  The field of Text-to-Speech has experienced huge improvements last years
benefiting from deep learning techniques. Producing realistic speech becomes
possible now. As a consequence, the research on the control of the
expressiveness, allowing to generate speech in different styles or manners, has
attracted increasing attention lately. Systems able to control style have been
developed and show impressive results. However the control parameters often
consist of latent variables and remain complex to interpret. In this paper, we
analyze and compare different latent spaces and obtain an interpretation of
their influence on expressive speech. This will enable the possibility to build
controllable speech synthesis systems with an understandable behaviour.
