Using semantic analysis to improve speech recognition performance
Computer Speech and Language
IEEE Transactions on Audio, Speech, and Language Processing
An outlook on design technologies for future integrated systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Unsupervised data processing for classifier-based speech translator
Computer Speech and Language
BBN TransTalk: Robust multilingual two-way speech-to-speech translation for mobile platforms
Computer Speech and Language
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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).