Subword-based modeling for handling OOV words inkeyword spotting
Yanzhang He, Brian Hutchinson, Peter Baumann, Mari Ostendorf, Eric Fosler‐Lussier, Janet B. Pierrehumbert · 2014
This work compares ASR decoding at different subword levels crossed with alternative keyword search strategies to handle the OOV issue for keyword spotting in the low-resource setting. We show that a morpheme-based subword modeling approach is effective in recovering OOV keywords within a Turkish low-resource keyword spotting task, where mixed word and morpheme decoding approach outperforms the traditional subword-based search from word-decoded lattices that are broken down to subword lattices. Furthermore, unsupervised learning of morphology works almost as well as a rule-based system designed for the language despite the low-resource condition. A staged keyword search strategy benefits from both methods of morphological analysis.