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arXiv · 1809.04203

Multimodal neural pronunciation modeling for spoken languages with logographic origin

Minh Nguyen·Gia H. Ngo·Nancy F. Chen
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Abstract

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.

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Minh Nguyen, Gia H. Ngo, Nancy F. Chen. 2018-09-12. Multimodal neural pronunciation modeling for spoken languages with logographic origin. https://arxiv.org/abs/1809.04203

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