Many-to-Many Voice Conversion using Conditional Cycle-Consistent Adversarial Networks

Voice Conversion

Categories: Adversarial Training | Convolutional Neural Network | Deep Learning | Generative Adversarial Network

Tags: 2020 | BongGu Ko | Dongsuk Yook | In-Chul Yoo | Keonnyeong Lee | Shindong Lee

Voice conversion (VC) refers to transforming the speaker characteristics of an utterance without altering its linguistic contents. Many works on voice conversion require to have parallel training data that is highly expensive to acquire. Recently, the cycle-consistent adversarial network (CycleGAN), which does not require parallel training data, has been applied to voice conversion, showing the state-of-the-art performance. The CycleGAN based voice conversion, however, can be used only for a pair of speakers, i.e., one-to-one voice conversion between two speakers. In this paper, we extend the ...

Many-to-Many Voice Conversion using Cycle-Consistent Variational Autoencoder with Multiple Decoders

Voice Conversion

Categories: Adversarial Training | Autoencoder | Fourier Transform | Generative Adversarial Network | Mean Opinion Score | Mel-spectrogram

Tags: 2019 | Dongsuk Yook | In-Chul Yoo | Keonnyeong Lee

One of the obstacles in many-to-many voice conversion is the requirement of the parallel training data, which contain pairs of utterances with the same linguistic content spoken by different speakers. Since collecting such parallel data is a highly expensive task, many works attempted to use non-parallel training data for many-to-many voice conversion. One of such approaches is using the variational autoencoder (VAE). Though it can handle many-to-many voice conversion without the parallel training, the VAE based voice conversion methods suffer from low sound qualities of the converted speech. ...