Interactions between Word and Speech Sound Categorization in Language Acquisition

这个奖项是根据2009年美国复苏和再投资法案（公法111-5）资助的。婴儿首先学习什么--声音还是单词？标准的观点是婴儿首先学习声音。为了最有效地表示单词，他们必须获得一组适当的语音类别。然而，婴儿似乎能够在语音类别学习完成之前很好地识别流利语音中的单词。 语音类别往往显示出相当大的重叠，因此，只关注语音特征的分布可能会导致学习者混淆用于区分单词的差异的超类别。这项研究调查了婴儿如何同时而不是顺序地学习多层语言结构，学习从未标记的输入中对语音和单词进行分类。 本研究将结合实验和计算技术来研究婴儿是否能在人工语言学习任务中结合联合收割机这些学习过程，以及这种互动如何实际上使学习问题变得更容易，这项研究将直接有助于我们对正常语言发展的理解。这项研究是高度跨学科的，涉及心理学，语言学，统计学和计算机科学的思想，其结果有可能为未来的无序习得研究和计算机系统的开发提供信息，这些系统可以从未标记的数据中学习语音类别。

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).What do babies learn first -- sounds or words? The standard view is that infants learn sounds first. In order to represent words most efficiently, they must have acquired an appropriate set of speech sound categories. However, infants appear to be able to recognize words in fluent speech well before speech sound category learning is complete. Speech sound categories often display considerable overlap, so that focusing solely on distributions of speech sound characteristics may lead learners to posit supercategories that conflate differences used to distinguish among words. This research investigates how infants might learn multiple layers of linguistic structure simultaneously rather than sequentially, learning to categorize both speech sounds and words from unlabeled input. A combination of empirical and computational techniques will be used to investigate whether infants can combine these learning processes in artificial language learning tasks and how such interaction might actually make the learning problem easier.This research will directly contribute to our understanding of normal language development. The research is highly interdisciplinary, involving ideas from psychology, linguistics, statistics and computer science, and its outcome has the potential to inform future research on disordered acquisition and the development of computer systems that can learn speech sound categories from unlabeled data.