Bibliographie complète

Automatic Speech Recognition (ASR) has advanced considerably in recent years, largely fueled by breakthroughs in self-supervised learning and the increasing availability of large-scale multilingual speech corpora. Models such as Whisper have demonstrated remarkable performance across numerous languages. However, despite its strong multilingual coverage, performance remains highly variable across languages. In this study, we fine-tune Whisper in four regional languages spoken in France, each selected to represent distinct linguistic and resource conditions: Basque, Occitan, Alsatian, and Shimaore. Performance is increased by fine-tuning for each language, but to significantly varying degrees. This is due to a combination of varying data quantity and quality, language similarity to an already well-covered language, and transcription standardisation: all things that low-resource languages may lack.