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Training Autoregressive Speech Recognition Models with Limited in-domain Supervision
Advances in self-supervised learning have significantly reduced the amount of transcribed audio required for training.
Combining Unsupervised and Text Augmented Semi-Supervised Learning for Low Resourced Autoregressive Speech Recognition
Final performance is an additional 2% better absolute when using CTC-based decoding for semi-supervised training compared to shallow fusion.
Overcoming Domain Mismatch in Low Resource Sequence-to-Sequence ASR Models using Hybrid Generated Pseudotranscripts
We show that there is a sizable initial gap in such a data condition between hybrid and seq2seq models, and the hybrid model is able to further improve through the use of additional language model (LM) data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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