Emotional voice conversion (EVC) aims to change the emotional state of an
utterance while preserving the linguistic content and speaker identity. In this
paper, we propose a novel 2-stage training strategy for sequence-to-sequence
emotional voice conversion with a limited amount of emotional speech data. We
note that the proposed EVC framework leverages text-to-speech (TTS) as they
share a common goal that is to generate high-quality expressive voice. In stage
1, we perform style initialization with a multi-speaker TTS corpus, to
disentangle speaking style and linguistic content. In stage 2, we perform
emotion training with a limited amount of emotional speech data, to learn how
to disentangle emotional style and linguistic information from the speech. The
proposed framework can perform both spectrum and prosody conversion and
achieves significant improvement over the state-of-the-art baselines in both
objective and subjective evaluation.

本文提出了一种新的 2 阶段训练策略，用于在保留语言内容和说话者身份的同时改变话语的情感状态，包括用于转换情感样式和语言信息的技术，并在客观和主观评估中显著改善目前的技术水平。