Neurodevelopmental oscillatory basis of speech processing in noise

Abstract

Humans' extraordinary ability to understand speech in noise relies on multiple processes that develop with age. Using magnetoencephalography (MEG), we characterize the underlying neuromaturational basis by quantifying how cortical oscillations in 144 participants (aged 5-27 years) track phrasal and syllabic structures in connected speech mixed with different types of noise. While the extraction of prosodic cues from clear speech was stable during development, its maintenance in a multi-talker background matured rapidly up to age 9 and was associated with speech comprehension. Furthermore, while the extraction of subtler information provided by syllables matured at age 9, its maintenance in noisy backgrounds progressively matured until adulthood. Altogether, these results highlight distinct behaviorally relevant maturational trajectories for the neuronal signatures of speech perception. In accordance with grain-size proposals, neuromaturational milestones are reached increasingly late for linguistic units of decreasing size, with further delays incurred by noise.

Keywords: Audiovisual speech integration; Cortical tracking of speech (CTS); Development; Magnetoencephalography (MEG); Speech-in-noise (SiN) perception.

Fig. 1

Illustration of the time-course of a video stimulus. Videos lasted approximately 6 min and were divided into 10 blocks to which experimental conditions were assigned. There were two blocks of the noiseless condition, and eight blocks of speech-in-noise conditions: one block for each possible combination of the four types of noise and the two types of visual display.

Fig. 2

Dependence on age of syllabic CTS in the noiseless condition. Dashed red lines indicate the beginning and the end of the maturation process.

Fig. 3

Impact of the main effects on nCTS at phrasal (A) and syllabic rates (B). Mean and SEM (Standard Error of the Mean) values are displayed as a function of noise properties. The four traces correspond to conditions with (connected traces) and without (dashed traces) visual speech (VS), within the left (blue traces) and right (red traces) hemispheres. nCTS values are bounded between –1 and 1, with values below 0 indicating lower CTS in speech-in-noise conditions than in noiseless conditions.

Fig. 4

Dependence on age of phrasal (A) and syllabic (B) nCTS. nCTS was pooled across conditions with and without visual speech, and across least- and most-energetic conditions. Phrasal nCTS was further pooled across hemispheres. Dashed red lines indicate the beginning and end of the maturation process.

Fig. 5

Impact of the noise condition and of the presence or absence of concomitant visual speech on the speech comprehension scores, pooled across age groups. Vertical bars indicate SEM values.

Fig. 6

Behavioral relevance of phrasal nCTS in babble noise. The speech comprehension scores and phrasal nCTS values were corrected for their nonlinear dependence on age. Presented values are positive when the initial values were above those of the fitted model and negative otherwise.

Fig. 7

Sources of phrasal (A) and syllabic (B) CTS in the left and right hemispheres. The overlays present the mean CTS values across all conditions and participants (regardless of age). Values at MNI coordinates |X|> 25 mm were projected orthogonally onto the parasagittal slice of coordinates |X| = 50 mm. The location of each significant source of CTS in each condition and age group is indicated with a white star (with the same projection scheme).

Fig. 8

Sources of CTS color-coded for age group (A, phrasal; C, syllabic) and for the informational property of the noise (B, phrasal); the other property being shape-coded.