Subword-based modeling for handling OOV words inkeyword spotting

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

Subword-based modeling for handling OOV words inkeyword spotting

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