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

婴儿语言习得的模型使研究人员可以测试有关婴儿如何通过语言学习母语的假设。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有意义，并专注于协同发音，这是一个根据相邻声音的质量而产生不同声音的过程。协同发音与语言中语音模式的历史创造、变化和丧失密切相关。因此，本项目中开发的模型进一步加深了我们对婴儿语言习得和历史语言变化的理解。了解学习过程的不同方面是如何相互作用的，也有助于准确定位可能导致发育性语言障碍的缺陷。最后，建立能够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，以与人类学习者相同的方式有力地适应新的语言、口音和话语领域。建立了一系列无监督的语音学习模型，以考察学习者如何考虑协同发音。虽然以前的语音学习模型已经独立地对每个声音进行了分类，但这些模型从一组约束开始，这些约束捕捉了声音之间可能的依赖关系，并使用马尔可夫随机场进行了形式化。然后，他们从数据中学习一种特定语言的制约因素。来自Childes数据库的儿童定向语音的录音通过强制对齐进行标注，作为测试语料库，用于将新开发的模型与以前的模型进行比较。该项目对现有模型的不足之处进行了严格的评估，为解释语音可变性提供了一个新的框架，并建立了支持开发和评估未来语音学习模型的语料库。

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.