Tunable keyword-aware language modeling and context dependent fillers for LVCSR-based spoken keyword search

We explore the potential of using keyword-aware language modeling to extend the ability of trading higher false alarm rates in exchange for lower miss detection rates in LVCSRbased keyword search (KWS). A context-dependent keyword language modeling method is also proposed to further enhance the keyword-aware language modeling framework by reducing the number of false alarms often sacrificed in order to achieve the desirable low miss detection rates. We demonstrate that by using keyword-aware language modeling, a KWS system is able to achieve different operating points (misses vs. false alarms) by tuning a parameter in language modeling. We observe a relative gain of 20% in actual term weighted value (ATWV) performance with the keyword-aware KWS systems over the conventional LVCSR-based KWS systems when testing on the English Switchboard data. Moreover the proposed context-dependent keyword language modeling could further achieve a 9% relative ATWV improvement over the original keyword-aware KWS systems for single-word keywords which cause the most false alarms.