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A Methodical Framework Utilizing Transforms and Biomimetic Intelligence-Based Optimization with Machine Learning for

Sunil Kumar Prabhakar1, Dong-Ok Won1

  • 1Department of Artificial Intelligence Convergence, Chuncheon 24252, Republic of Korea.

Biomimetics (Basel, Switzerland)
|September 27, 2024
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Summary
This summary is machine-generated.

This study enhances speech emotion recognition (SER) by applying novel transforms and optimization techniques. The Chirplet transform combined with the Chameleon Swarm Algorithm and Twin Extreme Learning Machine achieved high accuracy in classifying emotions from speech.

Keywords:
ELMSERclassificationfeature selectiontransforms

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Area of Science:

  • Artificial Intelligence
  • Signal Processing
  • Machine Learning

Background:

  • Speech emotion recognition (SER) is crucial for human-computer interaction, medical applications, and entertainment.
  • Current SER methods require advanced feature extraction and selection for accurate emotional state judgment.
  • Integrating diverse signal processing transforms with intelligent optimization is key to advancing SER.

Purpose of the Study:

  • To explore the efficacy of six advanced signal transforms for speech emotion recognition.
  • To investigate the performance of feature selection techniques, including Overlapping Information Feature Selection (OIFS) and biomimetic algorithms (Harris Hawks Optimization and Chameleon Swarm Algorithm).
  • To evaluate the classification accuracy of various machine learning models, particularly the Extreme Learning Machine (ELM) and Twin Extreme Learning Machine (TELM).

Main Methods:

  • Applied six transforms: synchrosqueezing, fractional Stockwell (FST), K-sine transform-dependent integrated system (KSTDIS), flexible analytic wavelet (FAWT), chirplet, and superlet transforms to speech signals.
  • Utilized Overlapping Information Feature Selection (OIFS), Harris Hawks Optimization (HHO), and Chameleon Swarm Algorithm (CSA) for feature selection.
  • Classified extracted features using ten machine learning classifiers, focusing on ELM and TELM, across four datasets (EMOVO, RAVDESS, SAVEE, Berlin Emo-DB).

Main Results:

  • The Chirplet transform with CSA and TELM achieved 80.63% accuracy on the EMOVO dataset.
  • The FAWT transform with HHO and TELM reached 85.76% accuracy on the RAVDESS dataset.
  • The Chirplet transform with OIFS and TELM obtained 83.94% accuracy on the SAVEE dataset.
  • The KSTDIS transform with CSA and TELM demonstrated 89.77% accuracy on the Berlin Emo-DB dataset.

Conclusions:

  • The combination of advanced transforms, intelligent feature selection, and ELM/TELM classifiers significantly improves speech emotion recognition accuracy.
  • Biomimetic optimization algorithms like CSA and HHO show strong potential in enhancing feature selection for SER tasks.
  • The study validates the effectiveness of proposed methods across multiple benchmark datasets, offering a robust approach for SER systems.