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メディアが運用すべき持続可能なVTuberをつくる技術

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メディアが運用すべき持続可能なVTuberをつくる技術

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PyCon JP 2019 (2019/09/17) @Hirosaji
https://pycon.jp/2019/

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Title (English): VTuber technology for media company with high operational sustainability

PyCon JP 2019 (2019/09/17) @Hirosaji
https://pycon.jp/2019/

=====
Title (English): VTuber technology for media company with high operational sustainability

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メディアが運用すべき持続可能なVTuberをつくる技術

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  3. 3. AI https://note.mu/nikkei_jisedai/n/n4fc1cd3fd9b5 https://github.com/Hirosaji/toy-codes/blob/master/handle_wav/forPyCon/ analyze_and_synthesize_wav.py hujinsen / StarGAN Voice Conversion https://github.com/hujinsen/StarGAN-Voice-Conversion Learn Amazon Sumerian https://docs.sumerian.amazonaws.com/
  4. 4. , 2017 , 2018 DNN , 2019 , 1-10-4. 2019 Toda, T., Black, A. W. and Tokuda, K.: Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory, IEEE T. ASLP. 2007
  5. 5. Takuhiro, K. and Hirokazu, K.: Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks, arXiv. 2017 Fuming, F., Junichi Y., Isao, E., and Jaime, L. T.: High-quality nonparallel voice conversion based on cycle-consistent adversarial network, ICASSP. 2018 Kameoka, H., Kaneko, T., Tanaka, K., Hojo, N. StarGAN-VC: Non-parallel many-to-many voice conversion using star generative adversarial networks., In 2018 IEEE Spoken Language Technology Workshop (SLT), pp. 266-273, IEEE. 2018

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