Speech naturalness improvement via $$\mathrm {\epsilon }$$ ϵ -closed extended vectors sets in voice conversion systems

Author:

Jannati Mohammad Javad,Sayadiyan Abolghasem,Razi Abolfazl

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing,Software

Reference40 articles.

1. Abe, M., Nakamura, S., Shikano, K., & Kuwabara, H. (1988). Voice conversion through vector quantization. In International conference on acoustics, speech, and signal processing, ICASSP-88 (Vol. 1, pp. 655–658).

2. Charlier, M., Ohtani, Y., Toda, T., Moinet, A., & Dutoit, T. (2009). Cross-language voice conversion based on eigenvoices. In 10th Annual conference of the international speech communication association, Brighton, UK, September 6–10, pp. 1635–1638.

3. Chen, L., Ling, Z., Liu, L., & Dai, L. (2014). Voice conversion using deep neural networks with layer-wise generative training. IEEE ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1859–1872.

4. Chen, X., & Zhang, L. (2014). High-quality voice conversion system based on GMM statistical parameters and RBF neural network. The Journal of China Universities of Posts and Telecommunications, 21(5), 68–75.

5. Childers, D., Yegnanarayana, B., & Wu, K. (1985) Voice conversion: Factors responsible for quality. In IEEE international conference on acoustics, speech, and signal processing, ICASSP ’85 (Vol. 10, pp. 748–751).

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