不同的不同方式:在自闭症谱系障碍中对声音和表情的网络分析方法
Ethan Weed1, Riccardo Fusaroli1, Elizabeth Simmons2
1Linguistics, Cognitive Science, and Semiotics, Aarhus University, Aarhus, Denmark.
概括
这项研究使用了网络分析,为患有自闭症谱系障碍 (ASD) 和神经类型 (NT) 扬声器的人在演讲中找到不同的声学配置文件. 这些发现揭示了理解ASD语言模式的新方法.
科学领域:
- 语言学的语言学.
- 神经科学是一个神经科学.
- 语音科学 语言科学
背景情况:
- 区分自闭症谱系障碍 (ASD) 和神经类型 (NT) 发言者之间的表情是具有挑战性的.
- 在ASD中不典型的语音模式可能源于不同的声学配置,而不是单一的非典型性.
研究的目的:
- 调查基于网络的声学配置文件是否可以解释ASD和NT扬声器之间的prosody中的群体差异.
- 探索社区检测算法在识别基于声学特征的扬声器集群中的实用性.
主要方法:
- 从26个扬声器 (有和没有ASD) 的语音数据被建模为声学特征网络.
- 一个社区检测算法识别了声学上相似的扬声器集群.
- 集群与人类非典型性评级 (天真和专家) 相比较.
主要成果:
- 出现了三种不同的说话群:主要是ASD,主要是NT,以及混合的ASD/NT.
- 人类评分器可靠地区分了所有集群中的ASD和NT扬声器.
- 网络分析揭示了语音模式中潜在的声学结构.
结论:
- 社区检测方法提供了一个对人类评级的补充方法,用于分析ASD中的prosody.
- 网络分析可以帮助在ASD人群中描述多样化的语调特征.
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