN/LSTM,变压器).
- 专注于数据挖掘和人工智能方法用于ASD的语音和语言表征.
主要成果:
- 自动语音指数在ASD检测和严重程度相关性方面表现出中高的准确性.
- 跨语言,年龄和设置的性能变化是一个持续的问题.
- 挑战包括异质评估,小型数据集,隐私,公平性和可解释性.
结论:
- 优先优化和整合现有的工具链用于ASD语音评估.
- 促进全球,保护隐私的数据共享,用于研究.
- 利用人工智能创新进行增强,标记效率,以及可解释的,准备就绪的AI.
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