Design and Implementation of a Natural Processing Language Model to Solve Language Barriers in the Healthcare Sector

Student: Faith Omowumi Ogunleye (Project, 2025)
Department of Computer and Information Science
Bamidele Olumilua University of Edu. Science and Tech. Ikere Ekiti, Ekiti State

Abstract

This project presents the design and implementation of a Natural Language Processing (NLP) model developed to overcome language barriers in Nigeria’s healthcare sector. The system integrates speech recognition and machine translation to facilitate communication between Yoruba-speaking patients and English-speaking healthcare providers. Using transformer-based architectures such as Whisper and Helsinki, the model accurately converts Yoruba speech into English text. The research demonstrates that NLP can significantly enhance diagnosis accuracy, treatment adherence, and patient satisfaction in multilingual healthcare environments. This solution promotes inclusivity, improves healthcare accessibility, and contributes to the advancement of artificial intelligence applications for low-resource languages in Africa.

Keywords
NLP Healthcare Language Barriers Yoruba Speech Recognition Translation AI Machine Learning Nigeria Communication Transformer Models Whisper Helsinki
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