earticle

영어

Both human listeners and Automatic Speech Recognition (ASR) systems tend to struggle more with recognizing second-language (L2) speech than first-language (L1) speech. This study examined the performance of Whisper (a state-of-the-art ASR system) and L1 English listeners in recognizing L1 and L2 English under a controlled, homogeneous setting (using the same sentences and data collection procedures), enabling a direct comparison across listener and talker types. Speech recordings from 67 L2 English talkers and 25 L1 English talkers embedded in varying levels of background noise were tested, and transcriptions from Whisper and humans were analyzed. Across both L1 and L2 speech, Whisper showed overall higher word recognition accuracy than humans. Notably, it achieved near-perfect performance in quiet or low-noise conditions. Despite this superior performance, Whisper showed greater hallucination rates than humans under loud-noise conditions, with a particularly large gap for L2 speech. Further analysis revealed that Whisper’s hallucination rates remained higher for L2 than L1 speech even after controlling for accuracy, suggesting that these hallucinations are not merely a byproduct of recognition difficulty but reflect a functional difference in how Whisper processes L1 vs. L2 speech. Overall, these findings underscore both the strengths and limitations of Whisper: its robustness in clean listening conditions, but also its hallucination bias against L2 speech.