Tacotron 2
Tacotron2
**Tacotron 2** is a neural network architecture for speech synthesis directly from text. It consists of two components:

- a recurrent sequence-to-sequence feature prediction network with
attention which predicts a sequence of mel spectrogram frames from
an input character sequence
- a modified version of [WaveNet](https://paperswithcode.com/method/wavenet) which generates time-domain waveform samples conditioned on the
predicted mel spectrogram frames

In contrast to the original [Tacotron](https://paperswithcode.com/method/tacotron), Tacotron 2 uses simpler building blocks, using vanilla [LSTM](https://paperswithcode.com/method/lstm) and convolutional layers in the encoder and decoder instead of [CBHG](https://paperswithcode.com/method/cbhg) stacks and [GRU](https://paperswithcode.com/method/gru) recurrent layers. Tacotron 2 does not use a “reduction factor”, i.e., each decoder step corresponds to a single spectrogram frame. Location-sensitive attention is used instead of [additive attention](https://paperswithcode.com/method/additive-attention).
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions
https://paperswithcode.com/paper/natural-tts-synthesis-by-conditioning-wavenet
Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions