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The proposed method aims to accurately recognize emotions in Gujarati audio by capturing sound features related to emotion. Further, created our own dataset to study the model correctly, aiming to accurately recognize emotions in Gujarati audio. Evaluation of different Gujarati audio datasets measures how well emotions are recognized using metrics like accuracy and recall. This study improves how we recognize emotions in languages that aren’t studied as much. Importantly, it involves feature extraction tailored to the nuances of the Gujarati language. Ultimately, the process culminates in creating a CSV file documenting each sound’s associated emotion, providing valuable insights for further analysis and application development.

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Emotion Recognition in Gujarati Audio Using Feature Extraction for Machine Learning Models

  • 会议论文

  • 发表日期：

    2025年

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  • Anjum Mansuri,
  • Krupa Mehta,
  • Zeba Zaveri

摘要

The proposed method aims to accurately recognize emotions in Gujarati audio by capturing sound features related to emotion. Further, created our own dataset to study the model correctly, aiming to accurately recognize emotions in Gujarati audio. Evaluation of different Gujarati audio datasets measures how well emotions are recognized using metrics like accuracy and recall. This study improves how we recognize emotions in languages that aren’t studied as much. Importantly, it involves feature extraction tailored to the nuances of the Gujarati language. Ultimately, the process culminates in creating a CSV file documenting each sound’s associated emotion, providing valuable insights for further analysis and application development.