In their everyday lives, listeners often find themselves in noisy environments where they must separate one acoustic signal (e.g., a talker) from an irrelevant background of unrelated acoustic elements. This process, called auditory stream segregation, is so ubiquitous that it is often taken for granted—that is, until it requires unexpected effort. Understanding speech-in-noise (SIN) can be challenging even for normal-hearing young adults but is particularly an issue in individuals who speak multiple languages. Current clinical measures of SIN understanding often rely on linguistic stimuli—phonemes, words, or sentences—almost exclusively presented in English. This presents a potential confound: does poor performance on clinical SIN measures indicate an auditory deficit or a linguistic difference? In this talk, Michael John’s will present data from three studies that make use of stochastic figure-ground (SFG) stimuli as a non-linguistic measure of auditory stream segregation. Across these studies, he will show that SFG tasks can predict SIN performance in normal-hearing young adults, independent of language experience, and can provide insights into the psychophysiological and neural underpinnings of auditory stream segregation and SIN understanding. He will discuss plans to develop SFG into a nonlinguistic metric that can help clinicians identify SIN deficits independently of language experience, with the goal of improving audiological care for linguistically diverse populations.