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Recent breakthroughs in NLP largely increased the presence of ASR systems inour daily lives. However, for many low-resource languages, ASR models stillneed 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 Germandialects, by providing insights about the performance of state-of-the-art ASRmodels on recently published Swiss German speech datasets. We propose a novelloss that takes into account the semantic distance between the predicted andthe ground-truth labels. We outperform current state-of-the-art results byfine-tuning OpenAI's Whisper model on Swiss-German datasets.

Spaiche: Extending State-of-the-Art ASR Models to Swiss German Dialects