Generative Adversarial Training for Text-to-Speech Synthesis Based on
  Raw Phonetic Input and Explicit Prosody Modelling
Tiberiu Boros Stefan Daniel Dumitrescu Ionut Mironica Radu Chivereanu
We describe an end-to-end speech synthesis system that uses generative
adversarial training. We train our Vocoder for raw phoneme-to-audio conversion,
using explicit phonetic, pitch and duration modeling. We experiment with
several pre-trained models for contextualized and decontextualized word
embeddings and we introduce a new method for highly expressive character voice
matching, based on discreet style tokens.