An Analysis of Performance Evaluation Metrics for Voice Conversion Models
Author:
Affiliation:
1. National Institute of Technology Durgapur,Department of Computer Science and Engineering,Durgapur,India
2. Jalpaiguri Government Engineering College,Department of Electronic and Communication Engineering,Jalpaiguri,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10038981/10039721/10040000.pdf?arnumber=10040000
Reference34 articles.
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4. An adaptive learning based generative adversarial network for one-to-one voice conversion;dhar;IEEE Transactions on Artificial Intelligence,2022
5. High-Quality Nonparallel Voice Conversion Based on Cycle-Consistent Adversarial Network
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2. CCLCap-AE-AVSS: Cycle consistency loss based capsule autoencoders for audio–visual speech synthesis;Journal of Intelligent Systems;2024-01-01
3. FID-RPRGAN-VC: Fréchet Inception Distance Loss based Region-wise Position Normalized Relativistic GAN for Non-Parallel Voice Conversion;2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC);2023-10-31
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