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Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.

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Summary

This study enhances speech emotion recognition (SER) by optimizing feature selection and data augmentation with a 1D convolutional neural network (CNN). The approach achieves high accuracy across multiple datasets, improving human-computer interaction applications.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Signal Processing

Background:

  • Speech emotion recognition (SER) is crucial for human-computer interaction (HCI) but faces challenges due to imbalanced data and feature selection.
  • Existing SER models often lack clarity on sufficient features and are limited to single-language datasets.

Purpose of the Study:

  • To investigate the impact of classification approaches, feature combinations, and data augmentation on speech emotion detection accuracy.
  • To identify the optimal combination of handcrafted features and data augmentation for robust SER.
  • To evaluate a 1D convolutional neural network (CNN) model for SER performance across multiple languages.

Main Methods:

  • Utilized a 1D CNN classification model for speech emotion detection.
  • Employed handcrafted features and data augmentation techniques.
  • Evaluated the model on multiple language datasets (BAVED, ANAD, SAVEE).

Main Results:

  • The 1D CNN model demonstrated superior performance compared to traditional machine learning methods.
  • Achieved high accuracy rates: 97.09% on BAVED, 96.44% on ANAD, and 83.33% on SAVEE.
  • The combination of discriminating features and data augmentation significantly improved detection accuracy.

Conclusions:

  • Optimized feature selection and data augmentation are critical for enhancing SER accuracy.
  • The proposed 1D CNN model offers an effective approach for cross-lingual speech emotion recognition.
  • This research contributes to more sophisticated and reliable HCI applications through improved emotion detection.