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Speech synthesizers are optimized for fluent natural text. However, in a speech to speech translation system, they have to process machine translation output, which is often not fluent. Rendering machine translations as speech makes them even harder to understand than the synthesis of natural text. A speech synthesizer must deal with the disfluencies in translations in order to be comprehensible and communicate the content. In this paper, we explore three synthesis strategies that address different problems found in translation output. By carrying out listening tasks and measuring transcription accuracies, we find that these methods can make the synthesis of translations more intelligible.