  Realistic emotional voice conversion (EVC) aims to enhance emotional
diversity of converted audios, making the synthesized voices more authentic and
natural. To this end, we propose Emotional Intensity-aware Network (EINet),
dynamically adjusting intonation and rhythm by incorporating controllable
emotional intensity. To better capture nuances in emotional intensity, we go
beyond mere distance measurements among acoustic features. Instead, an emotion
evaluator is utilized to precisely quantify speaker's emotional state. By
employing an intensity mapper, intensity pseudo-labels are obtained to bridge
the gap between emotional speech intensity modeling and run-time conversion. To
ensure high speech quality while retaining controllability, an emotion renderer
is used for combining linguistic features smoothly with manipulated emotional
features at frame level. Furthermore, we employ a duration predictor to
facilitate adaptive prediction of rhythm changes condition on specifying
intensity value. Experimental results show EINet's superior performance in
naturalness and diversity of emotional expression compared to state-of-the-art
EVC methods.
