K
Keiichiro Oura
Researcher at Nagoya Institute of Technology
Publications - 74
Citations - 1741
Keiichiro Oura is an academic researcher from Nagoya Institute of Technology. The author has contributed to research in topics: Speech synthesis & Hidden Markov model. The author has an hindex of 20, co-authored 73 publications receiving 1478 citations.
Papers
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Journal ArticleDOI
Speech Synthesis Based on Hidden Markov Models
TL;DR: This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech.
Recent development of the HMM-based singing voice synthesis system - Sinsy.
TL;DR: This paper introduces several techniques, i.e., trajectory training, a vibrato model, and a time-lag model, into the DNN-based singing voice synthesis system to synthesize the high quality singing voices.
Proceedings Article
Thousands of voices for HMM-based speech synthesis
Junichi Yamagishi,Bela Usabaev,Simon King,Oliver Watts,John Dines,Jilei Tian,Rile Hu,Yong Guan,Keiichiro Oura,Keiichi Tokuda,Reima Karhila,Mikko Kurimo +11 more
TL;DR: In this paper, a speaker-adaptive HMM-based speech synthesis system is proposed to produce high quality voices on non-TTS corpora such as ASR corpora.
Proceedings ArticleDOI
Singing voice synthesis based on deep neural networks
TL;DR: Subjective experimental results show that the DNN- based system outperformed the HMM-based system in terms of naturalness, and the relationship between the musical score and its acoustic features is modeled in frames by a DNN.
Journal ArticleDOI
Thousands of Voices for HMM-Based Speech Synthesis–Analysis and Application of TTS Systems Built on Various ASR Corpora
Junichi Yamagishi,Bela Usabaev,Simon King,Oliver Watts,John Dines,Jilei Tian,Yong Guan,Rile Hu,Keiichiro Oura,Yi-Jian Wu,Keiichi Tokuda,Reima Karhila,Mikko Kurimo +12 more
TL;DR: This paper demonstrates the thousands of voices for HMM-based speech synthesis that are made from several popular ASR corpora such as the Wall Street Journal, Resource Management, Globalphone, and SPEECON databases.