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Related Experiment Video

Updated: Jan 14, 2026

Infant Auditory Processing and Event-related Brain Oscillations
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Neuro-oscillatory models of cortical speech processing.

Olesia Dogonasheva1, Anne-Lise Giraud1, Denis Zakharov2

  • 1Université Paris Cité, Institut Pasteur, AP-HP, Inserm, Fondation Pour l'Audition, Institut de l'Audition, IHU reConnect, Paris, F-75012, France.

Neural Networks : the Official Journal of the International Neural Network Society
|October 27, 2025
PubMed
Summary
This summary is machine-generated.

Computational models using neural oscillations (gamma, theta, delta) show promise for real-time speech perception, mimicking human brain functions for better speech recognition. Further research is needed to address semantic complexity and computational demands.

Keywords:
Auditory cortexComputational modelingNeuronal modelsOscillationsSpeech processing

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Speech Processing

Background:

  • Neural oscillations, including gamma, theta, and delta waves, play a crucial role in auditory processing.
  • Understanding these rhythmic brain activities is key to deciphering speech perception mechanisms.
  • Current speech recognition models lack the real-time processing capabilities of the human brain.

Purpose of the Study:

  • To review computational models of neural oscillations in speech perception.
  • To analyze how rhythmic brain activities encode speech elements from phonemes to meaning.
  • To explore the potential of these models for real-time speech understanding and recognition.

Main Methods:

  • Review of computational models focusing on neural oscillations (gamma, theta, delta).
  • Analysis of mechanisms for encoding phonemes, syllables, and words.
  • Evaluation of biological plausibility and real-time processing applications.

Main Results:

  • Models demonstrate how neural oscillations contribute to speech segmentation and meaning inference.
  • Neural oscillation models offer insights into real-time speech processing, a gap in current AI.
  • Biological plausibility and computational demands are key considerations for model development.

Conclusions:

  • Computational models of neural oscillations provide valuable insights into speech perception.
  • Challenges include modeling semantic complexity and contextual integration.
  • Future research should focus on enhancing model realism and addressing computational challenges for improved speech recognition.