@inproceedings{30ca4ef4ea724ebda2c42043014127d6,
title = "CNEG-VC: Contrastive Learning Using Hard Negative Example In Non-Parallel Voice Conversion",
abstract = "Contrastive learning has advantages for non-parallel voice conversion, but the previous conversion results could be better and more preserved. In previous techniques, negative samples were randomly selected in the features vector from different locations. A positive example could not be effectively pushed toward the query examples. We present contrastive learning in non-parallel voice conversion to solve this problem using hard negative examples. We named it CNEG-VC. Specifically, we teach the generator to generate negative examples. Our proposed generator has specific features. First, Instance-wise negative examples are generated based on voice input. Second, when taught with an adversarial loss, it can produce hard negative examples. The generator significantly improves non-parallel voice conversion performance. Our CNEG-VC achieved state-of-the-art results by outperforming previous techniques.",
keywords = "contrastive learning, generative adversarial networks, hard negative example, non-parallel data, Voice conversion",
author = "Bima Prihasto and Lin, {Yi Xing} and Le, {Phuong Thi} and Huang, {Chien Lin} and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",
year = "2023",
doi = "10.1109/ICASSP49357.2023.10094995",
language = "???core.languages.en_GB???",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings",
}