  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 incorporate an additional encoder into the
pre-trained E2E-ST model to realize text translation modelling, and then unify
the decoder's output representation for text and speech translation tasks by
reducing the correspondent representation mismatch in available triplet
training data. During domain adaptation, a k-nearest-neighbor (kNN) classifier
is introduced to produce the final translation distribution using the external
datastore built by the domain-specific text translation corpus, while the
universal output representation is adopted to perform a similarity search.
Experiments on the Europarl-ST benchmark demonstrate that when in-domain text
translation data is involved only, our proposed approach significantly improves
baseline by 12.82 BLEU on average in all translation directions, even
outperforming the strong in-domain fine-tuning method.
