  Graphemes of most languages encode pronunciation, though some are more
explicit than others. Languages like Spanish have a straightforward mapping
between its graphemes and phonemes, while this mapping is more convoluted for
languages like English. Spoken languages such as Cantonese present even more
challenges in pronunciation modeling: (1) they do not have a standard written
form, (2) the closest graphemic origins are logographic Han characters, of
which only a subset of these logographic characters implicitly encodes
pronunciation. In this work, we propose a multimodal approach to predict the
pronunciation of Cantonese logographic characters, using neural networks with a
geometric representation of logographs and pronunciation of cognates in
historically related languages. The proposed framework improves performance by
18.1% and 25.0% respective to unimodal and multimodal baselines.
