title: Analyzing ASR Pretraining for Low-Resource Speech-to-Text Translation
abstract: Previous work has shown that for low-resource source languages, automatic speech-to-text translation (AST) can be improved by pre-training an end-to-end model on automatic speech recognition (ASR) data from a high-resource language. However, it is not clear what factors - e.g., language relatedness or size of the pretraining data - yield the biggest improvements, or whether pretraining can be effectively combined with other methods such as data augmentation. Here, we experiment with pretraining 