Mask-wearing during the COVID-19 pandemic has prompted a growing interest in the functional impact of masks on speech and communication. Prior work has shown that masks dampen sound, impede visual communication cues, and reduce intelligibility. However, more work is needed to understand how speakers change their speech while wearing a mask and to identify strategies to overcome the impact of wearing a mask. Data were collected from 19 healthy adults during a single in-person session. We investigated the effects of wearing a KN95 mask on speech intelligibility, as judged by two speech-language pathologists, examined speech kinematics and acoustics associated with mask-wearing, and explored KN95 acoustic filtering. We then considered the efficacy of three speaking strategies to improve speech intelligibility: Loud, Clear, and Slow speech. To inform speaker strategy recommendations, we related findings to self-reported speaker effort. Results indicated that healthy speakers could compensate for the presence of a mask and achieve normal speech intelligibility. Additionally, we showed that speaking loudly or clearly—and, to a lesser extent, slowly—improved speech intelligibility. However, using these strategies may require increased physical and cognitive effort and should be used only when necessary. These results can inform recommendations for speakers wearing masks, particularly those with communication disorders (e.g., dysarthria) who may struggle to adapt to a mask but can respond to explicit instructions. Such recommendations may further help non-native speakers and those communicating in a noisy environment or with listeners with hearing loss.

In this study, 38 young adults participated in a probabilistic A/B prototype category learning task under observational and feedback-based conditions. The study compared learning success (testing accuracy) and strategy use (multi-cue vs. single feature vs. random pattern) between training conditions. The feedback-related negativity (FRN) and P3a event related potentials were measured to explore the relationships between feedback processing and strategy use under a probabilistic paradigm. A greater number of participants were found to utilize an optimal, multi-cue strategy following feedback-based training than observational training, adding to the body of research suggesting that feedback can influence learning approach. There was a significant interaction between training phase and strategy on FRN amplitude. Specifically, participants who used a strategy in which category membership was determined by a single feature (single feature strategy) exhibited a significant decrease in FRN amplitude from early training to late training, perhaps due to reduced utilization of feedback or reduced prediction error. There were no significant main or interaction effects between valence, training phase, or strategy on P3a amplitude. Findings are consistent with prior research suggesting that learners vary in their approach to learning and that training method influences learning. Findings also suggest that measures of feedback processing during probabilistic category learning may reflect changes in feedback utilization and may further illuminate differences among individual learners.

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