Fine-tuning Flemish Dutch Self-Supervised Speech Models for Low-Resource Germanic ASR

Recent research in speech processing exhibits a growing interest in unsupervised and self-supervised representation learning from unlabelled data to alleviate the need for large amounts of annotated data. We investigate several popular pre-training methods and apply them to Flemish Dutch. We compare off-the-shelf English pre-trained models to models trained on an increasing amount of Flemish data. We find that the most important factors for positive transfer to downstream speech recognition tasks include a substantial amount of data and a matching pre-training domain. Ideally, we also finetune

Research goal: What is the impact of fine-tuning a Flemish Dutch self-supervised speech model on a low-resource Germanic language with a distinct phonetic inventory compared to direct transfer from English pre-trained models, as evaluated on the CommonVoice ASR benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.9/10.