Before revision:  Compared to prior work on semi-supervised and unsupervised approaches, slimIPL not only simplifies the training process, but also achieves competitive and state-of-the-art results on LibriSpeech test sets in both standard and low-resource settings .
After revision:  Compared to prior work on semi-supervised and unsupervised approaches, slimIPL not only simplifies the training process, but also achieves competitive and state-of-the-art results on LibriSpeech test sets in both standard and low-resource settings with CTC-based models. slimIPL features a dynamic cache for pseudo-labels which reduces sensitivity to changes in relabeling hyperparameters and results in improves training stability. slimIPL is also highly-efficient and requires 3.5-4x fewer computational resources to converge than other state-of-the-art semi/self-supervised approaches. With only 10 hours of labeled audio, slimIPL is competitive with self-supervised approaches, and is state-of-the-art with 100 hours of labeled audio without the use of a language model both at test time and during pseudo-label generation .