Syllable based Lattice Transduction for Keyword Search

Low-resource speech recognition and keyword search (KWS) are important topics for speech technologies. However, their performance often suffers from out-ofvocabulary (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 1 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. 1Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Defense US Army Research Laboratory contract number W911NF-12-C-0014. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoD/ARL, or the U.S. Government.