An adaptive model of lexical repairs in language production

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.