In recent years, emotional text-to-speech has shown considerable progress.
However, it requires a large amount of labeled data, which is not easily
accessible. Even if it is possible to acquire an emotional speech dataset,
there is still a limitation in controlling emotion intensity. In this work, we
propose a novel method for cross-speaker emotion transfer and manipulation
using vector arithmetic in latent style space. By leveraging only a few labeled
samples, we generate emotional speech from reading-style speech without losing
the speaker identity. Furthermore, emotion strength is readily controllable
using a scalar value, providing an intuitive way for users to manipulate
speech. Experimental results show the proposed method affords superior
performance in terms of expressiveness, naturalness, and controllability,
preserving speaker identity.

本文提出一种基于潜在风格空间中的矢量算术方法的跨说话人情感转移和操纵研究，可以使用仅有的几个标记样本从阅读风格语音生成情感语音，并且情感强度可以轻松控制，保留了说话人的身份。实验结果表明这种方法在表达性、自然度和可控性方面具有优越性。