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Enhancing Speech Emotion Recognition Using Dual Feature Extraction Encoders.

Ilkhomjon Pulatov1, Rashid Oteniyazov2, Fazliddin Makhmudov1

  • 1Department of Computer Engineering, Gachon University, Seongnam 13120, Republic of Korea.

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|July 29, 2023
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Summary
This summary is machine-generated.

This study introduces a novel speech emotion recognition framework using spectrograms and semantic features. The system achieved high accuracy, outperforming existing models on benchmark datasets.

Keywords:
CNNLSTMMFCCfeature extractionspectrogramspeech emotion recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Speech Processing

Background:

  • Accurate speech emotion recognition is vital for human-computer interaction.
  • Existing methods have limitations in performance and precision.
  • Developing robust emotion detection from speech is an ongoing challenge.

Purpose of the Study:

  • To develop an innovative framework for speech emotion recognition (SER).
  • To improve SER performance by addressing inadequacies in current methodologies.
  • To enhance the accuracy and efficacy of emotion interpretation from human speech.

Main Methods:

  • Utilized a wholly convolutional neural network for speech spectrogram transcription.
  • Employed Mel-frequency cepstral coefficient (MFCC) feature extraction integrated with Speech2Vec for semantic encoding.
  • Processed dual features through a long short-term memory (LSTM) network and a fully connected layer.

Main Results:

  • Achieved 94.8% accuracy on the RAVDESS dataset.
  • Achieved 94.0% accuracy on the EMO-DB dataset.
  • Demonstrated superior performance compared to established SER models.

Conclusions:

  • The proposed framework significantly enhances speech emotion recognition accuracy.
  • The combined approach of spectrogram and semantic features proves effective.
  • This system offers a more sophisticated and efficient solution for SER.