Converting Anyone's Emotion: Towards Speaker-Independent Emotional Voice Conversion
This addresses the challenge of speaker-dependent emotion conversion in speech processing, offering a more generalizable solution, though it is incremental in improving existing methods.
The paper tackles the problem of emotional voice conversion by proposing a speaker-independent framework that can convert anyone's emotion without parallel data, achieving competitive results for both seen and unseen speakers.
Emotional voice conversion aims to convert the emotion of 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 consider that there is a common code between speakers for emotional expression in a spoken language, therefore, a speaker-independent mapping between emotional states is possible. In this paper, we propose a speaker-independent emotional voice conversion framework, that can convert anyone's emotion without the need for parallel data. We propose a VAW-GAN based encoder-decoder structure to learn the spectrum and prosody mapping. We perform prosody conversion by using continuous wavelet transform (CWT) to model the temporal dependencies. We also investigate the use of F0 as an additional input to the decoder to improve emotion conversion performance. Experiments show that the proposed speaker-independent framework achieves competitive results for both seen and unseen speakers.