Speech signals have a unique shape of long-term modulation spectrum that is distinct from environmental noise, music, and non-speech vocalizations. Does the human auditory system adapt to the speech long-term modulation spectrum and efficiently extract critical information from speech signals? To answer this question, we tested whether neural responses to speech signals can be captured by specific modulation spectra of non-speech acoustic stimuli. We generated amplitude modulated (AM) noise with the speech modulation spectrum and 1/f modulation spectra of different exponents to imitate temporal dynamics of different natural sounds. We presented these AM stimuli and a 10-minute piece of natural speech to 19 human participants undergoing electroencephalography (EEG) recording. We derived temporal response functions (TRF) to the AM stimuli of different spectrum shapes and found distinct neural dynamics for each type of TRFs. We then used the TRFs of AM stimuli to predict neural responses to the speech signals, and found that 1) the TRFs of AM modulation spectra of exponents 1, 1.5 and 2 preferably captured EEG responses to speech signals in the delta band and 2) the theta neural band of speech neural responses can be captured by the AM stimuli of an exponent of 0.75. Our results suggest that the human auditory system shows specificity to the long-term modulation spectrum and is equipped with characteristic neural algorithms tailored to extract critical acoustic information from speech signals.
You may also like
Tracing the many paths of vision
January 23, 2021Max Planck Institute of Neurobiology
Networks for memory and learning
December 11, 2020Max Planck Institute of Psychiatry
Word contexts enhance the neural representation of...
December 11, 2020Max Planck Institute for Psycholinguistics
What social distancing does to a brain
December 2, 2020Max Planck Institute for Brain Research
From the inside out – how the brain forms sensory...
November 13, 2020Max Planck Institute for Brain Research
Looking does not mean seeing
November 10, 2020Max Planck Institute for Biological Cybernetics