Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development
Automated vocal analysis of naturalistic recordings from children with autism, language delay, and typical development
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DOI：
10.1073/pnas.1003882107
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发表时间：
2010-07-27
影响因子：
11.1
通讯作者：
Warren, S. F.
中科院分区：
文献类型：
--
作者：
Oller, D. K.;Niyogi, P.;Warren, S. F.
几代人以来，对声音发展及其在语言中的作用的研究一直在艰苦地进行，人类转录员和分析师从少量记录样本中编码和测量。我们的研究说明了一种方法，通过自动分析在儿童家中自然收集的大量全天录音来获得早期言语发展的措施。一个主要的目标是提供洞察力的发展，婴儿控制的基础设施的语音特征，通过大规模的统计分析战略选择的声学参数。在追求这一目标的过程中，我们发现，我们实施的第一种自动化方法不仅能够跟踪儿童在已知在语音中起关键作用的声学参数上的发展，而且还能够将发声与典型发育中的儿童和自闭症或语言延迟儿童区分开来。该方法是完全自动化的，没有人为干预，允许以前所未有的规模进行有效的采样和分析。这项工作显示了从根本上加强声乐发展研究的潜力，并为用于检测儿童早期语言相关障碍的电池增加了一个完全客观的测量方法。因此，自动化分析应该很快就能有助于早期疾病的筛查和诊断程序，更普遍的是，研究结果为自然环境中的语言研究提供了基本方法。
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.