Syllable based keyword search: Transducing syllable lattices to word lattices

This paper presents a weighted finite state transducer (WFST) based syllable decoding and transduction framework for keyword search (KWS). Acoustic context dependent phone models are trained from word forced alignments. Then syllable decoding is done with lattices generated using a syllable lexicon and language model (LM). To process out-of-vocabulary (OOV) keywords, pronunciations are produced using a grapheme-to-syllable (G2S) system. A syllable to word lexical transducer containing both in-vocabulary (IV) and OOV keywords is then constructed and composed with a keyword-boosted LM transducer. The composed transducer is then used to transduce syllable lattices to word lattices for final KWS. We show that our method can effectively perform KWS on both IV and OOV keywords, and yields up to 0.03 Actual Term-Weighted Value (ATWV) improvement over searching keywords directly in subword lattices. Word Error Rates (WER) and KWS results are reported for three different languages.