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Recurrent neural network language models (RNNLMs) have becoming increasingly popular in many applications such as automatic speech recognition (ASR). Significant performance improvements in both perplexity and word error rate over standard n-gram LMs have been widely reported on ASR tasks. In contrast, published research on using RNNLMs for keyword search systems has been relatively limited. In this paper the application of RNNLMs for the IARPA Babel keyword search task is investigated. Indoi:10.1109/icassp.2017.7953263 dblp:conf/icassp/ChenRVLKG17 fatcat:pxja34y3orea7nqrb5pd3trzie