Infant Vocal Communication: Typical Development and Autism Risk

 描述（由申请人提供）：研究目前正在使用全天录音跟踪早期婴儿的声音交流，这些录音用人类编码进行评估，并在有限的情况下进行大规模的自动分析。结果揭示了意想不到的发展特征，说明了语言的基础。在孟菲斯大学（UM）进行的大部分工作都建立在语言发展的基础理论之上，该理论通过提供广泛使用的声乐发展阶段模型和确定临床有用标记（特别是与典型牙牙学语（CB）的发生和巩固相关的标记）提供了基础，从而取得了显著的成功，CB是口语的关键基础。基础设施框架内的UM实证工作，主要基于纵向全天记录的人类编码，也开始对足月分娩后第1个月和早产2个月以上仍在医院的婴儿的发声基础和发声相互作用进行新的描述。这项工作指出了生命最初几个月疾病风险的可能指标。新解释的进一步发展有望阐明有声语言的发展，并为自闭症谱系障碍（ASD）等疾病提供预测因子。 拟议的研究将扩大UM的努力，以更大的规模和解决语言发展的预测和ASD通过ASD同胞研究。这项工作将联合收割机之间的UM和埃默里大学的马库斯自闭症中心（MAC），无论是独立的，并通过其卓越的自闭症中心（ACE）计划项目从美国国立卫生研究院，是在收集一个巨大的一套纵向全天记录的婴儿1）显示典型的发展（TD），2）在ASD的风险，因为他们有一个哥哥与ASD，（3）由于社会经济地位（SES）低而有语言延迟（LD）的风险。MAC/ACE研究包括使用自动声学方法对录音进行分析，但不包括对大规模录音集进行人工编码的资金。事实上，广泛的人类编码对ACE项目至关重要，因为没有它，就没有足够的黄金标准数据来提高发育预测和发育沟通障碍检测自动化方法的准确性和相关性。此外，语音模式和交互的许多方面还无法通过自动化手段来实现，人工编码是唯一可行的选择。拟议的研究将结合联合收割机的努力，UM和MAC，与1）MAC提供纵向全天记录整个第一年的生活UM从3组，2）UM执行编码和人工辅助声学分析的代表性采样段从录音，和3）两个网站共同努力，根据人类编码预测24个月的婴儿结局/分析，并基于自动化分析，该自动化分析将产生于通过人类编码/分析而成为可能的监督机器学习。这项工作的目标是改善语言发展的预测和ASD的早期识别。

 DESCRIPTION (provided by applicant): Research is now tracking early infant vocal communication using all-day recordings evaluated with human coding and in limited cases with automated analysis at very large scales. Results reveal unexpected features of development, illustrating foundations of language. Much of this work conducted at the University of Memphis (UM) is founded in an infrastructural theory of language development that has produced notable successes by supplying a widely-used stage model of vocal development and by providing the basis for determining clinically useful markers, especially associated with the onset and consolidation of canonical babbling (CB), a key foundation for spoken language. The UM empirical work within the infrastructural framework, based primarily on human-coding of longitudinal all-day recordings, has also begun to produce a new account of vocal foundations and vocal interaction both in the 1st months after full-term birth and in infants born prematurely by >2 months and still in the hospital. This work points toward possible indicators of risk for disorders in the first months of life. Further development of the new account holds promise for illuminating the development of vocal language and supplying predictors for conditions such as autism spectrum disorders (ASD). The proposed research will expand the UM effort to much larger scale and address prediction of language development and of ASD through an ASD sibling study. The work will combine forces between the UM and Emory University's Marcus Autism Center (MAC), which both independently and through its Autism Center of Excellence (ACE) Program Project from NIH, is in the process of collecting a massive set of longitudinal all-day recordings of infants 1) showing typical development (TD), 2) at risk for ASD because they have an older sibling with ASD, and 3) at risk for language delay (LD) due to low socioeconomic status (SES). The MAC/ACE research includes analysis of the recordings with automated acoustic methods, but does not include funding for human coding of the massive recording set. Extensive human coding is in fact crucial for the ACE project, because without it, there is insufficient gold-standard data in terms of which to advance the accuracy and relevance of automated methods for developmental prediction and for detection of developmental communication disorders. Furthermore, many aspects of vocal patterning and interaction are as yet simply not accessible by automated means-human coding is the only workable option. The proposed research will combine efforts of UM and MAC, with 1) MAC supplying longitudinal all-day recordings across the 1st year of life to UM from the 3 groups, 2) UM performing coding and human-assisted acoustic analysis on representatively sampled segments from the recordings, and 3) both sites working together to predict infant outcomes at 24 months based on the human coding/analysis and based on the automated analysis that will result from supervised machine learning made possible by the human coding/analysis. The work targets improving prediction of language development and early identification of ASD.