Emotional voice conversion (EVC) seeks to modify the emotional tone of a
speaker's voice while preserving the original linguistic content and the
speaker's unique vocal characteristics. Recent advancements in EVC have
involved the simultaneous modeling of pitch and duration, utilizing the
potential of sequence-to-sequence (seq2seq) models. To enhance reliability and
efficiency in conversion, this study shifts focus towards parallel speech
generation. We introduce Duration-Flexible EVC (DurFlex-EVC), which integrates
a style autoencoder and unit aligner. Traditional models, while incorporating
self-supervised learning (SSL) representations that contain both linguistic and
paralinguistic information, have neglected this dual nature, leading to reduced
controllability. Addressing this issue, we implement cross-attention to
synchronize these representations with various emotions. Additionally, a style
autoencoder is developed for the disentanglement and manipulation of style
elements. The efficacy of our approach is validated through both subjective and
objective evaluations, establishing its superiority over existing models in the
field.

本研究介绍了一种基于序列到序列模型的 Duration-Flexible 情感语音转换方法 (DurFlex-EVC)，通过引入样式自编码器和单位对齐器，实现了同时建模音高和持续时间的并行语音生成，提高了转换的可靠性和效率，并通过跨注意机制使得包含语言和语外信息的自监督学习表示与各种情感进行同步，进一步通过样式自编码器对样式元素进行解耦和操作，经过主客观评估证明了该方法相较于现有模型在领域内的优越性。