Long-form Simultaneous Speech Translation: Thesis Proposal
Peter Polák
Simultaneous speech translation (SST) aims to provide real-time translation
of spoken language, even before the speaker finishes their sentence.
Traditionally, SST has been addressed primarily by cascaded systems that
decompose the task into subtasks, including speech recognition, segmentation,
and machine translation. However, the advent of deep learning has sparked
significant interest in end-to-end (E2E) systems. Nevertheless, a major
limitation of most approaches to E2E SST reported in the current literature is
that they assume that the source speech is pre-segmented into sentences, which
is a significant obstacle for practical, real-world applications. This thesis
proposal addresses end-to-end simultaneous speech translation, particularly in
the long-form setting, i.e., without pre-segmentation. We present a survey of
the latest advancements in E2E SST, assess the primary obstacles in SST and its
relevance to long-form scenarios, and suggest approaches to tackle these
challenges.