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Most end-to-end speech recognition systems model text directly as a sequenceof characters or sub-words. Current approaches to sub-word extraction onlyconsider character sequence frequencies, which at times produce inferiorsub-word segmentation that might lead to erroneous speech recognition output.We propose pronunciation-assisted sub-word modeling (PASM), a sub-wordextraction method that leverages the pronunciation information of a word.Experiments show that the proposed method can greatly improve upon thecharacter-based baseline, and also outperform commonly used byte-pair encodingmethods.

Improving End-to-end Speech Recognition with Pronunciation-assisted Sub-word Modeling