2016 ICME:Phonetic posteriorgrams for many-to-one voice conversion without parallel data training - 代码天地

2016 ICME:Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

其他 2019-04-05 01:19:35 阅读次数: 0

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转载自blog.csdn.net/qq_40168949/article/details/87805967

2016 ICME:Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

[论文笔记] Phonetic posteriorgrams for many-to-one voice conversion without parallel data training

One-shot Voice Conversion with Disentangled Representations by Leveraging Phonetic Posteriorgrams

Voice conversion with SI-DNN and KL divergence based mapping without parallel training data

2018icassp-Non-parallel voice conversion using variational autoencoders conditioned by phonetic PPGs

Odessey2018:Average Modeling Approach to Voice Conversion with Non-Parallel Data

sub2019ins:A Vocoder-free WaveNet Voice Conversion with Non-Parallel Data

Parallel-data-free voice conversion using cycle-consistent adversarial networks

StarGAN-VC： non-parallel many-to-many voice conversion with StaGAN

F0-CONSISTENT MANY-TO-MANY NON-PARALLEL VOICE CONVERSION VIA CONDITIONAL AUTOENCODER

2019ins---Fast Learning for Non-Parallel Many-to-Many Voice Conversion with Residual Star-GAN

Non-parallel Voice Conversion using Weighted Generative Adversarial Networks

Transferring Source Style in Non-Parallel Voice Conversion

Foreign Accent Conversion by Synthesizing Speech from Phonetic Posteriorgrams：革新语音转换的开源利器...

MLHPC 2016 | Communication Quantization for Data-parallel Training of Deep Neural Networks

语音合成论文优选：声音转换Non-Parallel Sequence-to-Sequence Voice Conversion with Disentangled Linguistic

One-shot Voice Conversion with Global Speaker Embeddings

2019icas--Cross-lingual Voice Conversion with Bilingual Phonetic PosteriorGram and Average Modeling

论文笔记：WORD TRANSLATION WITHOUT PARALLEL DATA

The Voice Conversion Challenge 2018

Many-to-many Cross-lingual Voice Conversion with a Jointly Trained Speaker Embedding Network

Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder

语音合成论文优选：One-shot Voice Conversion by Separating Speaker and Content Representations with Instance N

【VC】VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture

Limited Data Emotional Voice Conversion Leveraging Text-to-Speech: Two-stage Seq2Seq Train

Whether To Pretrain DNN or Not?: An Empirical Analysis for Voice Conversion

Unsupervised Cross-Domain Singing Voice Conversion

李宏毅，语音转换，voice conversion

Self-Supervised Representations for Singing Voice Conversion

many-to-one lazy

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