IBM Mastor: Multilingual Automatic Speech-To-Speech Translator
Yuqing Gao
Bowen Zhou
Liang Gu
Sarikaya, R.
Hong-kwang Kuo
Rosti, A.-V.I.
Afify, M.
Weiihong Zhu
IBM Thomas J. Watson Res. Center, Yorktown Heights, NY;
Abstract
In this paper, we describe the IBM MASTOR systems which handle spontaneous free-form speech-to-speech translation on both laptop and hand-held PDAs. Challenges include speech recognition and machine translation in adverse environments, lack of data and linguistic resources for under-studied languages, and the need to rapidly develop capabilities for new languages. Importantly, the code and models must fit within the limited memory and computational resources of hand-held devices. 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)
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