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Multimodal Emotion Detection in Low-Resource Languages Using Lightweight Transformer Architectures: A Dual-Level Fusion Framework Integrating DistilBERT, CNN-BiGRU, and MobileViT for Efficient Real-Time Urdu Affective Computing

This paper addresses emotion recognition in low-resource language settings for healthcare and human-computer interaction (HCI). Most existing multimodal systems rely on resource-intensive transformers or high-resource languages, limiting their applicability to low-resource languages like Urdu. We pr...

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Main Authors: Muhammad Azhar, Adeen Amjad, Muhammad Arman, Deshinta Arrova Dewi
Format: Artigo
Language:Inglês
Published: MDPI AG 2026-05-01
Series:Information
Subjects:
affective computing
multimodal emotion recognition
Urdu language processing
lightweight transformers
edge computing
public health
Online Access:https://www.mdpi.com/2078-2489/17/5/458
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