| Duration prediction using multiple Gaussian process experts for GPR-based speech synthesis D Moungsri, T Koriyama, T Kobayashi 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 10 | 2017 |
| HMM-based Thai speech synthesis using unsupervised stress context labeling D Moungsri, T Koriyama, T Kobayashi Signal and Information Processing Association Annual Summit and Conference …, 2014 | 10 | 2014 |
| Unsupervised Stress Information Labeling Using Gaussian Process Latent Variable Model for Statistical Speech Synthesis. D Moungsri, T Koriyama, T Kobayashi Interspeech, 1517-1521, 2016 | 8 | 2016 |
| Duration prediction using multi-level model for GPR-based speech synthesis. D Moungsri, T Koriyama, T Kobayashi INTERSPEECH, 1591-1595, 2015 | 7 | 2015 |
| Tone modeling using stress information for HMM-based Thai speech synthesis D Moungsri, T Koriyama, T Nose, T Kobayashi Proc. Speech Prosody 7, 1057-1061, 2014 | 5 | 2014 |
| Tone modeling using Gaussian process latent variable model for statistical speech synthesis D Moungsri, T Koriyama, T Kobayashi Proc. Speech Prosody 8, 1014-1018, 2016 | 3 | 2016 |
| GPR-based Thai speech synthesis using multi-level duration prediction D Moungsri, T Koriyama, T Kobayashi Speech Communication 99, 114-123, 2018 | 2 | 2018 |
| Prosody Modeling Based on Gaussian Process Regression for Thai Speech Synthesis D Moungsri 東京工業大学, 2018 | | 2018 |
| Enhanced F0 generation for GPR-based speech synthesis considering syllable-based prosodic features D Moungsri, T Koriyama, T Kobayashi 2017 Asia-Pacific Signal and Information Processing Association Annual …, 2017 | | 2017 |
