Spectrum and Prosody Conversion for Cross-lingual Voice Conversion with CycleGAN

Voice Conversion

Categories: Adversarial Training | Convolutional Neural Network | Fourier Transform | Generative Adversarial Network | Mel-spectrogram | Wavelet Transform

Tags: 2020 | Berrak Sisman | Haizhou Li | Kun Zhou | Zongyang Du

Cross-lingual voice conversion aims to change source speaker's voice to sound like that of target speaker, when source and target speakers speak different languages. It relies on non-parallel training data from two different languages, hence, is more challenging than mono-lingual voice conversion. Previous studies on cross-lingual voice conversion mainly focus on spectral conversion with a linear transformation for F0 transfer. However, as an important prosodic factor, F0 is inherently hierarchical, thus it is insufficient to just use a linear method for conversion. We propose the use of conti...

Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion

Voice Conversion

Categories: Adversarial Training | Convolutional Neural Network | Deep Learning | Fourier Transform | Generative Adversarial Network | Mean Opinion Score | Mel-spectrogram | Wavelet Transform

Tags: 2020 | Berrak Sisman | Haizhou Li | Kun Zhou | Mingyang Zhang

Emotional voice conversion aims to convert the emotion of the speech from one state to another while preserving the linguistic content and speaker identity. The prior studies on emotional voice conversion are mostly carried out under the assumption that emotion is speaker-dependent. We believe that emotions are expressed universally across speakers, therefore, the speaker-independent mapping between emotional states of speech is possible. In this paper, we propose to build a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for paralle...