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Related Experiment Video

Updated: Nov 14, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Data-driven machine learning models for decoding speech categorization from evoked brain responses.

Md Sultan Mahmud1,2, Mohammed Yeasin1,2, Gavin M Bidelman2,3,4

  • 1Department of Electrical and Computer Engineering, University of Memphis, 3815 Central Avenue, Memphis, TN 38152, United States of America.

Journal of Neural Engineering
|March 10, 2021
PubMed
Summary
This summary is machine-generated.

The human brain categorizes speech sounds early, around 120 ms, using a fronto-temporal-parietal network. This categorical perception (CP) is more pronounced in the left hemisphere for processing speech acoustics.

Keywords:
auditory event-related potentials (ERPs)behavioral slopecategorical perceptiondecision processmachine learningstability selectionsupport vector machine (SVM)

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Area of Science:

  • Neuroscience
  • Auditory Perception
  • Speech Processing

Background:

  • Categorical perception (CP) is crucial for understanding speech despite acoustic variability.
  • Investigating the neural basis of CP for speech sounds is essential for understanding auditory processing.

Purpose of the Study:

  • To investigate the spatiotemporal characteristics of auditory neural activity reflecting CP for speech.
  • To differentiate neural activity for phonetic prototypes versus ambiguous speech sounds.

Main Methods:

  • Recorded 64-channel electroencephalograms (EEGs) during rapid vowel sound classification.
  • Utilized support vector machine classifiers and stability selection for source-level analysis of event-related potentials.
  • Determined decoding accuracy and timing of CP across brain regions.

Main Results:

  • Early whole-brain data (120 ms) decoded speech categories with high accuracy (95.16%).
  • Left hemisphere decoding was earlier and more accurate than the right hemisphere.
  • Specific brain regions, including auditory cortex and IFG, showed categorical representation during encoding and decision stages.

Conclusions:

  • Abstract speech categories emerge early (∼120 ms) in auditory processing.
  • A compact fronto-temporal-parietal network dominates early speech categorization.
  • Neural activity reflects CP, with later stages linked to individual differences in categorical hearing.