语音声学允许高准确性对自闭症谱系障碍进行分类
Frédéric Briend1, Céline David1, Silvia Silleresi2
1UMR 1253, iBrain, Université de Tours, INSERM, 37000, Tours, France.
Translational psychiatry
|July 8, 2023
概括
客观的语音分析有助于早期发现自闭症谱系障碍 (ASD). 声学特征准确地识别了患有自闭症的儿童,提供了潜在的新诊断工具.
科学领域:
- 神经科学是一个神经科学.
- 语音和听力科学 语言和听力科学
- 发育儿科 发育儿科
背景情况:
- 早期识别自闭症谱系障碍 (ASD) 对有效干预至关重要.
- 目前用于ASD的诊断工具往往缺乏足够的诊断能力.
- 需要采取客观措施来提高自闭症检测的准确性和效率.
研究的目的:
- 评估语音声学特征的分类性能,用于识别患有自闭症儿童.
- 为了比较声乐生物标志物的诊断准确度与神经类型儿童和具有发育性语言障碍或带有耳植入器的感觉神经听力损失的异质群体.
主要方法:
- 一项追溯诊断研究涉及108名儿童 (38名自闭症,24名典型发育,46名DLD/CI).
- 从非单词重复任务中分析语音样本的声学特性.
- 使用蒙特卡洛交叉验证和ROC监督的k-Means集群的分类模型的开发.
主要成果:
- 语音声学在将ASD与典型发育儿童分类方面取得了91%的准确性.
- 当将自闭症与非自闭症儿童的异质组区分开来时,观察到85%的准确性.
- 该研究表明,与使用类似方法的先前研究相比,该研究的准确性更高.
结论:
- 语音声学参数是一种可行的,易于测量的工具,可帮助诊断ASD.
- 客观声乐生物标志物显示出改善早期自闭症检测的巨大潜力.
- 这些发现支持将声学语音分析集成到ASD的诊断协议中.
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