Speech-to-Singing Conversion Based on Boundary Equilibrium GAN
Da-Yi Wu, Yi‐Hsuan Yang · 2020
This paper investigates the use of generative adversarial network (GAN)-based models for converting a speech signal into a singing one, without reference to the phoneme sequence underlying the speech.This is achieved by viewing speech-to-singing conversion as a style transfer problem.Specifically, given a speech input, and the F0 contour of the target singing output, the proposed model generates the spectrogram of a singing signal with a progressive-growing encoder/decoder architecture.Moreover, the model uses a boundary equilibrium GAN loss term such that it can learn from both paired and unpaired data.The spectrogram is finally converted into wave with a separate GAN-based vocoder.Our quantitative and qualitative analysis show that the proposed model generates singing voices with much higher naturalness than an existing non adversarially-trained baseline.