  Recent breakthroughs in NLP largely increased the presence of ASR systems in
our daily lives. However, for many low-resource languages, ASR models still
need to be improved due in part to the difficulty of acquiring pertinent data.
This project aims to help advance research in ASR models for Swiss German
dialects, by providing insights about the performance of state-of-the-art ASR
models on recently published Swiss German speech datasets. We propose a novel
loss that takes into account the semantic distance between the predicted and
the ground-truth labels. We outperform current state-of-the-art results by
fine-tuning OpenAI's Whisper model on Swiss-German datasets.
