Language identification for speech-to-speech translation

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-to-speech translation system while minimizing the impact on the system’s 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.