DurFlex-EVC: Duration-Flexible Emotional Voice Conversion with Parallel Generation

Emotional voice conversion involves modifying the pitch, spectral envelope, and other acoustic characteristics of speech to match a desired emotional state while maintaining the speaker's identity. Recent advances in EVC involve simultaneously modeling pitch and duration by exploiting the potential of sequence-to-sequence models. In this study, we focus on parallel speech generation to increase the reliability and efficiency of conversion. We introduce a duration-flexible EVC (DurFlex-EVC) that integrates a style autoencoder and a unit aligner. The previous variable-duration parallel generation model required text-to-speech alignment. We consider self-supervised model representation and discrete speech units to be the core of our parallel generation. The style autoencoder promotes content style disentanglement by separating the source style of the input features and applying them with the target style. The unit aligner encodes unit-level features by modeling emotional context. Furthermore, we enhance the style of the features with a hierarchical stylize encoder and generate high-quality Mel-spectrograms with a diffusion-based generator. The effectiveness of the approach has been validated through subjective and objective evaluations and has been demonstrated to be superior to baseline models.