Jointly Trained Conversion Model With LPCNet for Any-to-One Voice Conversion Using Speaker-Independent Linguistic Features
Author:
Affiliation:
1. ObEN, Pasadena, CA, USA
2. Queensland University of Technology, Brisbane, QLD, Australia
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/6287639/9668973/09968235.pdf?arnumber=9968235
Reference48 articles.
1. Phonetic posteriorgrams for many-to-one voice conversion without parallel data training
2. Tacotron: Towards End-to-End Speech Synthesis
3. Voice Conversion from Unaligned Corpora Using Variational Autoencoding Wasserstein Generative Adversarial Networks
4. The language-independent bottleneck features
5. Non-Parallel Sequence-to-Sequence Voice Conversion With Disentangled Linguistic and Speaker Representations
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1. Reimagining speech: a scoping review of deep learning-based methods for non-parallel voice conversion;Frontiers in Signal Processing;2024-08-16
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