在自闭症谱系障碍中,在情感产生期间的声音和面部行为
Hardik Kothare1, Vikram Ramanarayanan1,2, Michael Neumann1
1Modality.AI, Inc., San Francisco, CA.
Journal of speech, language, and hearing research : JSLHR
|December 19, 2024
概括
自动语音和面部分析可以区分自闭症谱系障碍 (ASD) 的儿童与神经类型儿童. 这些视听指标有效地识别出情绪表达的差异,帮助诊断.
科学领域:
- 语音语言病理学 语言病理学
- 发展心理学是发展心理学.
- 生物医学工程 生物医学工程
背景情况:
- 自闭症谱系障碍 (ASD) 在社交沟通和情绪表达方面存在挑战.
- 客观地测量ASD儿童的情感行为对于诊断和干预至关重要.
- 视听数据的自动化分析提供了一种新的方法来量化情感沟通中的微妙差异.
研究的目的:
- 确定从影响生产任务中自动化的视听指标是否可以区分自闭症谱系障碍 (ASD) 的儿童与典型的发展对照.
- 调查ASD儿童与神经类型对照儿童之间声音和面部情绪表达的差异.
主要方法:
- 40名患有自闭症的儿童和21名神经类型对照者与虚拟代理人Tina互动.
- 参与者在不同的任务要求下传达了四种情绪 (快乐,愤怒,悲伤,害怕).
- 对声 (基本频率,强度,声音质量) 和面部 (唇,眼睛,眉毛运动) 度量进行了自动分析.
主要成果:
- 与对照人群相比,患有自闭症的儿童表现出更大的语音音调变性和更早的音调轮变化.
- 在特定条件下观察到语音强度和语音质量的显著差异.
- 面部指标,包括下唇加速,唇宽,眼睛开口和眉毛移位,是关键的歧视因素.
- 语音和面部表情指标都实现了高分类准确度,超过了机会.
结论:
- 从情感生产任务中获得的自动语音和面部指标在区分ASD儿童与神经类型对照中是有效的.
- 这些客观措施为ASD的情感沟通差异提供了有价值的见解.
- 这种方法有可能增强诊断工具和理解ASD的情绪表达.
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