Multimodal neural pronunciation modeling for spoken languages with logographic origin (D18-1)
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| Challenge: | Graphemes of most languages encode pronunciation, though some are more explicit than others . pronunciation modeling in logographic languages requires decomposing logographs into subunits . |
| Approach: | They propose a multimodal approach to predict pronunciation of Cantonese logographic characters using neural networks. |
| Outcome: | The proposed framework improves performance by 18.1% and 25.0% respectively to unimodal and multimodal baselines. |
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