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Collaborative Research: Multimethod Investigation of Articulatory and Perceptual Constraints on Natural Language Evolution

合作研究：自然语言进化的发音和感知约束的多方法研究

基本信息

批准号：
1749376
负责人：
Matthew Dye
金额：
$ 34.4万
依托单位：
Rochester Institute of Tech
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-15 至 2023-10-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749376&HistoricalAwards=false
关键词：
Collaborative Research Multimethod Investigation Articulatory

项目摘要

Languages change over time, such that the way we speak English now is very different than the speech patterns of elder generations and our distant ancestors. This project will exploit the visual nature of sign languages--where the body parts producing language are highly visible--to determine whether languages change so that they are easier to produce or so that they are easier to understand. In doing so, the project will address fundamental theoretical questions about language change that cannot be addressed by analyzing historical samples of spoken languages. To this end, the researchers will develop computational tools that allow 3D human body poses to be automatically extracted from 2D video. Such tools will be useful for the development of automated sign language recognition, promoting accessibility for deaf and hard-of-hearing people, and for developing automated systems for recognizing and classifying human gestures. The research will involve deaf and hard-of-hearing students, helping to increase diversity in the nation's scientific workforce.It is well documented that sign languages change over time, and it is a commonly held belief that those changes have resulted from successive generations making language easier to perceive. However, most of this evidence has been anecdotal and descriptive and has not quantified changes in the ease of perception and production of ASL over time. The research team will take advantage of the fully visible articulators of sign languages to develop novel pose estimation algorithms that are able to automatically extract information contained in 2D video to create accurate 3D models of articulator movement during language production. The recent birth and rapid evolution of Nicaraguan Sign Language (NSL) has allowed researchers to study language change, from the beginning, on a compressed time-scale. By leveraging an existing NSL database--comprised of 2D videos from four generations of Nicaraguan signers--and utilizing these novel pose estimation algorithms, the researchers will be able to empirically assess the extent to which linguistic changes are driven by perceptual constraints imposed by the human visual system and/or articulatory constraints imposed by the musculoskeletal system. The researchers will also query lexical databases of American Sign Language to test predictions about the perceptual form of modern day ASL, and conduct behavioral studies with deaf and hearing users of ASL to test hypotheses regarding the allocation of visual attention as a result of both deafness and acquisition of a sign language. In doing so, the research will provide valuable information about how the human brain changes the tools we use (in this case, language) and the way that those tools in turn shape the function of the human brain. This will provide a more complex understanding of language change that illuminates the complex interaction between languages and the human beings that use them.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

语言随着时间的推移而变化，因此我们现在说英语的方式与老一辈和我们遥远的祖先的讲话模式非常不同。该项目将利用手语的视觉性质-产生语言的身体部位非常明显-以确定语言的变化是否使其更容易产生或更容易理解。在此过程中，该项目将解决语言变化的基本理论问题，这些问题无法通过分析口语的历史样本来解决。为此，研究人员将开发计算工具，允许从2D视频中自动提取3D人体姿势。这些工具将有助于开发自动手语识别，促进聋人和听力障碍者的无障碍环境，并开发识别和分类人类手势的自动系统。这项研究将涉及聋人和听力困难的学生，有助于增加国家科学工作者的多样性。有充分的证据表明，手语随着时间的推移而变化，人们普遍认为，这些变化是由于连续几代人使语言更容易理解。然而，大多数证据都是轶事和描述性的，并没有量化的变化，在容易感知和生产的美国手语随着时间的推移。研究团队将利用手语的完全可见的发音器官来开发新的姿势估计算法，这些算法能够自动提取2D视频中包含的信息，以创建语言产生过程中发音器官运动的准确3D模型。近年来，巴布亚手语（NSL）的诞生和快速发展使研究人员能够在压缩的时间尺度上从一开始就研究语言的变化。通过利用现有的NSL数据库-由四代印度尼西亚签名者的2D视频组成-并利用这些新颖的姿势估计算法，研究人员将能够凭经验评估语言变化受人类视觉系统施加的感知约束和/或肌肉骨骼系统施加的发音约束驱动的程度。研究人员还将查询美国手语的词汇数据库，以测试对现代ASL感知形式的预测，并对ASL的聋人和听力用户进行行为研究，以测试关于耳聋和手语习得导致视觉注意力分配的假设。在这样做的过程中，这项研究将提供有关人类大脑如何改变我们使用的工具（在这种情况下，语言）以及这些工具反过来塑造人类大脑功能的方式的宝贵信息。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。