自闭症谱系障碍的口语流利个体的语言模式障碍:机器学习分析
Chuanbo Hu1, Jacob Thrasher2, Wenqi Li1
1Department of Computer Science, University at Albany, Albany, NY, United States.
Frontiers in neuroinformatics
|November 10, 2025
概括
在具有自闭症谱系障碍 (ASD) 的口语流利的个体中识别微妙的语音模式是具有挑战性的. 这项研究发现,除了Mel-Frequency Cepstral 系数之外,特定的声学和声特征有效地表征了ASD语音异常.
科学领域:
- 神经科学是一个神经科学.
- 语音语言病理学 语音语言病理学
- 计算语言学 计算语言学
背景情况:
- 在口语流利的个体中诊断自闭症谱系障碍 (ASD) 由于微妙和异质的语言模式而带来挑战.
- 现有的诊断方法可能无法完全捕捉ASD语音相关症状的细微差别.
研究的目的:
- 在口语流利的个体中识别与ASD相关的独特语音特征.
- 在使用自闭症诊断观察表 (ADOS-2) 的检查员与患者对话中分析语音模式.
主要方法:
- 从ADOS-2模块4对话中分析了40个与语音相关的特征 (语调,音量,速度,暂停,光谱特征,色彩,持续时间).
- 训练机器学习模型,包括支持矢量机器 (SVM),以根据语音特征对ASD参与者进行分类.
- 使用交叉验证和分类指标来评估模型性能.
主要成果:
- 一个支持向量机 (SVM) 模型使用所有40个功能实现了84.49%的F1得分.
- 当 Mel-Frequency Cepstral Coefficients (MFCC) 和 Chroma 特征被排除时,性能得到了 86.27% 的 F1 评分和 85.77% 的准确性,专注于形和光谱特征.
- 在优化模型中,光谱分布和光谱心位被确定为关键特征.
结论:
- 一组简洁的非MFCC和选定的光谱特征有效地描述了ASD口语流利个体的语言异常.
- 数据驱动的语音分析模型显示有可能补充ASD的临床评估.
- 这种方法增强了对ASD语音相关表现的理解.
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