title: How Important is a Language Model for Low-resource ASR?
abstract: N-gram language models (LMs) are the innovation that first made large-vocabulary continuous automatic speech recognition (ASR) viable.With neural end-to-end ASR architectures, however, LMs have become an afterthought.While the effect on accuracy may be negligible for English and Mandarin, jettisoning the LM might not make sense for the world's remaining 6000+ languages.In this paper, we investigate the role of the LM in low-resource ASR.First we ask: does using an n-gram LM in decoding in neural