Investigating RNN-based speech enhancement methods for noise-robust Text-to-Speech

Deep Learning has been applied successfully to speech processing. In this paper we propose an architecture for speech synthesis using multiple speakers. Some hidden layers are shared by all the speakers, while there is a specific output layer for each speaker. Objective and perceptual experiments prove that this scheme produces much better results in comparison with sin- gle speaker model. Moreover, we also tackle the problem of speaker interpolation by adding a new output layer (a-layer) on top of the multi-output branches. An identifying code is injected into the layer together with acoustic features of many speakers. Experiments show that the a-layer can effectively learn to interpolate the acoustic features between speakers.