  Despite recent technology advancements, the effectiveness of neural
approaches to end-to-end speech-to-text translation is still limited by the
paucity of publicly available training corpora. We tackle this limitation with
a method to improve data exploitation and boost the system's performance at
inference time. Our approach allows us to customize "on the fly" an existing
model to each incoming translation request. At its core, it exploits an
instance selection procedure to retrieve, from a given pool of data, a small
set of samples similar to the input query in terms of latent properties of its
audio signal. The retrieved samples are then used for an instance-specific
fine-tuning of the model. We evaluate our approach in three different
scenarios. In all data conditions (different languages, in/out-of-domain
adaptation), our instance-based adaptation yields coherent performance gains
over static models.
