DHB/Collaborative Research: Using Machine Learning to Model the Interplay of Production Dynamics and Perception Dynamics in Phonological Acquision

该项目将声学建模技术应用于成人和儿童语音记录的大型多语言数据库中，以实现稳健的自动语音识别。它探讨了在任何特定的言语社区中，与言语产生和感知相关的认知表征是如何被正常发育的儿童内化的。目前的语言习得模型无法解释言语社区之间以及社区内年轻和年长说话者之间的细微语音差异，因为它们是基于将字母转录作为主要观察工具的研究。由于这个项目是语音工程、语言学以及言语和听力科学专家的合作，它使儿童语言研究人员能够开发出更细粒度的测量方法，开始回答关于语言习得动态的几个关键问题，包括以下三个问题：来自不同语言社区的成年说话者如何感知不同年龄说话者发音的声学模式？成人对儿童作品的刻板印象如何影响对儿童的演讲？成年人对儿童发音错误的知觉反应如何影响儿童掌握特定社区模式的过程？这些问题是理解不同文化之间存在哪些相似之处和不同之处的关键部分，这些问题对于我们理解人类进化的语言能力意味着什么。此外，通过将语言学和工程学方面的进展联系起来，模拟结果应为开发一种新的语音技术方法提供基础，在若干领域产生更广泛的影响，如为残疾人提供辅助技术和更民主地利用电子数据库。

This project adapts acoustic modeling techniques for robust Automatic Speech Recognition to a large multi-language database of adult and child speech recordings. It explores how cognitive representations relevant to speech production and perception in any given speech community come to be internalized by normally developing children.Current models of language acquisition cannot account for fine-grained phonetic differences between speech communities and between younger and older speakers within a community because they are based on research that uses alphabetic transcription as the primary observational tool. Because this project is a collaboration among specialists in speech engineering, linguistics, and speech and hearing science it enables child-language researchers to develop finer-grained measures to begin to answer several key questions about the dynamics of language acquisition, including the following three: How do adult speakers from different speech communities perceive the acoustic patterns in words pronounced by speakers of different ages? How do adult stereotypes about children's productions shape speech addressed to children? How do adults' perceptual responses to children's (mis)articulations shape the course of children's mastery of the community-specific patterns? These questions are a key part of understanding what similarities and differences exist across cultures and what these mean for our understanding of the human capacity for speech evolved. In addition, by linking together advances in linguistic and engineering, the modeling results should provide a basis for developing a new approach to speech technology, with broader impacts in several areas such as assistive technology for the disabled and more democratic access to electronic databases.