A New GAN-based End-to-End TTS Training Algorithm

9 Apr 2019 • Haohan Guo • Frank K. Soong • Lei He • Lei Xie

End-to-end, autoregressive model-based TTS has shown significant performance improvements over the conventional one. However, the autoregressive module training is affected by the exposure bias, or the mismatch between the different distributions of real and predicted data... (read more)

PDF Abstract

Results from the Paper

Edit

Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

METHOD	TYPE
Griffin-Lim Algorithm	Phase Reconstruction
Sigmoid Activation	Activation Functions
Highway Layer	Miscellaneous Components
Residual Connection	Skip Connections
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
Convolution	Convolutions
GAN	Generative Models

METHOD

TYPE

Griffin-Lim Algorithm