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测了长期的认知发展，学术成就和成年后的预期收入。早期的语言发展反过来取决于与成年人的语言互动。越来越多的研究人员使用一整天的录音来研究儿童语言发展和儿童与看护者的互动。与简短的语言样本相比，一天的录音记录了孩子在一天中的全部经历。全天录音也被用于应用设置。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水平家庭的大规模干预项目，包括芝加哥的三千万字计划和普罗维登斯谈话计划，都在使用一整天的录音，向父母提供自动化的个性化反馈，了解他们的孩子何时以及多久听到成人的话，并经历谈话的转折。全天录音的特点对研究人员和从业人员有利，但也带来了独特的挑战。首先，它们的长持续时间是研究儿童与成人互动的时间动态的理想选择，但利用长持续时间需要招募自动语音识别技术。当前的自动语音识别系统在儿童语音方面存在困难，并且受到录音中所表示的嘈杂和变化的声学环境的挑战。另一个挑战是，录音捕捉私人时刻，需要长时间的人听删除。这使得研究人员很难公开分享这些录音，因此个别研究实验室收集的录音的潜在价值没有得到充分发挥。该项目将创建一个名为HomeBank的新资源，它将有三个关键组成部分：（1）包含已被人类听众移除私人信息的长达一天的音频记录的公共数据集，（2）一个更大的数据集，包含大约10到100倍的录音时间，这些录音没有删除私人信息，并且是免费的，但仅限于那些在人类研究道德方面受过训练的人，以及（3）一个开源的计算机程序库，可以自动分析一整天的录音。HomeBank将利用现有的网络基础设施来共享语言数据，称为TalkBank。数据集中包含的全天录音将代表典型的发育和临床群体，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。我们希望主要用户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计算机程序最终将改善早期干预所依据的数据，并改善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.