HK-LegiCoST: Leveraging Non-Verbatim Transcripts for Speech Translation
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
HENT-SRT: Hierarchical Efficient Neural Transducer with Self-Distillation for Joint Speech Recognition and Translation
Languages in Multilingual Speech Foundation Models Align Both Phonetically and Semantically
KIT's Low-resource Speech Translation Systems for IWSLT2025: System Enhancement with Synthetic Data and Model Regularization
Explainability of Speech Recognition Transformers via Gradient-based Attention Visualization
TASK AWARE MULTI-TASK LEARNING FOR SPEECH TO TEXT TASKS
토론
이 논문과 연결된 포럼 글이 아직 없습니다.