  In recent years, prompting has quickly become one of the standard ways of
steering the outputs of generative machine learning models, due to its
intuitive use of natural language. In this work, we propose a system
conditioned on embeddings derived from an emotionally rich text that serves as
prompt. Thereby, a joint representation of speaker and prompt embeddings is
integrated at several points within a transformer-based architecture. Our
approach is trained on merged emotional speech and text datasets and varies
prompts in each training iteration to increase the generalization capabilities
of the model. Objective and subjective evaluation results demonstrate the
ability of the conditioned synthesis system to accurately transfer the emotions
present in a prompt to speech. At the same time, precise tractability of
speaker identities as well as overall high speech quality and intelligibility
are maintained.
