Linguistics and Languages

Combining predictive coding and neural oscillations enables online syllable recognition in natural speech

S. Hovsepyan, I. Olasagasti, et al.

This innovative research by Sevada Hovsepyan, Itsaso Olasagasti, and Anne-Lise Giraud investigates how predictive coding and neural oscillations enhance our ability to recognize syllables in natural speech. The developed computational model reveals the remarkable alignment of internal predictions and acoustic inputs, showcasing the dynamic interplay vital for effective sensory processing.

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Abstract

This paper explores the combined effects of predictive coding and neural oscillations on online syllable recognition in natural speech. A computational model, Process, was developed to identify syllable sequences in continuous speech by using predictions from internal spectro-temporal representations and theta oscillations to signal syllable onsets and durations. The model demonstrates that online syllable identification works best when theta-gamma coupling temporally aligns spectro-temporal predictions with acoustic input, highlighting the interplay between predictive coding and neural oscillations in dynamic sensory processing.

Publisher

Nature Communications

Published On

Jun 19, 2020

Authors

Sevada Hovsepyan, Itsaso Olasagasti, Anne-Lise Giraud

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