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This paper investigates the use of language identification (LID) in real-time speech-to-speech translation systems. We propose a framework that incorporates LID capability into a speech-tospeech translation system while minimizing the impact on the systems real-time performance. We compared two phone-based LID approaches, namely PRLM and PPRLM, to a proposed extended approach based on Conditional Random Field classifiers. The performances of these three approaches were evaluated to identify the input language in the CMU English-Iraqi TransTAC system, and the proposed approach obtained significantly higher classification accuracies on two of the three test sets evaluated.