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Pain Level Classification from Speech Using GRU-Mixer Architecture with Log-Mel Spectrogram Features.

Adi Alhudhaif1

  • 1Department of Computer Science, College of Computer Engineering and Sciences in Al-kharj, Prince Sattam Bin Abdulaziz University, P.O. Box 151, Al-Kharj 11942, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces the Gated Recurrent Unit (GRU)-Mixer, a deep learning model for automatic pain detection from speech. It achieves high accuracy in classifying pain levels, offering a promising tool for non-invasive patient assessment.

Keywords:
GRU-MixerLog-Mel spectrogramspain level determinationspeech signals

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Area of Science:

  • Computational linguistics
  • Affective computing
  • Machine learning for healthcare

Background:

  • Automatic pain detection from speech offers non-invasive, real-time assessment, crucial for patients unable to self-report.
  • Existing methods require further development for robust clinical application.

Purpose of the Study:

  • To introduce and evaluate the Gated Recurrent Unit (GRU)-Mixer, a novel deep learning model for speech-based pain classification.
  • To establish a benchmark for future research in affective computing and pain recognition.

Main Methods:

  • A lightweight recurrent deep learning model (GRU-Mixer) was developed, processing Log-Mel spectrograms from speech.
  • The model utilizes stacked bidirectional GRUs and adaptive average pooling for temporal feature extraction.
  • Speaker-independent training with class-balanced loss was employed for generalization across binary, graded intensity, and thermal-state pain classification tasks.

Main Results:

  • The GRU-Mixer achieved 83.86% accuracy for binary pain detection (pain vs. non-pain).
  • Multiclass pain intensity classification (mild, moderate, severe) reached 75.36% accuracy.
  • The model demonstrated strong performance on the TAME Pain dataset.

Conclusions:

  • The GRU-Mixer presents an effective benchmark architecture for speech-based pain recognition.
  • This study is the first deep learning classification work on the TAME Pain dataset.
  • The findings support the potential of AI in objective pain assessment through vocal biomarkers.