Voice conversion using generative trained deep neural networks with multiple frame spectral envelopes

Voice conversion using deep neural networks

10.32657/10356/72102 ◽

2017 ◽

Author(s):

◽

Quy Hy Nguyen

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Voice Conversion

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Voice Conversion from Arbitrary Speakers Based on Deep Neural Networks with Adversarial Learning

Advances in Intelligent Information Hiding and Multimedia Signal Processing - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-319-63859-1_13 ◽

2017 ◽

pp. 97-103

Author(s):

Sou Miyamoto ◽

Takashi Nose ◽

Suzunosuke Ito ◽

Harunori Koike ◽

Yuya Chiba ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Voice Conversion ◽

Adversarial Learning

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Voice Conversion System Based on Deep Neural Networks

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.8668 ◽

2020 ◽

Vol 17 (1) ◽

pp. 316-321

Author(s):

V. Naveena ◽

Susmitha Vekkot ◽

K. Jeeva Priya

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

State Of The Art ◽

Objective Evaluation ◽

Subjective Assessment ◽

Voice Conversion ◽

Mel Frequency Cepstral Coefficients ◽

Listening Test ◽

Conversion System ◽

Maximum Improvement

The paper focuses on usage of deep neural networks for converting a person’s voice to another person’s voice, analogous to a mimic. The work in this paper introduces the concept of neural networks and deploys multi-layer deep neural networks for building a framework for voice conversion. The spectral Mel-Frequency Cepstral Coefficients (MFCCs) are converted using a 10-layer deep network while fundamental frequency (F0) conversion is accomplished by logarithmic Gaussian normalized transformation. MFCCs are subjected to inverse cepstral filtering while changes in F0 are incorporated using Pitch Synchronous OverLap Add (PSOLA) algorithm for re-synthesis. The results obtained are compared using Mel Cepstral Distortion (MCD) for objective evaluation while ABX-listening test is conducted for subjective assessment. Maximum improvement in MCD of 13.87% is obtained for female-to-male conversion while ABX-listening test indicates that female-to-male is closest to target with an agreement of 76.2%. The method achieves reasonably good performance compared to state-of-the-art using optimal resources and avoids requirement of highly complex computations.

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