Voice Conversion
149 papers with code • 2 benchmarks • 5 datasets
Voice Conversion is a technology that modifies the speech of a source speaker and makes their speech sound like that of another target speaker without changing the linguistic information.
Benchmarks
These leaderboards are used to track progress in Voice Conversion
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Libraries
Use these libraries to find Voice Conversion models and implementations
LibriSpeech
ESD
VESUS
Most implemented papers
StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks
This paper proposes a method that allows non-parallel many-to-many voice conversion (VC) by using a variant of a generative adversarial network (GAN) called StarGAN.
One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization
jjery2243542/adaptive_voice_conversion
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Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers.
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
liusongxiang/StarGAN-Voice-Conversion
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On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.
Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks
A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based method under advantageous conditions with parallel and twice the amount of data.
CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion
Non-parallel voice conversion (VC) is a technique for learning the mapping from source to target speech without relying on parallel data.
MOSNet: Deep Learning based Objective Assessment for Voice Conversion
In this paper, we propose deep learning-based assessment models to predict human ratings of converted speech.
Unsupervised Speech Decomposition via Triple Information Bottleneck
auspicious3000/SpeechSplit
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ICML 2020
Speech information can be roughly decomposed into four components: language content, timbre, pitch, and rhythm.
Utilizing Self-supervised Representations for MOS Prediction
s3prl/s3prl
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In this paper, we use self-supervised pre-trained models for MOS prediction.
Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning
s3prl/s3prl
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To explore this issue, we proposed to employ Mockingjay, a self-supervised learning based model, to protect anti-spoofing models against adversarial attacks in the black-box scenario.
Voice Conversion from Non-parallel Corpora Using Variational Auto-encoder
We propose a flexible framework for spectral conversion (SC) that facilitates training with unaligned corpora.
