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A keyword-sensitive language modeling framework for spoken keyword search (KWS) is proposed to combine the advantages of conventional keyword-filler based and large vocabulary continuous speech recognition (LVCSR) based KWS systems. The proposed framework allows keyword search systems to be flexible on keyword target settings as in the LVCSR-based keyword search. In low-resource scenarios it facilitates KWS with an ability to achieve high keyword detection accuracy as in the keyword-filler based systems and to attain a low false alarm rate inherent in the LVCSR-based systems. The proposed keyword-aware grammar is realized by incorporating keyword information to re-train and modify the language models used in LVCSR-based KWS. Experimental results, on the evalpart1 data of the IARPA Babel OpenKWS13 Vietnamese tasks, indicate that the proposed approach achieves a …