<p>Successful language comprehension requires listeners to rapidly adapt to linguistic variability, including accented speech. While a growing body of research suggests that long-term exposure to diverse linguistic input can facilitate such adaptation, characterizing the real-world experience remains a challenge. Traditional self-report measures are limited by recall bias and fail to capture incidental exposure. To address this limitation, we introduce a novel geolocation-based metric that quantifies local linguistic diversity using US Census data at the zip code level. Specifically, we use the proportion of nonnative English speakers as a proxy for environmental exposure to accented speech. We report an initial application of this method in a large-scale online perceptual study (<i>N</i> = 647) of monolingual English speakers across the United States. Participants completed a cross-modal matching task designed to assess rapid adaptation to Chinese-accented English. Results show that our census-based metric predicted both subjective familiarity with the accent and perceptual adaptation during the first few moments of exposure. Crucially, adaptation was predicted by the prevalence of Chinese-accented speech in participants’ local environment, but not by the prevalence of other accents (e.g., Spanish), suggesting accent-dependent facilitation. This new geolocation-based approach provides a scalable, objective complement to self-report measures, and is readily applicable even to populations where self-reporting proves infeasible or unreliable, such as older adults and clinical populations. It will enhance our understanding of how linguistic diversity in the environment shapes speech perception, adaptation, and learning.</p>