The document discusses various methods and technologies related to voice conversion, citing multiple research papers from 2007 to 2019. It highlights advancements in techniques such as maximum-likelihood estimation and cycle-consistent adversarial networks. Notable implementations and frameworks, including StarGAN for voice conversion and Amazon Sumerian, are also referenced.

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35. 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/

36. , 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

37. 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