HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
Cihan Xiao Henry Li Xinyuan Jinyi Yang Dongji Gao Matthew Wiesner Kevin Duh Sanjeev Khudanpur
We introduce HK-LegiCoST, a new three-way parallel corpus of
Cantonese-English translations, containing 600+ hours of Cantonese audio, its
standard traditional Chinese transcript, and English translation, segmented and
aligned at the sentence level. We describe the notable challenges in corpus
preparation: segmentation, alignment of long audio recordings, and
sentence-level alignment with non-verbatim transcripts. Such transcripts make
the corpus suitable for speech translation research when there are significant
differences between the spoken and written forms of the source language. Due to
its large size, we are able to demonstrate competitive speech translation
baselines on HK-LegiCoST and extend them to promising cross-corpus results on
the FLEURS Cantonese subset. These results deliver insights into speech
recognition and translation research in languages for which non-verbatim or
``noisy'' transcription is common due to various factors, including vernacular
and dialectal speech.