Multi-target Voice Conversion without Parallel Data by Adversarially Learning Disentangled Audio Representations
This addresses the need for efficient multi-speaker voice conversion in speech processing, though it is incremental as it builds on existing Cycle-GAN methods.
The paper tackles the problem of voice conversion to multiple speakers without parallel data by proposing an adversarial learning framework that disentangles speaker characteristics from linguistic content, achieving very good voice quality for a target set of 20 speakers.
Recently, cycle-consistent adversarial network (Cycle-GAN) has been successfully applied to voice conversion to a different speaker without parallel data, although in those approaches an individual model is needed for each target speaker. In this paper, we propose an adversarial learning framework for voice conversion, with which a single model can be trained to convert the voice to many different speakers, all without parallel data, by separating the speaker characteristics from the linguistic content in speech signals. An autoencoder is first trained to extract speaker-independent latent representations and speaker embedding separately using another auxiliary speaker classifier to regularize the latent representation. The decoder then takes the speaker-independent latent representation and the target speaker embedding as the input to generate the voice of the target speaker with the linguistic content of the source utterance. The quality of decoder output is further improved by patching with the residual signal produced by another pair of generator and discriminator. A target speaker set size of 20 was tested in the preliminary experiments, and very good voice quality was obtained. Conventional voice conversion metrics are reported. We also show that the speaker information has been properly reduced from the latent representations.