  Emotional voice conversion (EVC) traditionally targets the transformation of
spoken utterances from one emotional state to another, with previous research
mainly focusing on discrete emotion categories. This paper departs from the
norm by introducing a novel perspective: a nuanced rendering of mixed emotions
and enhancing control over emotional expression. To achieve this, we propose a
novel EVC framework, Mixed-EVC, which only leverages discrete emotion training
labels. We construct an attribute vector that encodes the relationships among
these discrete emotions, which is predicted using a ranking-based support
vector machine and then integrated into a sequence-to-sequence (seq2seq) EVC
framework. Mixed-EVC not only learns to characterize the input emotional style
but also quantifies its relevance to other emotions during training. As a
result, users have the ability to assign these attributes to achieve their
desired rendering of mixed emotions. Objective and subjective evaluations
confirm the effectiveness of our approach in terms of mixed emotion synthesis
and control while surpassing traditional baselines in the conversion of
discrete emotions from one to another.
