Simple acoustic features can explain phoneme-based predictions of cortical responses to speech

Daube, C., Ince, R. A.A. and Gross, J. (2019) Simple acoustic features can explain phoneme-based predictions of cortical responses to speech. Current Biology, 29(12), 1924-1937.e9. (doi:10.1016/j.cub.2019.04.067) (PMID:31130454) (PMCID:PMC6584359)

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Abstract

When we listen to speech, we have to make sense of a waveform of sound pressure. Hierarchical models of speech perception assume that, to extract semantic meaning, the signal is transformed into unknown, intermediate neuronal representations. Traditionally, studies of such intermediate representations are guided by linguistically defined concepts, such as phonemes. Here, we argue that in order to arrive at an unbiased understanding of the neuronal responses to speech, we should focus instead on representations obtained directly from the stimulus. We illustrate our view with a data-driven, information theoretic analysis of a dataset of 24 young, healthy humans who listened to a 1 h narrative while their magnetoencephalogram (MEG) was recorded. We find that two recent results, the improved performance of an encoding model in which annotated linguistic and acoustic features were combined and the decoding of phoneme subgroups from phoneme-locked responses, can be explained by an encoding model that is based entirely on acoustic features. These acoustic features capitalize on acoustic edges and outperform Gabor-filtered spectrograms, which can explicitly describe the spectrotemporal characteristics of individual phonemes. By replicating our results in publicly available electroencephalography (EEG) data, we conclude that models of brain responses based on linguistic features can serve as excellent benchmarks. However, we believe that in order to further our understanding of human cortical responses to speech, we should also explore low-level and parsimonious explanations for apparent high-level phenomena.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Daube, Mr Christoph and Ince, Dr Robin and Gross, Professor Joachim
Authors: Daube, C., Ince, R. A.A., and Gross, J.
College/School:College of Medical Veterinary and Life Sciences > Institute of Neuroscience and Psychology
College of Science and Engineering > School of Psychology
Journal Name:Current Biology
Publisher:Elsevier (Cell Press)
ISSN:0960-9822
ISSN (Online):1879-0445
Published Online:23 May 2019
Copyright Holders:Copyright © 2019 The Authors
First Published:First published in Current Biology 29(12): 1924-1937.e9
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
3042400Beyond Pairwise Connectivity: developing an information theoretic hypergraph methodology for multi-modal resting state neuroimaging analysisRobin InceWellcome Trust (WELLCOTR)214120/Z/18/ZNP - Centre for Cognitive Neuroimaging (CCNi)
597051Natural and modulated neural communication: State-dependent decoding and driving of human Brain Oscillations.Joachim GrossWellcome Trust (WELLCOTR)098433/Z/12/ZINP - CENTRE FOR COGNITIVE NEUROIMAGING