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...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Muhammad Azhar, Adeen Amjad, Muhammad Arman, Deshinta Arrova Dewi
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2026-05-01
Colecção:	Information
Assuntos:	affective computing multimodal emotion recognition Urdu language processing lightweight transformers edge computing public health
Acesso em linha:	https://www.mdpi.com/2078-2489/17/5/458
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!