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arXiv · 1904.04479

Who Needs Words? Lexicon-Free Speech Recognition

Tatiana Likhomanenko·Gabriel Synnaeve·Ronan Collobert
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Abstract

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.

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Tatiana Likhomanenko, Gabriel Synnaeve, Ronan Collobert. 2019-04-09. Who Needs Words? Lexicon-Free Speech Recognition. https://doi.org/10.21437/interspeech.2019-3107

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