Combining voice and language features improves automated autism detection.
Combining voice and language features improves automated autism detection.
复制标题
语音和语言功能相结合可改善自闭症检测。
DOI：
10.1002/aur.2733
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发表时间：
2022-07
期刊：
影响因子：
4.7
通讯作者：
Fombonne, Eric
中科院分区：
文献类型：
--
作者：
MacFarlane, Heather;Salem, Alexandra C.;Chen, Liu;Asgari, Meysam;Fombonne, Eric
关键词：
表达和接受语言的变化，务实语言的难度和韵律困难都是自闭症谱系障碍的特征（ASD）。将个人分类为ASD或非ASD。 ADOS-2任务使用支持向量机（SVM；二进制分类器）和接收器操作特征（ROC）导致曲线下的ROC区域（AUC）在0.7800下，ALM模型是0.8748的AUC，并且均为0.8748的模型。时间。 AVM模型检测到的ASD参与者的语言配置文件比语言模型的组合，语言和语音特征的自动化方式成功地区分了有或没有自闭症的儿童。
自闭症的人通常会使用传统的临床测量和语言测试的沟通差异，无法使用158名7至17岁儿童的语言记录进行捕获。
Variability in expressive and receptive language, difficulty with pragmatic language, and prosodic difficulties are all features of autism spectrum disorder (ASD). Quantifying language and voice characteristics is an important step for measuring outcomes for autistic people, yet clinical measurement is cumbersome and costly. Using natural language processing (NLP) methods and a harmonic model of speech, we analyzed language transcripts and audio recordings to automatically classify individuals as ASD or non-ASD. One-hundred fifty-eight participants (88 ASD, 70 non-ASD) ages 7 to 17 were evaluated with the autism diagnostic observation schedule (ADOS-2), module 3. The ADOS-2 was transcribed following modified SALT guidelines. Seven automated language measures (ALMs) and 10 automated voice measures (AVMs) for each participant were generated from the transcripts and audio of one ADOS-2 task. The measures were analyzed using support vector machine (SVM; a binary classifier) and receiver operating characteristic (ROC). The AVM model resulted in an ROC area under the curve (AUC) of 0.7800, the ALM model an AUC of 0.8748, and the combined model a significantly improved AUC of 0.9205. The ALM model better detected ASD participants who were younger and had lower language skills and shorter activity time. ASD participants detected by the AVM model had better language profiles than those detected by the language model. In combination, automated measurement of language and voice characteristics successfully differentiated children with and without autism. This methodology could help design robust outcome measures for future research. People with autism often struggle with communication differences which traditional clinical measures and language tests cannot fully capture. Using language transcripts and audio recordings from 158 children ages 7 to 17, we showed that automated, objective language and voice measurements successfully predict the child’s diagnosis. This methodology could help design improved outcome measures for research.
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影响因子：
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作者：
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通讯作者：
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影响因子：
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