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Non-Parametric Domain Adaptation for End-to-End Speech Translation. (arXiv:2205.11211v2 [cs.CL] UPDATED)
May 26, 2022, 1:12 a.m. | Yichao Du, Weizhi Wang, Zhirui Zhang, Boxing Chen, Tong Xu, Jun Xie, Enhong Chen
cs.CL updates on arXiv.org arxiv.org
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
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