RIDIR: Collaborative Research: Enabling Access to and Analysis of Shared Daylong Child and Family Audio Data

儿童在生命最初几年的语言发展预示着长期的认知发展、学业成就和成年后的预期收入。早期的语言发展反过来又依赖于与成年人的语言互动。研究人员越来越多地使用一整天的录音来研究儿童语言发展和儿童与照顾者的互动。与短小的语言样本相比，全天的录音记录了孩子在一天中经历的所有事情。一整天的录音也在应用环境中使用。例如，研究表明，当他们进入一年级时，来自较高社会经济背景的儿童比来自较低社会经济背景的儿童多听到数千万个单词，从而使社会不平等永久化。多个针对低社会经济家庭的大规模干预项目，包括芝加哥的3000万字倡议和普罗维登斯谈话项目，正在使用为期一天的录音向父母提供自动化的个性化反馈，告知他们的孩子何时以及多长时间听到成人话语和经历对话。对研究人员和从业者有利的全天录音的特点也构成了独特的挑战。首先，它们的长持续时间是研究儿童与成人互动的时间动力学的理想选择，但要利用长持续时间，需要采用自动语音识别技术。当前的自动语音识别系统在处理儿童语音方面存在困难，并且受到记录中所代表的噪声和变化的声学环境的挑战。另一个挑战是，这些录音捕捉到了需要人类长时间聆听才能删除的私人时刻。这使得研究人员很难公开共享录音，从而使各个研究实验室收集的录音的潜在价值没有完全实现。该项目将创建一个名为HomeBank的新资源，它将包括三个关键组件：(1)公共数据集，其中包含已被人类听众删除私人信息的一天的录音；(2)更大的数据集，其中包含大约十到一百倍长的录音，这些录音没有删除私人信息，并且将免费，但仅限于那些受过人类研究伦理培训的人；以及(3)一个开放源代码的计算机程序存储库，用于自动分析一天的录音。HomeBank将利用现有的网络基础设施共享名为TalkBank的语言数据。数据集中包括的全天录音将代表典型的发育中和临床群体，从新生儿到学龄儿童的年龄范围，以及一系列语言和社会经济背景。我们预计主要用户将是基础和应用儿童发展研究人员以及开发自动语音识别技术的工程师。免费访问的数据库和开放源码的计算机程序最终将改善早期干预所依据的数据和可用于向父母提供关于他们向子女提供的语言输入的反馈的工具。

A child's language development in the first few years of life predicts long-term cognitive development, academic achievement, and expected income as an adult. Early language development in turn depends on linguistic interactions with adults. Increasingly, researchers are using daylong audio recordings to study child language development and child-caregiver interactions. Compared to short language samples, daylong recordings capture the full range of experiences a child has over the course of a day. Daylong audio recordings are also being used in applied settings. For example, studies show that by the time they enter First Grade, children from higher socioeconomic backgrounds hear tens of millions more words than children from lower socioeconomic backgrounds, perpetuating social inequalities. Multiple large-scale intervention projects targeting low socioeconomic households, including the Thirty Million Words Initiative in Chicago and the Providence Talks program, are using daylong audio recordings to provide automated, personalized feedback to parents on when and how often their child hears adult words and experiences conversational turns. The features of daylong recordings that are advantageous for researchers and practitioners also pose unique challenges. For one, their long durations are ideal for studying the temporal dynamics of child-adult interaction, but taking advantage of the long durations requires the enlistment of automated speech recognition technology. Current automatic speech recognition systems have difficulties with child speech and are challenged by the noisy and varied acoustic environments represented in the recordings. Another challenge is that the recordings capture private moments that require long hours of human listening to remove. This makes it difficult for researchers to share the recordings publicly, so that the potential value of the recordings collected by individual research labs is not fully realized.This project will create a new resource, called HomeBank, that will have three key components: (1) a public dataset containing daylong audio recordings that have had private information removed by human listeners, (2) a larger dataset containing about ten to one hundred times as many hours of recording that have not had private information removed and will be free but restricted to those who have demonstrated training in human research ethics, and (3) an open-source repository of computer programs to automatically analyze the daylong audio recordings. HomeBank will take advantage of an existing cyberinfrastructure for sharing linguistic data called TalkBank. The daylong audio recordings included in the datasets will represent both typically developing and clinical groups, a range of ages from newborn infants to school age children, and a range of language and socioeconomic backgrounds. We expect the primary users to be basic and applied child development researchers as well as engineers developing automatic speech recognition technologies. The free-to-access database and the open source computer programs will ultimately improve both the data on which early interventions are based and the tools available for providing parents with feedback on the linguistic input they provide their children.