Using word burst analysis to rescore keyword search candidates on low-resource languages

For low-resource languages, keyword search (KWS) remains challenging due to the lack of training data. This work aims to bolster KWS performance in low-resource languages by incorporating word burst information into the decision process. We find that this information can improve performance when we focus analysis on particularly problematic KWS candidates: low-scoring correct hits, and high-scoring false alarms.