We are a team of computer science our graduation project about EmotionalSpeech Synthesis.

We've found valuable information like research papers and WaveNet, Tacotron. A website (https://www.voicery.com/)

we were hoping to get to know more information from you.

We need more details what should we start with to grasp the fundamentals to build this idea, what is the architecture to be used in this project, whether there are papers, a GitHub Repository containing helpful documentation, datasets, some other resources, previous knowledge.

  • $\begingroup$ If i have understood the problem right, you have programmed a software a software for emotional speech synthesis and the problem is, that the generated audio quality is low. And now the question is how to improve it with the help of Recurrent neural networks, right? $\endgroup$ – Manuel Rodriguez Oct 16 '19 at 20:04
  • $\begingroup$ No we didn't, we ask where to start? $\endgroup$ – Andrew Maher Oct 17 '19 at 5:44

Since I am working with audio synthesis projects, I can suggest you to go through the recently released paper "MelNet: A Generative Model for Audio in the Frequency Domain", in this architecture human like speech is synthesized, I think this might be better than WaveNet where the generated audio does contain robotic nature, I think this will definitely help you with Emotional speech synthesis as it generates human-like voices. You can check for online demos as well.


Here Is the Paper!

Related YouTube Video

Hope this helps!

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  • $\begingroup$ Thanks for helping the demo is really great, If you please send the paper again as the link doesn't work. $\endgroup$ – Andrew Maher Oct 17 '19 at 5:44
  • $\begingroup$ Hi @AndrewMaher Glad that it helped you,I have edited the link. If this or any answer has solved your question please consider accepting it by clicking the check-mark so that others will be benefited. $\endgroup$ – SUMAN Oct 17 '19 at 7:05
  • $\begingroup$ Can you suggest good datasets to start with $\endgroup$ – Andrew Maher Oct 19 '19 at 12:25

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