Improving noisy student training for low-resource languages in End-to-End ASR using CycleGAN and inter-domain losses
Journal: arXiv (Cornell University)
Year: 2024

Training a semi-supervised end-to-end speech recognition system using noisy student training has significantly improved performance. However, this approach requires a substantial amount of paired speech-text and unlabeled speech, which is costly for low-resource languages. Therefore, this paper considers a more extreme case of semi-supervised end-to-end automatic speech recognition where there are limited paired speech-text, unlabeled speech (less than five hours), and abundant external text. Fi