IBM MASTOR system
Yuqing Gao, Wei Zhang, Laurent Besacier, Liang Gu, Bowen Zhou, Ruhi Sarikaya, Mohamed Afify, Hong-Kwang Jeff Kuo, Wei-zhong Zhu, Yonggang Deng, Charles Prosser · 2006
In this paper, we describe the IBM MASTOR, a speech-to-speech translation system that can translate spontaneous free-form speech in real-time on both laptop and hand-held PDAs. Challenges include speech recognition and machine translation in adverse environments, lack of training data and linguistic resources for under-studied languages, and the need to rapidly develop capabilities for new languages. Another challenge is designing algorithms and building models in a scalable manner to perform well even on memory and CPU deficient hand-held computers. We describe our approaches, experience, and success in building working free-form S2S systems that can handle two language pairs (including a low-resource language).