title: CTC Regularization for Low-Resource Speech-to-Text Translation
abstract: The challenges of building speech-to-text translation (ST) systems (e.g., a relative lack of parallel speech-text data and robustness to noise in audio) are exacerbated for low-resource language pairs.In this work, we seek to improve low-resource ST by building on previous studies that regularize ST training with the connectionist temporal classification (CTC) loss.By systematically evaluating a diverse range of linguistic annotations as CTC labels across multiple auxiliary loss configurations, 