A systematic investigation of the benefits of moving from frame-by-frame models to sequence-to-sequence models.
Wang et al. Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers
This paper introduces the VALL-E model, which frames speech synthesis as a language modelling task in which a sequence of audio codec codes are generated conditionally, given a preceding sequence of text (and a speech prompt).
Tachibana et al. Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention
DCTTS is comparable to Tacotron, but is faster because it uses non-recurrent architectures for the encoder and decoder.
Shen et al: Natural TTS Synthesis By Conditioning Wavenet On Mel Spectrogram Predictions
Tacotron 2 was one of the most successful sequence-to-sequence models for text-to-speech of its time and inspired many subsequent models.
Ren et al. FastSpeech 2: Fast and High-Quality End-to-End Text to Speech
FastSpeech 2 improves over FastSpeech by not requiring a complicated teacher-student training regime, but instead being trained directly on the data. It is very similar to FastPitch 2, which was released around the same by different authors.
Łańcucki. FastPitch: Parallel Text-to-speech with Pitch Prediction
Very similar to FastSpeech2, FastPitch has the advantage of an official Open Source implementation by the author (at NVIDIA).
This is the new version. Still under construction.