  Homophone characters are common in tonal syllable-based languages, such as
Mandarin and Cantonese. The data-intensive end-to-end Automatic Speech
Recognition (ASR) systems are more likely to mis-recognize homophone characters
and rare words under low-resource settings. For the problem of lowresource
Cantonese speech recognition, this paper presents a novel homophone extension
method to integrate human knowledge of the homophone lexicon into the beam
search decoding process with language model re-scoring. Besides, we propose an
automatic unified writing method to merge the variants of Cantonese characters
and standardize speech annotation guidelines, which enables more efficient
utilization of labeled utterances by providing more samples for the merged
characters. We empirically show that both homophone extension and unified
writing improve the recognition performance significantly on both in-domain and
out-of-domain test sets, with an absolute Character Error Rate (CER) decrease
of around 5% and 18%.
