Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The embeddings are trained with no explicit labels, yet learn to model a large range of acoustic expressiveness... (read more)

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Methods used in the Paper


METHOD TYPE
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