  Lexicon-free speech recognition naturally deals with the problem of
out-of-vocabulary (OOV) words. In this paper, we show that character-based
language models (LM) can perform as well as word-based LMs for speech
recognition, in word error rates (WER), even without restricting the decoding
to a lexicon. We study character-based LMs and show that convolutional LMs can
effectively leverage large (character) contexts, which is key for good speech
recognition performance downstream. We specifically show that the lexicon-free
decoding performance (WER) on utterances with OOV words using character-based
LMs is better than lexicon-based decoding, both with character or word-based
LMs.
