Impact of Data Augmentation on Robustness of Self-Supervised Flemish Dutch Speech Recognition Models Against Adversarial and

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

What is the impact of data augmentation techniques on the robustness of self-supervised pre-trained models for Flemish Dutch speech recognition when evaluated against adversarial noise and real-world acoustic conditions?

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