Selective cortical representation of attended speaker in multi-talker speech perception - Mesgarani, Nima; Chang, Edward F. (Nature (London), 2012)
The neural correlates of how attended speech is internally represented are described, shedding light on the ‘cocktail party problem’. Heard instinct The 'cocktail-party problem' — the question of what goes on in our brains when we listen selectively for one person's voice while ignoring many others — has puzzled researchers from various disciplines for years. Using electrophysiological recordings from neurosurgery patients listening to two speakers simultaneously, Nima Mesgarani and Edward Chang determine the neural correlates associated with the internal representation of attended speech. They find that the neural responses in the auditory cortex represent the attended voice robustly, almost as if the second voice were not there. With these patterns established, a simple algorithm trained on various speakers predicts which stimulus a subject is attending to, on the basis of the patterns emerging in the secondary auditory cortex. These results suggest that speech representation in the brain reflects not only the acoustic environment, but also the listener's understanding of these signals. As well as shedding light on a long-standing neurobiological problem, this work may give clues as to how automatic speech recognition might be improved to cope with more than one talker. Humans possess a remarkable ability to attend to a single speaker’s voice in a multi-talker background 1 , 2 , 3 . How the auditory system manages to extract intelligible speech under such acoustically complex and adverse listening conditions is not known, and, indeed, it is not clear how attended speech is internally represented 4 , 5 . Here, using multi-electrode surface recordings from the cortex of subjects engaged in a listening task with two simultaneous speakers, we demonstrate that population responses in non-primary human auditory cortex encode critical features of attended speech: speech spectrograms reconstructed based on cortical responses to the mixture of speakers reveal the salient spectral and temporal features of the attended speaker, as if subjects were listening to that speaker alone. A simple classifier trained solely on examples of single speakers can decode both attended words and speaker identity. We find that task performance is well predicted by a rapid increase in attention-modulated neural selectivity across both single-electrode and population-level cortical responses. These findings demonstrate that the cortical representation of speech does not merely reflect the external acoustic environment, but instead gives rise to the perceptual aspects relevant for the listener’s intended goal.