MeetDot: Videoconferencing with Live Translation Captions

Autor:	Arkhangorodsky, Arkady, Chu, Christopher, Fang, Scot, Huang, Yiqi, Jiang, Denglin, Nagesh, Ajay, Zhang, Boliang, Knight, Kevin
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes. Comment: 7 pages, 4 figures, Accepted as EMNLP 2021 demo paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2109.09577 Zobrazit plný text záznamu View this record from Arxiv