Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data

6 Apr 2019 Roee Levy Leshem Raja Giryes

This paper introduces Taco-VC, a novel architecture for voice conversion based on Tacotron synthesizer, which is a sequence-to-sequence with attention model. The training of multi-speaker voice conversion systems requires a large number of resources, both in training and corpus size... (read more)

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Methods used in the Paper


METHOD TYPE
Mixture of Logistic Distributions
Output Functions
Griffin-Lim Algorithm
Phase Reconstruction
Sigmoid Activation
Activation Functions
Highway Layer
Miscellaneous Components
Residual Connection
Skip Connections
Convolution
Convolutions
Batch Normalization
Normalization
Max Pooling
Pooling Operations
Residual GRU
Recurrent Neural Networks
BiGRU
Bidirectional Recurrent Neural Networks
Highway Network
Feedforward Networks
CBHG
Speech Synthesis Blocks
ReLU
Activation Functions
Dropout
Regularization
Dense Connections
Feedforward Networks
Tanh Activation
Activation Functions
Additive Attention
Attention Mechanisms
GRU
Recurrent Neural Networks
Tacotron
Text-to-Speech Models
Dilated Causal Convolution
Temporal Convolutions
WaveNet
Generative Audio Models