Examining the perception-production link in speech processing through the lens of accent imitation

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Everyone knows someone who is really good at imitating accents, or "linguistic chameleons" who naturally pick up the accent of a newly adopted community. While exposure to many different accents is an everyday occurrence, particularly in linguistically diverse countries like Canada, there is an enormous amount of variability in how we recognize, and process these different ways of speaking. There is evidence from previous work that several factors, including bilingualism, musical training, and accent familiarity may facilitate imitation, and we also know that adults are notoriously worse than children at picking up new accents. However, findings have differed across studies depending on the social status and linguistic features of the accent being imitated. This project aims to uncover what makes some features of accents easier to imitate than others, and what makes some people better imitators than others, with the broader goal of understanding the cognitive architecture that governs vocal imitation. Successful accent imitation is a complex behaviour, requiring first accurate perception of the features of a given accent, followed by accurate reproduction of the same features. We use a set of experiments targeting individual sub-processes of imitation across several different linguistic features (e.g. the pronunciation of a vowel, or the pitch pattern of a sentence) in children vs. adults, and in monolinguals vs. bilinguals. In order to maintain full control over what listeners hear and imitate, we use artificially manipulated, socially neutral accents that vary only in a single feature. Our method not only allows for a systematic investigation of which factors are predictive of success in imitation, but also allows us to understand why they are predictive, by determining which links in the speech processing chain account for the variability we see across individuals. Accent imitation provides a lens through which to better understand the relationship between speech perception and speech production, a fundamental component of an accurate model of speech processing. Beyond the theoretical import, the results will provide a fresh perspective on the cognitive processes underlying difficulty in foreign sound learning, which has been shown to be linked to imitative abilities. Finally, while there have been huge advances in automatic speech recognition (e.g. Siri) in the past decade, one of the persistent challenges for these technologies is dealing with accent variability. A better understanding of how humans perceive and adapt to accents may therefore contribute to improvements in speech recognition algorithms.