ArabEmoNet: A Lightweight Hybrid 2D CNN-BiLSTM Model with Attention for Robust Arabic Speech Emotion Recognition
Year: 2025
Type: conference-paper
Abstract: Speech emotion recognition is vital for humancomputer interaction, particularly for lowresource languages like Arabic, which face challenges due to limited data and research.We introduce ArabEmoNet, a lightweight architecture designed to overcome these limitations and deliver state-of-the-art performance.Unlike previous systems relying on discrete MFCC features and 1D convolutions, which miss nuanced spectro-temporal patterns, ArabEmoNet uses Mel spectrograms processed through 2D convolutions, preserving critical emotional cues often lost in traditional methods.While recent models favor large-scale architectures with millions of parameters, ArabEmoNet achieves superior results with just 1 million parameters, which is 90 times smaller than HuBERT base and 74 times smaller than Whisper.This efficiency makes it ideal for resource-constrained environments.ArabEmoNet advances Arabic speech emotion recognition, offering exceptional performance and accessibility for real-world applications.
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4416037653