Low-resource speech recognition and keyword search (KWS) are important topics for speech technologies. However, their performance often suffers from out-of-vocabulary (OOV) keywords. Subword units like syllables are useful in handling this issue. This report introduces a weighted finite state transducer (WFST) based syllable transduction framework for OOV handling in KWS. Syllable lattices are generated by performing syllable decoding and OOV keywords are entered into a pronunciation dictionary using a word-to-syllable pronunciation prediction system. Syllable lattices are then transduced into word lattices using both in-vocabulary word pronunciations and OOV pronunciations. Experiments on 5 languages provided by IARPA Babel project are presented, and it is shown that syllable transduction can effectively spot OOV keywords. Combination of this approach with two other OOV handling methods further improves keyword search performance.