Speech Synthesis Blocks

CBHG

Introduced by Wang et al. in Tacotron: Towards End-to-End Speech Synthesis

CBHG is a building block used in the Tacotron text-to-speech model. It consists of a bank of 1-D convolutional filters, followed by highway networks and a bidirectional gated recurrent unit (BiGRU).

The module is used to extract representations from sequences. The input sequence is first convolved with $K$ sets of 1-D convolutional filters, where the $k$-th set contains $C_{k}$ filters of width $k$ (i.e. $k = 1, 2, \dots , K$). These filters explicitly model local and contextual information (akin to modeling unigrams, bigrams, up to K-grams). The convolution outputs are stacked together and further max pooled along time to increase local invariances. A stride of 1 is used to preserve the original time resolution. The processed sequence is further passed to a few fixed-width 1-D convolutions, whose outputs are added with the original input sequence via residual connections. Batch normalization is used for all convolutional layers. The convolution outputs are fed into a multi-layer highway network to extract high-level features. Finally, a bidirectional GRU RNN is stacked on top to extract sequential features from both forward and backward context.

Source: Tacotron: Towards End-to-End Speech Synthesis

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Speech Synthesis 43 38.39%
Text-To-Speech Synthesis 15 13.39%
Decoder 10 8.93%
Sentence 6 5.36%
Voice Cloning 5 4.46%
Voice Conversion 4 3.57%
Speech Recognition 4 3.57%
Expressive Speech Synthesis 3 2.68%
Self-Supervised Learning 2 1.79%

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