GM-OT, a fused Wasserstein and Gromov-Wasserstein graph-matching alignment for BERT-to-acoustic knowledge transfer, reports 3.98% CER on AISHELL-1 test versus 5.76% for a Conformer+CTC baseline.
Improving CTC-Based Speech Recognition Via Knowl- edge Transferring from Pre-Trained Language Models,
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Cross-modal Knowledge Transfer Learning as Graph Matching Based on Optimal Transport for ASR
GM-OT, a fused Wasserstein and Gromov-Wasserstein graph-matching alignment for BERT-to-acoustic knowledge transfer, reports 3.98% CER on AISHELL-1 test versus 5.76% for a Conformer+CTC baseline.