Speaker-Independent Emotional Voice Conversion via Disentangled Representations

作者：Xunquan Chen, Xuexin Xu, Jinhui Chen, Zhizhong Zhang, Tetsuya Takiguchi, Edwin R. Hancock · 发表于：IEEE Transactions on Multimedia · 年份：2022 · DOI：10.1109/tmm.2022.3222646 · 被引用次数：8 · 研究领域：Speech and Audio Processing、Speech Recognition and Synthesis、Music and Audio Processing

Emotional Voice Conversion (EVC) technology aims to transfer emotional state in speech while keeping the linguistic information and speaker identity unchanged. Prior studies on EVC have been limited to perform the conversion for a specific speaker or a predefined set of multiple speakers seen in the training stage. When encountering arbitrary speakers that may be unseen during training (outside the set of speakers used in training), existing EVC methods have limited conversion capabilities. However, converting the emotion of arbitrary speakers, even those unseen during the training procedure, in one model is much more challenging and much more attractive in real-world scenarios. To address this problem, in this study, we propose SIEVC, a novel speaker-independent emotional voice conversion framework for arbitrary speakers via disentangled representation learning. The proposed method employs the autoencoder framework to disentangle the emotion information and emotion-independent information of each input speech into separated representation spaces. To achieve better disentanglement, we incorporate mutual information minimization into the training process. In addition, adversarial training is applied to enhance the quality of the generated audio signals. Finally, speaker-independent EVC for arbitrary speakers could be achieved by only replacing the emotion representations of source speech with the target ones. The experimental results demonstrate that the proposed EVC model out...