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Related Experiment Video

Updated: Aug 29, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

Objective speech intelligibility prediction using a deep learning model with continuous speech-evoked cortical

Youngmin Na¹, Hyosung Joo², Le Thi Trang²

¹Department of Biomedical Engineering, University of Ulsan, Ulsan, South Korea.

Frontiers in Neuroscience

|September 5, 2022

Summary

We developed a deep learning model using electroencephalography (EEG) to predict speech intelligibility. The PHENV model achieved 99.91% accuracy, offering a more objective measure for hearing aid users.

Keywords:

EEG continuous speech deep-learning occlusion sensitivity speech intelligibility

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Area of Science:

Neuroscience
Biomedical Engineering
Speech and Hearing Sciences

Background:

Auditory prostheses aid hearing-impaired individuals, but objective speech intelligibility assessment is challenging.
Current subjective methods limit precise evaluation of auditory prosthesis effectiveness.
Developing objective measures is crucial for optimizing hearing rehabilitation.

Purpose of the Study:

To predict speech intelligibility using electroencephalography (EEG) and a convolutional neural network.
To evaluate the performance of different speech features (ENV, PH, PHENV) in predicting speech intelligibility.
To identify informative brain regions for speech intelligibility prediction.

Main Methods:

Recorded 64-channel EEG from 87 participants under spectrally degraded speech conditions.
Extracted speech features: temporal envelope (ENV) and phoneme onset (PH).
Trained deep learning models using EEG data, event-related potentials (ERP), and feature correlations (PHENV).

Main Results:

The PHENV model achieved the highest speech intelligibility prediction accuracy at 99.91%.
ENV, PH, and PHENV models showed high prediction accuracies (99.42%, 99.55%, 99.91%).
Occlusion sensitivity analysis revealed distinct informative electrode locations for ENV and phoneme-based models.

Conclusions:

Deep learning models, particularly the PHENV model, can accurately predict speech intelligibility from EEG.
This approach offers a more objective and potentially comfortable alternative to subjective speech intelligibility tests.
The findings may advance the clinical assessment of auditory prosthesis efficacy.