Ethiopian Languages Speech-to-Speech Translation System for the Health Sector Using Large Language Models (LLMs)

Rahel Mekonnen with Hiwot Mekonnen of the Ethiopian Artificial Intelligence Institute in Ethiopia will develop a multilingual speech-to-speech translation system to remove language barriers in communication between healthcare providers and their patients. This system would use three of the local languages: Amharic, Afan Oromo, and Somali, making healthcare less dependent on the availability of translators, which is a challenge due to the multiple languages spoken in Ethiopia, and enabling direct and open communication with patients without the need for an intermediary. They will customize an existing LLM by training it with medical text data and transcribed audio data from anonymized doctor-patient conversations, including text-to-speech model training using text read by language experts. They will create both a mobile phone-based translation application for patients and a web-based application for physicians.