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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Published on: August 9, 2024

3D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms.

Noushin Hajarolasvadi¹, Hasan Demirel¹

¹Department of Electrical and Electronics Engineering, Eastern Mediterranean University, 99628 Gazimagusa, North Cyprus, via Mersin 10, Turkey.

Entropy (Basel, Switzerland)

|December 3, 2020

Summary

This study introduces a novel speech-based emotion recognition system using 3D Convolutional Neural Networks (CNNs). The method effectively identifies emotions from audio features and spectrograms, outperforming existing state-of-the-art approaches.

Keywords:

3D convolutional neural networks deep learning k-means clustering spectrograms speech emotion recognition

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Area of Science:

Artificial Intelligence
Speech Processing
Human-Computer Interaction

Background:

Emotion recognition is crucial for enhancing human-robot interaction.
Accurate emotion detection from speech remains a significant research challenge.

Purpose of the Study:

To propose an effective emotion recognition system utilizing speech signal analysis.
To improve the accuracy of emotion detection in human-robot interaction contexts.

Main Methods:

Speech signals are segmented into overlapping frames, and audio features (e.g., MFCC, pitch, intensity) are extracted.
Spectrograms are generated, and k-means clustering identifies keyframes to summarize signals.
A 3D Convolutional Neural Network (CNN) is trained on 3D tensors of keyframe spectrograms using 10-fold cross-validation.

Main Results:

The proposed 3D CNN model demonstrated superior performance compared to state-of-the-art methods.
Experiments were validated on diverse emotion databases: SAVEE, RML, and eNTERFACE'05.

Conclusions:

The developed speech-based emotion recognition system offers a promising approach for human-robot interaction.
The methodology effectively leverages audio features and deep learning for accurate emotion detection.