Xie, Liu, & Jaeger (2020). Cross-talker generalization during foreign-accented speech perception

Speech perception depends on the ability to generalize previously experienced input effectively across talkers. How such cross-talker generalization is achieved has remained an open question. In a seminal study, Bradlow & Bent (2008, henceforth BB08) found that exposure to just five minutes of accented speech can elicit improved recognition that generalizes to an unfamiliar talker of the same accent (N=70 participants). Cross-talker generalization was, however, only observed after exposure to multiple talkers of the accent, not after exposure to a single accented talker. This contrast between single- and multi-talker exposure has been highly influential beyond research on speech perception, suggesting a critical role of exposure variability in learning and generalization. We assess the replicability of BB08’s findings in two large-scale perception experiments (total N=640) including 20 unique combinations of exposure and test talkers. Like BB08, we find robust evidence for cross-talker generalization after multi-talker exposure. Unlike BB08, we also find evidence for generalization after single-talker exposure. The degree of cross-talker generalization depends on the specific combination of exposure and test talker. This and other recent findings suggest that exposure to cross-talker variability is not necessary for cross-talker generalization. Variability during exposure might affect generalization only indirectly, mediated through the informativeness of exposure about subsequent speech during test: similarity-based inferences can explain both the original BB08 and the present findings. We present Bayesian data analysis, including Bayesian meta-analyses and replication tests for generalized linear mixed models. All data, stimuli, and reproducible literate (R markdown) code are shared via OSF.