PNAS：利用錄音發現兒童自閉癥

　　一份報告說，科學家開發出了一種自動系統，通過分析兒童的語音錄音能夠預測兒童的年齡或者發現自閉癥兒童或者語言發育遲緩。Kimbrough Oller博士及其同事分析了安裝在200多位 10個月到4歲的兒童的衣服上的電池錄音機錄制的近1500條全天音軌。

　　一個自動系統把兒童的聲音與環境聲音分離開，然后根據語音發育理論定義的特性對兒童的發聲進行歸類和評級。這組科學家發現了典型的正在發育的兒童與那些此前被診斷出患有自閉癥和語言發育遲緩的兒童發音的一致的差異，而且發現這些分析可以可靠地預測一位正常發育的兒童的年齡。這組作者說，用于預測年齡并區分每個組的兒童的主要因素是兒童發出作為詞語的基礎的類似于音節的聲音的能力。

　　這組作者提出，這種方法可以讓科學家分析在兒童的自然家庭環境下錄制的許多語音，它很快就可能幫助針對自閉癥和其他語言和發育障礙的早期檢測。(生物谷Bioon.com)

　　生物谷推薦原文出處：

　　PNAS doi: 10.1073/pnas.1003882107

　　Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development

　　D. K. Ollera,b,1, P. Niyogic, S. Grayd, J. A. Richardsd, J. Gilkersond, D. Xud, U. Yapaneld, and S. F. Warrene

　　aSchool of Audiology and Speech-Language Pathology, University of Memphis, Memphis, TN 38105;

　　b Konrad Lorenz Institute for Evolution and Cognition Research, Altenberg, Austria A-3422;

　　cDepartments of Computer Science and Statistics, University of Chicago, Chicago, IL 60637;

　　d LENA Foundation, Boulder, CO 80301; and

　　eDepartment of Applied Behavioral Science and Institute for Life Span Studies, University of Kansas, Lawrence, KS 66045

　　For generations the study of vocal development and its role in language has been conducted laboriously, with human transcribers and analysts coding and taking measurements from small recorded samples. Our research illustrates a method to obtain measures of early speech development through automated analysis of massive quantities of day-long audio recordings collected naturalistically in children's homes. A primary goal is to provide insights into the development of infant control over infrastructural characteristics of speech through large-scale statistical analysis of strategically selected acoustic parameters. In pursuit of this goal we have discovered that the first automated approach we implemented is not only able to track children's development on acoustic parameters known to play key roles in speech, but also is able to differentiate vocalizations from typically developing children and children with autism or language delay. The method is totally automated, with no human intervention, allowing efficient sampling and analysis at unprecedented scales. The work shows the potential to fundamentally enhance research in vocal development and to add a fully objective measure to the battery used to detect speech-related disorders in early childhood. Thus, automated analysis should soon be able to contribute to screening and diagnosis procedures for early disorders, and more generally, the findings suggest fundamental methods for the study of language in natural environments.