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.

我们提出了一种通过由情感丰富的文本衍生出的嵌入来作为提示信息的系统，通过在基于 Transformer 的架构内多次集成发言者和提示信息的联合表示。我们的方法在合并情感语音和文本数据集上进行训练，并在每次训练迭代中变化提示信息，以增加模型的泛化能力。客观和主观评估结果表明，该条件合成系统能够准确地将提示中的情感转移到语音中。同时，保持了发言者身份的精确可追踪性以及整体的高话语质量和可理解性。