Combining Autoregressive Models and Phonological Knowledge Bases for Improved Accuracy in Korean Grapheme-to-Phoneme Conversion
Recent advances in deep learning have highlighted the importance of Grapheme-to-Phoneme (G2P) conversion in natural language processing and speech synthesis. Korean exhibits complex phonological changes such as liaison, initial sound law, and consonant assimilation, making it challenging to handle a...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11045935/ |
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