PNAS：利用录音发现儿童自闭症

　　一份报告说，科学家开发出了一种自动系统，通过分析儿童的语音录音能够预测儿童的年龄或者发现自闭症儿童或者语言发育迟缓。Kimbrough Oller博士及其同事分析了安装在200多位 10个月到4岁的儿童的衣服上的电池录音机录制的近1500条全天音轨。

　　一个自动系统把儿童的声音与环境声音分离开，然后根据语音发育理论定义的特性对儿童的发声进行归类和评级。这组科学家发现了典型的正在发育的儿童与那些此前被诊断出患有自闭症和语言发育迟缓的儿童发音的一致的差异，而且发现这些分析可以可靠地预测一位正常发育的儿童的年龄。这组作者说，用于预测年龄并区分每个组的儿童的主要因素是儿童发出作为词语的基础的类似于音节的声音的能力。

　　这组作者提出，这种方法可以让科学家分析在儿童的自然家庭环境下录制的许多语音，它很快就可能帮助针对自闭症和其他语言和发育障碍的早期检测。(生物谷Bioon.com)

　　生物谷推荐原文出处：

　　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.