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Speech emotion recognition based on improved masking EMD and convolutional recurrent neural network.

Congshan Sun1, Haifeng Li1, Lin Ma1

  • 1Faculty of Computing, Harbin Institute of Technology, Harbin, China.

Frontiers in Psychology
|January 26, 2023
PubMed
Summary

This study introduces IMEMD-CRNN, a novel framework for speech emotion recognition (SER). It effectively overcomes limitations of traditional methods, achieving state-of-the-art accuracy in identifying emotions from speech.

Keywords:
bidirectional gated recurrent unitsconvolutional neural networksempirical mode decompositionmode mixingspeech emotion recognition

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Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Computational Linguistics

Background:

  • Speech emotion recognition (SER) is crucial for human-computer interaction but challenged by the complex, nonlinear nature of emotional speech.
  • Empirical Mode Decomposition (EMD) is used for analyzing emotional speech, yet suffers from mode mixing, noise, and computational inefficiency.
  • Existing EMD improvements struggle with adaptive parameter setting and persistent issues like mode mixing and noise.

Purpose of the Study:

  • To propose a novel SER framework, IMEMD-CRNN, that addresses the limitations of existing EMD-based methods.
  • To introduce an improved Empirical Mode Decomposition (IMEMD) method for more effective speech signal decomposition.
  • To enhance the accuracy and efficiency of emotion recognition from speech signals.

Main Methods:

  • Developed an Improved Masking signal-based Empirical Mode Decomposition (IMEMD) method to decompose speech signals, mitigating mode mixing and noise.
  • Extracted 43-dimensional time-frequency features from the intrinsic mode functions (IMFs) generated by IMEMD.
  • Utilized a Convolutional Recurrent Neural Network (CRNN) with 2D CNN and Bidirectional Gated Recurrent Units (BiGRU) for emotion classification.

Main Results:

  • The IMEMD method effectively decomposes speech, producing less noisy intrinsic mode functions (IMFs) with improved physical meaning and efficiency.
  • The IMEMD-CRNN framework achieved 100% accuracy on the TESS dataset and 93.54% accuracy on the Emo-DB dataset for seven-emotion recognition.
  • Demonstrated significant improvements in speech emotion recognition performance compared to existing methods.

Conclusions:

  • The proposed IMEMD-CRNN framework offers a robust and efficient solution for speech emotion recognition.
  • IMEMD successfully addresses the mode mixing problem in EMD, leading to more meaningful signal decomposition.
  • This research advances the field of human-computer emotion interaction through improved SER capabilities.