View article

Speech recognition is remarkably robust to the listening background, even when the energy of background sounds strongly overlaps with that of speech. How the brain transforms the corrupted acoustic signal into a reliable neural representation suitable for speech recognition, however, remains elusive. Here, we hypothesize that this transformation is performed at the level of auditory cortex through adaptive neural encoding, and we test the hypothesis by recording, using MEG, the neural responses of human subjects listening to a narrated story. Spectrally matched stationary noise, which has maximal acoustic overlap with the speech, is mixed in at various intensity levels. Despite the severe acoustic interference caused by this noise, it is here demonstrated that low-frequency auditory cortical activity is reliably synchronized to the slow temporal modulations of speech, even when the noise is twice as strong as the …