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Joint Unsupervised and Supervised Training for Multilingual ASR
[article]
2021
arXiv
pre-print
Self-supervised training has shown promising gains in pretraining models and facilitating the downstream finetuning for speech recognition, like multilingual ASR. Most existing methods adopt a 2-stage scheme where the self-supervised loss is optimized in the first pretraining stage, and the standard supervised finetuning resumes in the second stage. In this paper, we propose an end-to-end (E2E) Joint Unsupervised and Supervised Training (JUST) method to combine the supervised RNN-T loss and the
arXiv:2111.08137v1
fatcat:xd2nhyl6ozed7acgc6lm2njx24