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When listeners encounter a difficult-to-understand talker in a difficult-to-understand situation, their perceptual mechanisms can adapt, making the talker in the situation easier to understand. This study examined talker-specific perceptual adaptation experimentally by embedding speech from second-language (L2) English talkers in varying levels of noise and collecting transcriptions from first-language English listeners (ten talkers, 100 listeners per experiment). Experiments 1 and 2 demonstrated that prior experience with a L2 talker's speech presented first without noise and then with gradually increasing levels of noise facilitated recognition of that talker in loud noise. Experiment 3 tested whether adaptation is driven by tuning-in to the talker's voice and speech patterns, by examining recognition of speech-in-loud-noise following experience with the talker in quiet. Finally, experiment 4 tested whether adaptation is driven by tuning-out the background noise, by measuring speech-in-loud-noise recognition after experience with the talker in consistently loud noise. The results showed that both tuning-in to the talker and tuning-out the noise contribute to talker-specific perceptual adaptation to L2 speech-in-noise. 

Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard—human transcription—is time- and resource-intensive. Recent advances in automatic speech recognition (ASR) systems raise the possibility of automating intelligibility measurement. This study tested 4 state-of-the-art ASR systems with second language speech-in-noise and found that one, whisper, performed at or above human listener accuracy. However, the content of whisper's responses diverged substantially from human responses, especially at lower signal-to-noise ratios, suggesting both opportunities and limitations for ASR--based speech intelligibility modeling.