Utilizing Enhanced Particle Swarm Optimization for Feature Selection in Gender-Emotion Detection From English Speech Signals

Utilizing Enhanced Particle Swarm Optimization for Feature Selection in Gender-Emotion Detection From English Speech Signals

Speech emotion recognition (SER) plays a vital role in various applications, enabling machines to decode and analyze emotions conveyed through speech. This study introduces a novel approach, Dynamic Gender-Aware Enhanced Binary Particle Swarm Optimization (DGA-EBPSO), that leverages a gender-specifi...

Full description

Saved in:
Bibliographic Details
Main Authors: Ammar Amjad, Li-Chia Tai, Hsien-Tsung Chang
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Dynamic gender-aware enhanced binary particle swarm optimization (DGA-EPSO)
emotion detection
emotional expression variations
feature selection
gender prediction
hybrid mutation strategy
Online Access:https://ieeexplore.ieee.org/document/10798436/
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://ieeexplore.ieee.org/document/10798436/

Similar Items