A novel keyword+LVCSR-filler based grammar network representation for spoken keyword search

A novel spoken keyword search grammar representation framework is proposed to combine the advantages of conventional keyword-filler based keyword search (KWS) and the LVCSR-based KWS systems. The proposed grammar representation allows keyword search systems to be flexible on keyword target settings as in the LVCSR-based keyword search. In low-resource scenarios it also provides the system with the ability to achieve high keyword detection accuracies as in the keyword-filler based KWS systems and to attain a low false alarm rate inherent in the LVCSR-based KWS systems. In this paper the proposed grammar is realized in three ways by modifying the language models used in LVCSR-based KWS. Tested on the evalpart1 data of the IARPA Babel OpenKWS13 Vietnamese tasks, experimental results indicate that the combined approaches achieve a significant ATWV improvement of more than 50% relatively (from 0.2093 to 0.3287) on the limited-language-pack task, while a 20% relative ATWV improvement (from 0.4578 to 0.5486) is observed on the full-language-pack task.