2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDHLT-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
SessionHLT-16: Applications in Natural Language
LocationGather.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
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.