Vocal exploration and interaction in the emergence of speech

描述（由申请人提供）：我们建议对早期人类发声进行纵向和微观遗传评估，作为语音的基础。这项工作开始专注于典型的声乐发展中的进化知情，动态系统框架阐明声乐发展的关键特征。这项工作继续测试的关键功能，在预测发展和发展异常（自闭症，语言延迟）的作用。基础工作的重点是：1）婴儿倾向于积极探索发声，创造和系统地重复类别（首先，尖叫，咆哮，元音样的声音等）。2）婴儿倾向于使用这些类别进行互动; 3）照顾者倾向于关注婴儿发声并识别婴儿发声类别以及它们如何表达婴儿状态和社会功能;（4）动态交互，其中看护者和婴儿系统地对发声作出反应，并以特定于婴儿发声类别及其功能的方式调节反应。这项工作的突破是：1）比以前的工作更详细地关注形成婴儿-婴儿发声互动支点的发声类别，影响发声及其社会功能; 2）通过微遗传方法，利用照顾者的计算机交互式日记数据，广泛的实验室记录，以及家中的全天记录，改善对变化的看法; 3）通过由受过训练的观察者和声学分析师在生命的前30个月期间对实验室和家庭录音进行复杂的编码，增强对婴儿和看护者发声的观点; 4.利用先进的定量工具（滞后序列分析、递归量化分析和神经网络建模），以促进对发声数据的快速和有洞察力的处理;以及5）以前所未有的规模提供对婴儿声音类别和看护者声音反应的自动分析（数千小时的自然主义录音）由大量人类编码和声学分析数据提供信息和验证。这些方法将产生非凡的潜在影响，提供了一个典型的经验发展的发展解释的参考点。影响研究将使用研究中产生的自动分析工具测试研究确定的声音类别发展和相互作用的关键变量，作为1）发展的预测因素，2）三组儿童（通常是发展中，自闭症，语言延迟）的分化，以及3）现有数据库中语言和社会发展的结果测量，包括1486个全天录音。 公共卫生相关性：这项工作将为发声发展和语言提供科学基础，并为自动化大规模发育监测典型发育中的婴儿发声和互动奠定基础。这项工作将预测发展水平，发展异常（自闭症，语言延迟）以及语言和社会措施的结果。

DESCRIPTION (provided by applicant): We propose longitudinal and microgenetic evaluation of early human vocalizations as a foundation for speech. The work starts with focus on typical vocal development within an evolutionarily-informed, dynamic systems framework illuminating key features of vocal development. The work proceeds to test the key features in terms of their roles in predicting development and anomalies of development (autism, language delay). The basic work focuses on: 1) the infant tendency to actively explore vocalization, creating and systematically repeating categories (at first, squeals, growls, vowel-like sounds, etc.) that become increasingly speech-like across the 1st year, 2) the infant tendency to engage in interaction using those categories; 3) the caregiver tendency to attend to infant vocalization and recognize both the infant vocal categories and how they express infant state and social functions; and 4) the dynamic interaction, where caregiver and infant respond systematically to vocalizations and modulate reactions in ways that are specific to the infant vocal categories and their functions. This work breaks ground by: 1) focusing in much more detail than in prior work on the vocal categories that form the fulcrum of caregiver-infant vocal interaction, affecting both vocalizations and their social functions; 2) improving perspectives on change through microgenetic approaches with computer-interactive diary data from caregivers, extensive laboratory recordings, and all-day recordings in the home; 3) enhancing perspectives on both infant and caregiver vocalizations through sophisticated coding of both lab and home recordings by trained observers and acoustic analysts across many dimensions of action during the 1st 30 months of life; 4) exploiting advanced quantitative tools (lag sequential analysis, recurrence quantification analysis, and neural network modeling) to facilitate rapid and insightful processing of vocalization data; and 5) providing automated analysis of infant vocal categories and caregiver vocal reactions at unprecedented scales (thousands of hours of naturalistic recording) informed and validated by an extensive body of human coded and acoustically analyzed data. These approaches will yield extraordinary potential impact by providing an empirical typically-developing reference point for developmental interpretation. Impact studies will test the key variables of vocal category development and interaction determined by the research using automated analytical tools also produced in the research as predictors of 1) development, 2) differentiation of three groups of children (typically developing, autistic, language delayed), and 3) outcome measures of language and social development in an existing database including 1486 all-day recordings. PUBLIC HEALTH RELEVANCE: This work will provide scientific foundations in vocal development and language, and foundations for automated large-scale developmental monitoring of typically developing infant vocalizations and interactions. The work will predict developmental level, anomalies of development (autism, language delay) and outcomes on language and social measures.