JVS corpus: free Japanese multi-speaker voice corpus S Takamichi, K Mitsui, Y Saito, T Koriyama, N Tanji, H Saruwatari arXiv preprint arXiv:1908.06248, 2019 | 63 | 2019 |
Statistical parametric speech synthesis based on Gaussian process regression T Koriyama, T Nose, T Kobayashi IEEE Journal of Selected Topics in Signal Processing 8 (2), 173-183, 2013 | 58 | 2013 |
Utmos: Utokyo-sarulab system for voicemos challenge 2022 T Saeki, D Xin, W Nakata, T Koriyama, S Takamichi, H Saruwatari arXiv preprint arXiv:2204.02152, 2022 | 55 | 2022 |
JSUT and JVS: Free Japanese voice corpora for accelerating speech synthesis research S Takamichi, R Sonobe, K Mitsui, Y Saito, T Koriyama, N Tanji, ... Acoustical Science and Technology 41 (5), 761-768, 2020 | 53 | 2020 |
Speech emotion recognition using convolutional long short-term memory neural network and support vector machines N Kurpukdee, T Koriyama, T Kobayashi, S Kasuriya, C Wutiwiwatchai, ... 2017 Asia-Pacific Signal and Information Processing Association Annual …, 2017 | 34 | 2017 |
Statistical parametric speech synthesis using deep Gaussian processes T Koriyama, T Kobayashi IEEE/ACM Transactions on Audio, Speech, and Language Processing 27 (5), 948-959, 2019 | 27 | 2019 |
On the use of extended context for HMM-based spontaneous conversational speech synthesis T Koriyama, T Nose, T Kobayashi Twelfth Annual Conference of the International Speech Communication Association, 2011 | 23 | 2011 |
Sampling-based speech parameter generation using moment-matching networks S Takamichi, T Koriyama, H Saruwatari arXiv preprint arXiv:1704.03626, 2017 | 22 | 2017 |
HMM-based expressive singing voice synthesis with singing style control and robust pitch modeling T Nose, M Kanemoto, T Koriyama, T Kobayashi Computer Speech & Language 34 (1), 308-322, 2015 | 22 | 2015 |
Cross-Lingual Text-To-Speech Synthesis via Domain Adaptation and Perceptual Similarity Regression in Speaker Space. D Xin, Y Saito, S Takamichi, T Koriyama, H Saruwatari Interspeech, 2947-2951, 2020 | 21 | 2020 |
Prosody generation using frame-based Gaussian process regression and classification for statistical parametric speech synthesis T Koriyama, T Kobayashi 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 15 | 2015 |
Parametric speech synthesis based on Gaussian process regression using global variance and hyperparameter optimization T Koriyama, T Nose, T Kobayashi 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 15 | 2014 |
Prosodic variation enhancement using unsupervised context labeling for HMM-based expressive speech synthesis Y Maeno, T Nose, T Kobayashi, T Koriyama, Y Ijima, H Nakajima, ... Speech Communication 57, 144-154, 2014 | 15 | 2014 |
Conversational spontaneous speech synthesis using average voice model. T Koriyama, T Nose, T Kobayashi INTERSPEECH, 853-856, 2010 | 15 | 2010 |
Audiobook speech synthesis conditioned by cross-sentence context-aware word embeddings W Nakata, T Koriyama, S Takamichi, N Tanji, Y Ijima, R Masumura, ... 11th ISCA Speech Synthesis Workshop (SSW 11), 211-215, 2021 | 14 | 2021 |
A comparison of speech synthesis systems based on GPR, HMM, and DNN with a small amount of training data. T Koriyama, T Kobayashi INTERSPEECH, 3496-3500, 2015 | 14 | 2015 |
Cross-Lingual Speaker Adaptation Using Domain Adaptation and Speaker Consistency Loss for Text-To-Speech Synthesis. D Xin, Y Saito, S Takamichi, T Koriyama, H Saruwatari Interspeech, 1614-1618, 2021 | 11 | 2021 |
Statistical nonparametric speech synthesis using sparse Gaussian T Koriyama, T Nose, T Kobayashi The Journal of Machine Learning Research 6, 1939-1959, 2005 | 11 | 2005 |
An introduction of Gaussian processes and deep Gaussian processes and their applications to speech processing T Koriyama Acoustical Science and Technology 41 (2), 457-464, 2020 | 10 | 2020 |
Semi-Supervised Prosody Modeling Using Deep Gaussian Process Latent Variable Model. T Koriyama, T Kobayashi INTERSPEECH, 4450-4454, 2019 | 10 | 2019 |