Face masks are an important tool for preventing the spread of COVID-19. However, it is unclear how different types of masks affect speech recognition in different levels of background noise. To address this, we investigated the effects of four masks (a surgical mask, N95 respirator, and two cloth masks) on recognition of spoken sentences in multi-talker babble. In low levels of background noise, masks had little to no effect, with no more than a 5.5% decrease in mean accuracy compared to a no-mask condition. In high levels of noise, mean accuracy was 2.8-18.2% lower than the no-mask condition, but the surgical mask continued to show no significant difference. The results demonstrate that different types of masks generally yield similar accuracy in low levels of background noise, but differences between masks become more apparent in high levels of noise.

Over the past two years, face masks have been a critical tool for preventing the spread of COVID-19. While previous studies have examined the effects of masks on speech recognition, much of this work was conducted early in the pandemic. Given that human listeners are able to adapt to a wide variety of novel contexts in speech perception, an open question concerns the extent to which listeners have adapted to masked speech during the pandemic. In order to evaluate this, we replicated Toscano and Toscano (PLOS ONE 16(2):e0246842, 2021), looking at the effects of several types of face masks on speech recognition in different levels of multi-talker babble noise. We also examined the effects of listeners’ self-reported frequency of encounters with masked speech and the effects of the implementation of public mask mandates on speech recognition. Overall, we found that listeners’ performance in the current experiment (with data collected in 2021) was similar to that of listeners in Toscano and Toscano (with data collected in 2020) and that performance did not differ based on mask experience. These findings suggest that listeners may have already adapted to masked speech by the time data were collected in 2020, are unable to adapt to masked speech, require additional context to be able to adapt, or that talkers also changed their productions over time. Implications for theories of perceptual learning in speech are discussed.

Listeners can account for systematic variability between talkers, which is learned over exposure to multiple talkers. Previous research suggests that listeners can both generalize prior knowledge of phoneme categories from a familiar to a novel talker (Eisner & McQueen, 2005; Kraljic & Samuel, 2006, 2007) and individuate talkers, preventing generalization (Luthra, Mechtenberg, & Myers, 2021; Tamminga, Wilder, Lai, & Wade, 2020). It is unclear how listeners balance these competing demands. Participants (n = 160) learned two novel talkers (one male and one female voice) with a unique voice onset time (VOT) boundary across two days. On each day, participants were passively exposed to a bimodal distribution of VOTs from one talker, then tested on a second talker (uniform distribution). Day 1 assessed how listeners generalize to a novel talker while Day 2 assessed how the talker that was learned on Day 1 is individuated from the new talker. We found evidence for generalization after Day 1 but little evidence of learning after learning both talkers on Day 2. Two follow-up experiments using interleaved designs and supervised learning also showed little evidence for individuation. This suggests that listeners are likely accounting for variability by shifting their VOT boundary to match current input.