Many-to-Many Voice Conversion based Feature Disentanglement using Variational Autoencoder
This work addresses voice conversion for unseen speaker scenarios, representing an incremental improvement over existing methods.
The paper tackles the problem of many-to-many voice conversion by proposing a feature disentanglement method using a Variational Autoencoder to separate speaker identity and linguistic content, achieving competitive performance in naturalness and speaker similarity compared to state-of-the-art models.
Voice conversion is a challenging task which transforms the voice characteristics of a source speaker to a target speaker without changing linguistic content. Recently, there have been many works on many-to-many Voice Conversion (VC) based on Variational Autoencoder (VAEs) achieving good results, however, these methods lack the ability to disentangle speaker identity and linguistic content to achieve good performance on unseen speaker scenarios. In this paper, we propose a new method based on feature disentanglement to tackle many to many voice conversion. The method has the capability to disentangle speaker identity and linguistic content from utterances, it can convert from many source speakers to many target speakers with a single autoencoder network. Moreover, it naturally deals with the unseen target speaker scenarios. We perform both objective and subjective evaluations to show the competitive performance of our proposed method compared with other state-of-the-art models in terms of naturalness and target speaker similarity.