ArticleCurrent biology : CB2019
Simple Acoustic Features Can Explain Phoneme-Based Predictions of Cortical Responses to Speech.
Article in Current biology : CB, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers.
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Who cites it
82 citing papers in PubMed.
- Article
- Article
- Infant Cortical Tracking of Speech Shows Emerging Spatial Release from Masking in the First Year of Life.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Dynamic acoustic-to-categorical representations of phonemes and prosody along ventral and dorsal speech streams.Nature communications · 2026Article
- Do Not Forget the Stimulus: A Missing Control in Naturalistic Studies of Neural Entrainment.eNeuro · 2026Article
- Speech Neurophysiology in Realistic Contexts: Big Hype or Big Leap?The European journal of neuroscience · 2026Review
- Neural Tracking of Sustained Attention, Attention Switching, and Natural Conversation in Audiovisual Environments Using Wearable EEG.The European journal of neuroscience · 2026Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.Nature communications · 2026Article
- Linguistic structure and language familiarity sharpen phoneme encoding in the brain.Communications biology · 2026Article
- Infant cortical tracking of speech shows emerging spatial release from masking in the first year of life.bioRxiv : the preprint server for biology · 2026Article
- Invariant correlation: a general and straightforward method to quantify invariance in time-varying neural signals.Proceedings of the ... Conference on Cognitive Computational Neuroscience · 2026Article
- ALPARC: artificial languages with phonological and acoustic rhythmicity controls.Frontiers in psychology · 2026Article
- Acoustic parameter combinations underlying mapping of pseudoword sounds to multiple domains of meaning: Representational similarity analyses and machine-learning models.The Journal of the Acoustical Society of America · 2025Article
- Broadband synergy versus oscillatory redundancy in the visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Article
- A listening advantage for native speech is reflected by attention-related activity in auditory cortex.Communications biology · 2025Article
- Article
- Quantifying the diverse contributions of hierarchical muscle interactions to motor function.iScience · 2025Article
- Language-specific neural dynamics extend syntax into the time domain.PLoS biology · 2025Article
- Measuring self-similarity in empirical signals to understand musical beat perception.The European journal of neuroscience · 2025Article
22 more citing papers are in PubMed but not listed here.
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3 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.