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syllable decoding and lattice generation. Out-of-vocabulary (OOV) keyword pronunciations are produced using a grapheme-to-syllable (G2S) system and then used to construct a lexical transducer. The lexical transducer is then composed with a keyword-boosted language model (LM) to transduce the 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
We propose a simple but effective weighted finite state transducer (WFST) based framework for handling out-of-vocabulary (OOV) keywords in a speech search task. State-of-the-art large vocabulary continuous speech recognition (LVCSR) and keyword search (KWS) systems are developed for conversational telephone speech in
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