Romanian Speech-to-Text Transcription for Medical Applications

Speech recognition models have an important role in improving efficiency and accessibility across various industries. Due to its advantages and benefits, this technology has transformed sectors such as healthcare, automotive, and customer service. However, the speech recognition in low-resource languages such as Romanian remains relatively underdeveloped due to the lack of data for training deep learning models. For this reason it cannot be utilized in the healthcare industry where Romanian specialized terms are unknown to the speech-to-text models. To address this problem, we propose to fine-tune large speech recognition models on a specialized dataset with Romanian audio and medical discourse. We obtain this dataset in a semi-automatic manner by lever-aging speech recognition and large language models. By adapting state-of-the-art fine-tuning approaches that are used for LLMs, we improve the performance of the model on Romanian medical discourse by 19.9% in substitution errors, 28% in deletion errors and 9 % in word error rate. This approach paves the way for speech recognition technology usage across many Romanian industries.