Long-Form End-to-End Speech Translation via Latent Alignment
  Segmentation
Peter Polák Ondřej Bojar
Current simultaneous speech translation models can process audio only up to a
few seconds long. Contemporary datasets provide an oracle segmentation into
sentences based on human-annotated transcripts and translations. However, the
segmentation into sentences is not available in the real world. Current speech
segmentation approaches either offer poor segmentation quality or have to trade
latency for quality. In this paper, we propose a novel segmentation approach
for a low-latency end-to-end speech translation. We leverage the existing
speech translation encoder-decoder architecture with ST CTC and show that it
can perform the segmentation task without supervision or additional parameters.
To the best of our knowledge, our method is the first that allows an actual
end-to-end simultaneous speech translation, as the same model is used for
translation and segmentation at the same time. On a diverse set of language
pairs and in- and out-of-domain data, we show that the proposed approach
achieves state-of-the-art quality at no additional computational cost.