Mel-S3R: Combining Mel-spectrogram and self-supervised speech representation with VQ-VAE for any-to-any voice conversion

Author:

Yang Jichen,Zhou YiORCID,Huang Hao

Publisher

Elsevier BV

Subject

Computer Science Applications,Computer Vision and Pattern Recognition,Linguistics and Language,Language and Linguistics,Communication,Modeling and Simulation,Software

Reference69 articles.

1. Vq-wav2vec: Self-supervised learning of discrete speech representations;Baevski,2019

2. Baevski, A., Zhou, Y., Mohamed, A., Auli, M., 2020. wav2vec 2.0: a framework for self-supervised learning of speech representation. In: Annual Conference on Neural Information Processing System.

3. Voice conversion using deep neural networks with layer-wise generative training;Chen;IEEE/ACM Trans. Audio Speech Lang. Process.,2014

4. Chen, Y.-N., Liu, L.-J., Hu, Y.-J., Jiang, Y., Ling, Z.-H., 2022. Improving Recognition-Synthesis Based any-to-one Voice Conversion with Cyclic Training. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 7007–7011.

5. WavLM: Large-scale self-supervised pre-training for full stack speech processing;Chen,2021

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