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Related Experiment Video

Updated: Sep 1, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Multimodal explainable AI predicts upcoming speech behavior in adults who stutter.

Arun Das1,2, Jeffrey Mock3, Farzan Irani4

  • 1Secure AI and Autonomy Laboratory, University of Texas at San Antonio, San Antonio, TX, United States.

Frontiers in Neuroscience
|August 18, 2022
PubMed
Summary

This study used electroencephalography (EEG) and facial muscle activity to predict stuttering in adults who stutter (AWS). The multimodal AI model achieved 80.8% accuracy, identifying brain-body dynamics related to speech behavior.

Keywords:
EEGdeep learningdisfluencyfacial expressionmachine learningmultimodalself-supervisedstuttering

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Area of Science:

  • Cognitive Neuroscience
  • Neuroscience
  • Artificial Intelligence

Background:

  • Understanding the relationship between dynamic brain activity and behavior is a key goal in cognitive neuroscience.
  • Brain activity can be measured directly (e.g., EEG) or indirectly through bodily signals like facial muscle movements.
  • Previous AI research indicates both EEG and facial muscles encode information related to various cognitive and emotional states.

Purpose of the Study:

  • To investigate the relationship between dynamic brain activity (EEG) and facial muscle activity in adults who stutter (AWS).
  • To predict future speech behavior (fluent vs. stuttered) using multimodal data during speech preparation.
  • To explore brain-behavior dynamics in AWS, who naturally fluctuate between fluent and stuttered speech.

Main Methods:

  • Utilized electroencephalography (EEG) and facial muscle activity recorded via video during speech preparation in AWS.
  • Employed an explainable self-supervised multimodal architecture to analyze temporal dynamics of EEG and facial muscle data.
  • Trained the model to predict whether upcoming speech would be fluent or stuttered on a trial-by-trial basis.

Main Results:

  • The multimodal architecture achieved 80.8% accuracy in predicting fluent or stuttered speech (chance=50%).
  • Identified specific EEG and facial muscle signals that differentiate fluent from stuttered speech trials.
  • Observed systematic variations in these signals across early and late speech preparation periods.

Conclusions:

  • Dynamic brain and facial muscle activity patterns can predict upcoming speech behavior in AWS.
  • The self-supervised multimodal approach effectively captured predictive brain-body dynamics.
  • This methodology holds potential for understanding neural mechanisms in various neurological/psychiatric disorders and developing brain-state estimation technologies.