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