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.
However, aligning representations betwee n linguistic and acoustic modalities remains a challenge due to inherent modality gaps
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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.