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Out-of-vocabulary (OOV) words can pose a particular problem for automatic speech recognition (ASR) of broadcast news. The language models (LMs) of ASR systems are typically trained on static corpora, whereas new words (particularly new proper nouns) are continually introduced in the media. Additionally, such OOVs are often content-rich proper nouns that are vital to understanding the topic. In this work, we explore methods for dynamically adding OOVs to language models by adapting the n-gramdoi:10.1109/slt.2016.7846299 dblp:conf/slt/CurreyIF16 fatcat:dsvzlwkn6zh5pj3giuxmpf7m24