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Related Experiment Video

Updated: Jun 15, 2025

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Speech Emotion Recognition Using openSMILE and GPT 3.5 Transformer.

Darius Turcian1, Vasile Stoicu-Tivadar1

  • 1Politehnica University Timişoara, Department of Automation and Applied Informatics, Timişoara, Romania.

Studies in Health Technology and Informatics
|August 23, 2024
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Summary
This summary is machine-generated.

This study introduces a novel method for detecting emotions in Romanian speech using AI and machine learning (ML). Combining semantic and acoustic analysis achieved 74% accuracy, paving the way for mental health research.

Keywords:
GPT 3.5Machine LearningSpeech Emotion RecognitionopenSMILE

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Area of Science:

  • Artificial Intelligence
  • Speech Emotion Recognition
  • Computational Linguistics

Background:

  • Machine learning (ML) has significantly advanced, improving emotion recognition in speech.
  • Romanian speech emotion detection presents unique challenges and opportunities.

Purpose of the Study:

  • To propose and evaluate a novel method for emotion detection in Romanian speech.
  • To combine semantic analysis (GPT Transformer) and acoustic analysis (openSMILE) for enhanced accuracy.

Main Methods:

  • Utilized GPT Transformer for semantic analysis of speech.
  • Employed openSMILE toolkit for acoustic feature extraction.
  • Integrated both approaches for a hybrid emotion detection system.

Main Results:

  • Achieved an overall accuracy of 74% for emotion detection.
  • Obtained a precision rate of approximately 82%.
  • Identified dataset limitations (size, quality) impacting performance.

Conclusions:

  • The hybrid approach shows promise for Romanian speech emotion recognition.
  • Future research can leverage emotion analysis for identifying mental health disorders.
  • Dataset enhancement is crucial for improving system robustness and accuracy.