Non-Parametric Domain Adaptation for End-to-End Speech Translation. (arXiv:2205.11211v5 [cs.CL] UPDATED)

End-to-End Speech Translation (E2E-ST) has received increasing attention due
to the potential of its less error propagation, lower latency, and fewer
parameters. However, the effectiveness of neural-based approaches to this task
is severely limited by the available training corpus, especially for domain
adaptation where in-domain triplet training data is scarce or nonexistent. In
this paper, we propose a novel non-parametric method that leverages
domain-specific text translation corpus to achieve domain adaptation for the
E2E-ST system. To this end, we first …

arxiv domain adaptation non-parametric parametric speech translation