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Trends in HearingNeurosciencePeer-reviewed journal article

Neural decoding of the speech envelope: Effects of intelligibility and spectral degradation

Alexis Deighton MacIntyreiD, Robert P. CarlyoniD, Tobias GoehringiD

PDFDOIPreprint

Abstract

During continuous speech perception, endogenous neural activity becomes time-locked to acoustic stimulus features, such as the speech amplitude envelope. This speech-brain coupling can be decoded using non-invasive brain imaging techniques, including electroencephalography (EEG). Neural decoding may provide clinical use as an objective measure of stimulus encoding by the brain - for example during cochlear implant (CI) listening, wherein the speech signal is severely spectrally degraded. Yet, interplay between acoustic and linguistic factors may lead to top-down modulation of perception, thereby complicating audiological applications. To address this ambiguity, we assess neural decoding of the speech envelope under spectral degradation with EEG in acoustically hearing listeners (n = 38; 18-35 years old) using vocoded speech. We dissociate sensory encoding from higher-order processing by employing intelligible (English) and non-intelligible (Dutch) stimuli, with auditory attention sustained using a repeated-phrase detection task. Subject-specific and group decoders were trained to reconstruct the speech envelope from held-out EEG data, with decoder significance determined via random permutation testing. Whereas speech envelope reconstruction did not vary by spectral resolution, intelligible speech was associated with better decoding accuracy in general. Results were similar across subject-specific and group analyses, with less consistent effects of spectral degradation in group decoding. Permutation tests revealed possible differences in decoder statistical significance by experimental condition. In general, while robust neural decoding was observed at the individual and group level, variability within participants would most likely prevent the clinical use of such a measure to differentiate levels of spectral degradation and intelligibility on an individual basis.

Citations
3
FWCI
0.73
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top 33%

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Alexis Deighton MacIntyre, Clément Gaultier, Tobias Goehring·Journal of Neural Engineering·Jan 20, 2026· 1 citations