Perception of accent distance by children and adults: Identifying acoustic-phonetic parameters using machine learning

During speech perception, listeners extract both linguistic and socio-indexical information. A salient aspect of talker identity relates to their accent, including their language learning status (first- or second-language learner - L1 or L2). Adults are highly sensitive to the presence and strength of an unfamiliar accent. Children are also sensitive to the presence of an L2 accent, but may have less awareness of differences among L1 varieties. Children’s perception of accent strength has received relatively less attention. Furthermore, the acoustic-phonetic cues that children attend to when making accent judgements are not well identified. Here, we address these issues by testing 6- and 12-year-old children and adults on a ladder task in which listeners ranked L1 and L2 talkers of English relative to a local accent baseline. Both groups of children’s rankings were similar to adults’. To interpret the speaker-dependent and -independent stimulus factors driving accent rankings across ages, machine learning was employed. Phonetic differences were the largest contributor to accent rankings; intonation, rhythm, stress, sentence length and complexity also contributed similarly across age groups. This analysis demonstrates the promise of machine learning for identifying the stimulus factors that drive accent perception. This approach could be taken to isolate factors impacting other aspects of L1/L2 processing including intelligibility, comprehension, and listening effort across the lifespan. The results support the primacy of segmental differences in perceptual accent scaling, and elucidate the specific phonological accent components that may drive social decision making starting in the early school age years.