How does the scalability of M2S-AVSR compare to contrastive audio-visual representation learning methods (e.g.

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 the scalability of M2S-AVSR compare to contrastive audio-visual representation learning methods (e.g., AV-Contrast) in terms of throughput and accuracy on the AVSpeech benchmark when trained on varying dataset sizes?

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