Dynamic Time-Locking Mechanism in the Cortical Representation of Spoken Words.

ENeuro
Anni NoraRiitta Salmelin

Abstract

Human speech has a unique capacity to carry and communicate rich meanings. However, it is not known how the highly dynamic and variable perceptual signal is mapped to existing linguistic and semantic representations. In this novel approach, we used the natural acoustic variability of sounds and mapped them to magnetoencephalography (MEG) data using physiologically-inspired machine-learning models. We aimed at determining how well the models, differing in their representation of temporal information, serve to decode and reconstruct spoken words from MEG recordings in 16 healthy volunteers. We discovered that dynamic time-locking of the cortical activation to the unfolding speech input is crucial for the encoding of the acoustic-phonetic features of speech. In contrast, time-locking was not highlighted in cortical processing of non-speech environmental sounds that conveyed the same meanings as the spoken words, including human-made sounds with temporal modulation content similar to speech. The amplitude envelope of the spoken words was particularly well reconstructed based on cortical evoked responses. Our results indicate that speech is encoded cortically with especially high temporal fidelity. This speech tracking by evoked res...Continue Reading

Citations

Oct 27, 2021·The European Journal of Neuroscience·Hanna RenvallRiitta Salmelin

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Software Mentioned

NSL
MATLAB
MaxFilter Neuromag
Adobe Audition
Freesurfer
MNE Suite
Elekta Neuromag Xplotter
Praat

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