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Related Experiment Video

Updated: May 3, 2026

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Speech-based system for detecting alcohol intoxication using optimized deep learning.

S Abirami1, V Vasudevan2, S Dhanasekaran3

  • 1Department of Computer Applications, Kalasalingam Academy of Research and Education, Krishnankovil, Tamil Nadu, India. abiramikare@gmail.com.

Psychopharmacology
|May 1, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning system for detecting alcohol intoxication using speech. The novel Dense Residual Recurrent Network (D2R_Net) achieves high accuracy, offering a promising tool for public safety.

Keywords:
Alcohol intoxicationDense residual recurrent networkIterative parrot optimizationPublic safety

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Area of Science:

  • Artificial Intelligence
  • Biomedical Engineering
  • Speech Processing

Background:

  • Alcohol intoxication significantly impacts speech patterns.
  • Accurate, non-invasive detection methods are crucial for public safety and healthcare.
  • Existing alcohol detection methods have limitations in real-time application and invasiveness.

Purpose of the Study:

  • To develop a highly accurate speech-based system for detecting alcohol intoxication.
  • To leverage deep learning for classifying speech as sober or intoxicated.
  • To optimize the system for efficiency and real-world deployment.

Main Methods:

  • Utilized a Dense Residual Recurrent Network (D2R_Net) combining dense residual blocks and Gated Recurrent Units (GRUs).
  • Employed Iterative Parrot Optimization (ItPaO) for hyperparameter tuning.
  • Pre-processed speech data into log-Mel spectrograms to capture alcohol-altered auditory patterns.

Main Results:

  • The proposed system achieved a balanced accuracy of 98.51% and specificity of 98.2%.
  • Outperformed existing speech-based alcohol detection models in extensive experiments.
  • Demonstrated high efficiency and robustness with minimal computational resource requirements.

Conclusions:

  • The developed speech-based system offers a highly accurate and non-invasive method for alcohol intoxication detection.
  • The D2R_Net architecture and ItPaO optimization provide a scalable and cost-effective solution.
  • This technology has significant potential for revolutionizing public safety and healthcare applications.