Seaver Autism Center for Research and Treatment, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1230, New York, NY 10029, USA; Department of Psychology, Stony Brook University, Stony Brook, NY 11794, USA.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, Box 1230, New York, NY 10029, USA.
Psychiatry Res. 2024 Oct;340:116109. doi: 10.1016/j.psychres.2024.116109. Epub 2024 Jul 30.
Speech and language differences have long been described as important characteristics of autism spectrum disorder (ASD). Linguistic abnormalities range from prosodic differences in pitch, intensity, and rate of speech, to language idiosyncrasies and difficulties with pragmatics and reciprocal conversation. Heterogeneity of findings and a reliance on qualitative, subjective ratings, however, limit a full understanding of linguistic phenotypes in autism. This review summarizes evidence of both speech and language differences in ASD. We also describe recent advances in linguistic research, aided by automated methods and software like natural language processing (NLP) and speech analytic software. Such approaches allow for objective, quantitative measurement of speech and language patterns that may be more tractable and unbiased. Future research integrating both speech and language features and capturing "natural language" samples may yield a more comprehensive understanding of language differences in autism, offering potential implications for diagnosis, intervention, and research.
言语和语言差异一直被描述为自闭症谱系障碍(ASD)的重要特征。语言异常范围从语音的音高、强度和语速的韵律差异,到语言的特殊性和语用学及互惠对话的困难。然而,发现的异质性和对定性、主观评估的依赖限制了对自闭症中语言表现型的全面理解。本综述总结了 ASD 中言语和语言差异的证据。我们还描述了语言研究的最新进展,这些进展得益于自动化方法和自然语言处理(NLP)和语音分析软件等软件的帮助。这些方法允许对言语和语言模式进行客观、定量的测量,这些模式可能更易于处理且无偏倚。整合言语和语言特征并捕捉“自然语言”样本的未来研究可能会更全面地了解自闭症中的语言差异,为诊断、干预和研究提供潜在影响。