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Emotional Speech Recognition Method Based on Word Transcription.

Gulmira Bekmanova1, Banu Yergesh1, Altynbek Sharipbay1

  • 1Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Nur-Sultan 010008, Kazakhstan.

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Summary

This study introduces an emotional speech recognition method to detect student emotions during online exams. The technique accurately identifies emotions in speech using a word-based knowledge base, proving effective for various applications.

Keywords:
affective computingartificial intelligencecrowd emotion recognitiondistance learninge-learningemotion recognitionspeech recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • The COVID-19 pandemic necessitated remote learning, increasing the need for tools to monitor student engagement and emotional states during online assessments.
  • Traditional speech recognition systems often lack the capability to interpret emotional nuances within spoken language.

Purpose of the Study:

  • To develop and evaluate an emotional speech recognition method for identifying student emotions during online exams.
  • To assess the accuracy and applicability of the proposed method across different languages and contexts.

Main Methods:

  • A novel emotional speech recognition approach utilizing a knowledge base of emotionally charged words (codebook).
  • Speech signal processing involving signal capture, speech detection, simplified transcription, word boundary determination, and comparison with the codebook.
  • Experimental validation using 420 audio recordings to assess method performance.

Main Results:

  • The method achieved an accuracy of 79.7% for the Kazakh language in recognizing emotions within spoken speech.
  • Demonstrated effectiveness in identifying positive and negative emotions, with complete word recognition when emotions are present.
  • The method is computationally inexpensive, allowing for widespread application.

Conclusions:

  • The developed emotional speech recognition method is effective and accurate, particularly for the Kazakh language.
  • The technique's low computational demands facilitate its broad deployment in diverse environments like schools and public spaces.
  • Future applications include developing systems for real-time threat detection based on emotional speech analysis.