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Towards reconstructing intelligible speech from the human auditory cortex.

Hassan Akbari1,2, Bahar Khalighinejad1,2, Jose L Herrero3,4

  • 1Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States.

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Researchers improved speech reconstruction from brain activity using deep learning and speech synthesis. This advances brain-computer interfaces (BCI) for communication restoration in paralyzed individuals and enhances human-computer interaction.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Auditory stimulus reconstruction decodes neural activity into acoustic signals.
  • Speech neuroprosthetics aim for direct brain-computer communication.
  • Current methods yield low-quality speech, limiting brain-computer interface (BCI) utility.

Purpose of the Study:

  • To enhance speech intelligibility for neuroprosthetic applications.
  • To investigate deep learning and speech synthesis for improved auditory cortex speech reconstruction.
  • To compare reconstruction accuracy using different regression methods and acoustic targets.

Main Methods:

  • Utilized deep neural networks (DNNs) and linear regression for speech reconstruction.
  • Compared reconstruction accuracy using auditory spectrograms versus speech synthesis parameters.
  • Evaluated performance across different neural frequency ranges.

Main Results:

  • A DNN model estimating speech synthesizer parameters from all neural frequencies achieved superior results.
  • Intelligibility improved by 65% compared to baseline linear regression methods.
  • The DNN approach demonstrated higher subjective and objective scores in a digit recognition task.

Conclusions:

  • Deep learning and speech synthesis significantly improve speech reconstruction from neural activity.
  • This advancement holds promise for next-generation speech BCI systems.
  • Potential applications include restoring communication for paralyzed patients and transforming human-computer interaction.