Kothare Hardik, Ramanarayanan Vikram, Neumann Michael, Liscombe Jackson, Richter Vanessa, Lampinen Linnea, Bai Alison, Preciado Cristian, Brogan Katherine, Demopoulos Carly
Modality.AI, Inc., San Francisco, CA.
University of California, San Francisco.
J Speech Lang Hear Res. 2025 Feb 4;68(2):419-434. doi: 10.1044/2024_JSLHR-23-00080. Epub 2024 Dec 19.
We investigate the extent to which automated audiovisual metrics extracted during an affect production task show statistically significant differences between a cohort of children diagnosed with autism spectrum disorder (ASD) and typically developing controls.
Forty children with ASD and 21 neurotypical controls interacted with a multimodal conversational platform with a virtual agent, Tina, who guided them through tasks prompting facial and vocal communication of four emotions-happy, angry, sad, and afraid-under conditions of high and low verbal and social cognitive task demands.
Individuals with ASD exhibited greater standard deviation of the fundamental frequency of the voice with the minima and maxima of the pitch contour occurring at an earlier time point as compared to controls. The intensity and voice quality of emotional speech were also different between the two cohorts in certain conditions. Additionally, facial metrics capturing the acceleration of the lower lip, lip width, eye opening, and vertical displacement of the eyebrows were also important markers to distinguish between children with ASD and neurotypical controls. Both facial and speech metrics performed well above chance in group classification accuracy.
Speech acoustic and facial metrics associated with affect production were effective in distinguishing between children with ASD and neurotypical controls.
我们研究了在情感产生任务中提取的自动视听指标在被诊断为自闭症谱系障碍（ASD）的儿童队列和发育正常的对照组之间显示出统计学显著差异的程度。
40名患有ASD的儿童和21名神经典型对照组与一个带有虚拟代理蒂娜的多模态对话平台进行互动，蒂娜在高言语和社会认知任务需求以及低言语和社会认知任务需求的条件下，引导他们完成促使面部和声音表达四种情绪——快乐、愤怒、悲伤和恐惧——的任务。
与对照组相比，患有ASD的个体语音基频的标准差更大，音高轮廓的最小值和最大值出现在更早的时间点。在某些条件下，两个队列之间情感语音的强度和音质也有所不同。此外，捕捉下唇加速度、唇宽、眼睛睁开和眉毛垂直位移的面部指标也是区分患有ASD的儿童和神经典型对照组的重要标志。面部和语音指标在组分类准确性方面均表现出远高于随机水平的性能。
与情感产生相关的语音声学和面部指标在区分患有ASD的儿童和神经典型对照组方面是有效的。
