A keyword-aware grammar framework for LVCSR-based spoken keyword search

In this paper, we proposed a method to realize the recently developed keyword-aware grammar for LVCSR-based keyword search using weight finite-state automata (WFSA). The approach creates a compact and deterministic grammar WFSA by inserting keyword paths to an existing n-gram WFSA. Tested on the evalpart1 data of the IARPA Babel OpenKWS13 Vietnamese and OpenKWS14 Tamil limited language pack tasks, the experimental results indicate the proposed keyword-aware framework achieves significant improvement, with about 50% relative actual term weighted value (ATWV) enhancement for both languages. Comparisons between the keyword-aware grammar and our previously proposed n-gram LM based approximation approach for the grammar also show that the KWS performances of these two realizations are complementary.