Adaptation to Noise in Human Speech Recognition Depends on Noise-Level Statistics and Fast Dynamic-Range Compression.

Human hearing adapts to background noise, as evidenced by the fact that listeners recognize more isolated words when words are presented later rather than earlier in noise. This adaptation likely occurs because the leading noise shifts ("adapts") the dynamic range of auditory neurons, which can improve the neural encoding of speech spectral and temporal cues. Because neural dynamic range adaptation depends on stimulus-level statistics, here we investigated the importance of "statistical" adaptation for improving speech recognition in noisy backgrounds. We compared the recognition of noised-masked words in the presence and in the absence of adapting noise precursors whose level was either constant or was changing every 50 ms according to different statistical distributions. Adaptation was measured for 28 listeners (9 men) and was quantified as the recognition improvement in the precursor relative to the no-precursor condition. Adaptation was largest for constant-level precursors and did not occur for highly fluctuating precursors, even when the two types of precursors had the same mean level and both activated the medial olivocochlear reflex. Instantaneous amplitude compression of the highly fluctuating precursor produced as much adaptation as the constant-level precursor did without compression. Together, results suggest that noise adaptation in speech recognition is probably mediated by neural dynamic range adaptation to the most frequent sound level. Further, they suggest that auditory peripheral compression per se, rather than the medial olivocochlear reflex, could facilitate noise adaptation by reducing the level fluctuations in the noise.