Decoupling Speaker-Independent Emotions for Voice Conversion via Source-Filter Networks

Abstract

Emotional voice conversion (VC) aims to convert a neutral voice to an emotional one while retaining the linguistic information and speaker identity. We note that the decoupling of emotional features from other speech information (such as content, speaker identity, etc.) is the key to achieving promising performance. Some recent attempts of speech representation decoupling on the neutral speech cannot work well on the emotional speech, due to the more complex entanglement of acoustic properties in the latter. To address this problem, here we propose a novel Source-Filter-based Emotional VC model (SFEVC) to achieve proper filtering of speaker-independent emotion cues from both the timbre and pitch features. Our SFEVC model consists of multi-channel encoders, emotion separate encoders, pre-trained speaker-dependent encoders, and the corresponding decoder. Note that all encoder modules adopt a designed information bottleneck auto-encoder. Additionally, to further improve the conversion quality for various emotions, a novel training strategy based on the 2D Valence-Arousal (VA) space is proposed. Experimental results show that the proposed SFEVC along with a VA training strategy outperforms all baselines and achieves the state-of-the-art performance in speaker-independent emotional VC with nonparallel data.