自然语音可以区分自闭症谱系障碍吗? 一个元分析
1School of Foreign Languages and Literature, Shandong University, Jinan 250100, China.
Behavioral sciences (Basel, Switzerland)
|February 23, 2024
概括
对自然语音的声学分析,特别是音调,显示出确定自闭症谱系障碍 (ASD) 的前景. 机器学习模型达到很高的准确性,表明语音模式是ASD检测的有价值的生物标志物.
科学领域:
- 语音声学 语音声学 语音声学
- 机器学习是机器学习.
- 神经发育障碍 神经发育障碍
背景情况:
- 自然的语音表达对人类沟通至关重要.
- 声学分析和机器学习越来越多地用于自闭症谱系障碍 (ASD) 检测.
研究的目的:
- 评估用于ASD检测的声学分析和机器学习的经验研究.
- 提供统计学支持的证据,用于用于识别自闭症的自然语音表达方式.
主要方法:
- 使用语音分析进行经验研究的元分析.
- 随机效果模型用于对声学参数的效果大小进行聚合.
- 对ASD识别准确性的机器学习研究的分析.
主要成果:
- 对于与音调相关的参数 (SMD 0.35-0.67) 的中等至大聚合效应大小,用于区分自闭症与典型发育 (TD) 个体.
- 对于时间性语音特征的不一致的发现 (SMD 0.07和-0.05).
- 机器学习模型显示,ASD识别的平均灵敏度为75.51%,特异性为80.31%.
结论:
- 自然的语音表达,特别是音调变化,对于识别患有自闭症的人来说是有效的.
- 年龄组可能会影响音调范围的差异,需要进一步调查.
- 语音声学分析与机器学习相结合,为ASD检测和研究提供了一个有希望的途径.
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