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

Who Needs Words? Lexicon-Free Speech Recognition