Paper: Segmentation Strategies for Streaming Speech Translation

ACL ID N13-1023
Title Segmentation Strategies for Streaming Speech Translation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2013

The study presented in this work is a first ef- fort at real-time speech translation of TED talks, a compendium of public talks with dif- ferent speakers addressing a variety of top- ics. We address the goal of achieving a sys- tem that balances translation accuracy and la- tency. In order to improve ASR performance for our diverse data set, adaptation techniques such as constrained model adaptation and vo- cal tract length normalization are found to be useful. In order to improve machine transla- tion (MT) performance, techniques that could be employed in real-time such as monotonic and partial translation retention are found to be of use. We also experiment with inserting text segmenters of various types between ASR and MT in a series of real-time translation ex- periments. Among other r...