  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.
