Signal-to-Noise Ratio Tolerance in Self-Supervised Speech Representations Across Low-Resource Languages

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: How does the signal-to-noise ratio tolerance of self-supervised speech representations pre-trained on Flemish Dutch compare to those pre-trained on other low-resource languages when evaluated on the CommonVoice benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.