Impact of Fine-Tuning Cross-Lingual English Models on Flemish Dutch Data versus Training from Scratch for LibriSpeech Performance

Abstract

Abstract: 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 Question

How does fine-tuning the same cross-lingual English pre-trained model on different amounts of Flemish Dutch data affect its performance on the LibriSpeech test set compared to models trained from scratch on equivalent amounts of Flemish Dutch data?

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