An adaptive model of lexical repairs in language production

语言生成中词汇修复的自适应模型

• 批准号: 2317121
• 负责人: Nazbanou Nozari
• 金额: $ 68.65万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2317121&HistoricalAwards=false
• 关键词: adaptive; model; lexical; repairs; language

Understanding how humans learn, produce and understand speech is one of the fundamental endeavors of cognitive science. This project investigates an often-overlooked aspect of speech processing, namely, how we can catch and repair the errors we make when we speak. Even though native adult speakers do not make many speech errors, children, second-language speakers, and individuals with brain injury do, and many of them show a remarkable ability to detect and repair their own errors. Repairing errors is important, because without repairs, communication can easily break down. Also, developing the ability to repair one’s errors in a new learner, like a child or a second-language learner, is a good signal that the learner is making progress. Yet, we know very little about how the brain can catch and repair speech errors. We also do not know if this ability differs between speakers of different languages. This project sheds light on these issues. Taking advantage of existing models of language production, we construct a computational model which mimics how the brain may detect and repair speech errors. We then test the predictions of this model using a large database that we build over the lifetime of the project from over 700 native speakers of four languages, English, French, Dutch, and Farsi. Using these data, we further refine and fine-tune the computational model to arrive at a model that best captures how speakers repair their speech errors. The project has two major outcomes: (1) The largest publicly available multi-lingual database of speech errors and repairs. (2) The first open-source computational model of speech error repairs. Both of these will be made freely accessible to anyone in the world who is interested in understanding or researching how humans produce language.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.

理解人类如何学习、产生和理解言语是认知科学的基本努力之一。这个项目研究了语音处理的一个经常被忽视的方面，即我们如何捕捉和修复我们说话时所犯的错误。尽管以英语为母语的成年人不会犯很多言语错误，但儿童、第二语言使用者和脑损伤的人会犯很多错误，他们中的许多人表现出了发现和修复自己错误的非凡能力。修复错误很重要，因为没有修复，通信很容易中断。此外，在一个新的学习者，如一个孩子或第二语言学习者，发展修复自己错误的能力，是一个很好的信号，学习者正在取得进展。然而，我们对大脑如何捕捉和修复语音错误知之甚少。我们也不知道这种能力在不同语言的使用者之间是否存在差异。该项目揭示了这些问题。利用现有的语言产生模型，我们构建了一个模拟大脑如何检测和修复语音错误的计算模型。然后，我们使用一个大型数据库来测试这个模型的预测，这个数据库是我们在项目的整个生命周期中从700多名母语为英语、法语、荷兰语和波斯语的人中建立的。使用这些数据，我们进一步完善和微调计算模型，以达到一个模型，最好地捕捉扬声器如何修复他们的语音错误。该项目有两个主要成果：（1）最大的公开可用的多语言语音错误和修复数据库。(2)第一个语音错误修复的开源计算模型。这两个奖项都将免费提供给世界上任何有兴趣了解或研究人类如何产生语言的人。这个奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。