Tibetan speech synthesis based on an improved neural network

Nowadays, Tibetan speech synthesis based on neural network has become the mainstream method. Among them, griffin-lim vocoder is widely used in because of its relatively simple synthesis.Aiming at problem low fidelity vocoder, this paper uses WaveNet instead for synthesis.This first convolution operation and attention mechanism to extract sequence features.And then linear projection feature amplification module predict mel spectrogram.Finally, use synthesize waveform. Experimental data shows that our model a better performance synthesis.