  Emotional speech synthesis aims to synthesize human voices with various
emotional effects. The current studies are mostly focused on imitating an
averaged style belonging to a specific emotion type. In this paper, we seek to
generate speech with a mixture of emotions at run-time. We propose a novel
formulation that measures the relative difference between the speech samples of
different emotions. We then incorporate our formulation into a
sequence-to-sequence emotional text-to-speech framework. During the training,
the framework does not only explicitly characterize emotion styles, but also
explores the ordinal nature of emotions by quantifying the differences with
other emotions. At run-time, we control the model to produce the desired
emotion mixture by manually defining an emotion attribute vector. The objective
and subjective evaluations have validated the effectiveness of the proposed
framework. To our best knowledge, this research is the first study on
modelling, synthesizing, and evaluating mixed emotions in speech.
