| Paper ID | HLT-16.4 |
| Paper Title |
NN-KOG2P: A NOVEL GRAPHEME-TO-PHONEME MODEL FOR KOREAN LANGUAGE |
| Authors |
Hwa-Yeon Kim, Jong-Hwan Kim, Jae-Min Kim, Naver Corporation, South Korea |
| Session | HLT-16: Applications in Natural Language |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Human Language Technology: [HLT-MLMD] Machine Learning Methods for Language |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
With the development of text-to-speech technology, high- quality voices can be heard in AI speaker responses, car navigation guidance, and news article-reading services. As services become more diverse, domains are expanded, requiring fast and high-performance grapheme-to-phoneme (G2P) technology. In this paper, we propose a novel Korean G2P model architecture, reflecting the characteristics of Korean pronunciation, called neural network-based Korean G2P (NN-KoG2P). Our proposed method achieves high accuracy in an open-domain dataset and a fast inference speed that can generate pronunciation sequences in real-time services. |
