RI: Small: Collaborative Research: Cognitive models of the acquisition of vowels in context

RI：小：协作研究：在上下文中习得元音的认知模型

• 批准号: 1422987
• 负责人: Micha Elsner
• 金额: $ 24万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422987&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Cognitive

Models of infant language acquisition allow researchers to test hypotheses about how infants learn their native language from speech. This project focuses on how infants determine which sounds are meaningful in their language and concentrates on co-articulation, a process in which sounds are produced differently depending on qualities of neighboring sounds. Co-articulation is closely tied to the historical creation, change, and loss of sound patterns in language. As a result, the models developed in this project further our understanding of both infant language acquisition and historical language change. Knowing how different aspects of the learning process interact can also help pinpoint deficits that might underlie developmental language disorders. Finally, building models of language acquisition that can work on speech data can potentially help create speech recognition technology that adapts robustly to novel languages, accents, and domains of discourse in the same way that human learners do. A series of unsupervised phonetic learning models are created to examine how learners take into account co-articulation. Whereas previous models of phonetic learning have categorized each sound independently, these models begin with a set of constraints that capture possible dependences between sounds, formalized using a Markov random field. They then learn from the data which constraints characterize a particular language. Recordings of child-directed speech from the CHILDES database are annotated through forced alignment to serve as test corpora for comparing the newly developed models with previous models. The project yields a rigorous evaluation of where existing models fall short, a new framework for accounting for phonetic variability, and corpora to support the development and evaluation of future phonetic learning models.

婴儿语言习得模型使研究人员能够测试婴儿如何从言语中学习母语的假设。 这个项目的重点是婴儿如何确定哪些声音在他们的语言中是有意义的，并集中在共同发音，这是一个根据相邻声音的质量产生不同声音的过程。共构与语言语音模式的历史生成、变化和丧失有着密切的联系。 因此，在这个项目中开发的模型进一步我们的理解婴儿语言习得和历史语言变化。了解学习过程的不同方面如何相互作用也可以帮助查明可能导致发育性语言障碍的缺陷。最后，构建可以处理语音数据的语言习得模型可能有助于创建语音识别技术，该技术可以像人类学习者一样强大地适应新的语言，口音和话语领域。一系列的无监督语音学习模型的创建，以检查学习者如何考虑到协同发音。 虽然以前的语音学习模型对每个声音都进行了独立的分类，但这些模型开始时使用一组约束条件来捕获声音之间可能的依赖关系，并使用马尔可夫随机场进行形式化。然后，他们从数据中学习特定语言的约束特征。 从CHILDES数据库中的儿童导向的语音记录注释通过强制对齐作为测试语料库，用于比较新开发的模型与以前的模型。该项目产生了一个严格的评估现有的模型不足，一个新的框架，占语音的变化，语料库，以支持未来的语音学习模型的开发和评估。