  We present a unified system to realize one-shot voice conversion (VC) on the
pitch, rhythm, and speaker attributes. Existing works generally ignore the
correlation between prosody and language content, leading to the degradation of
naturalness in converted speech. Additionally, the lack of proper language
features prevents these systems from accurately preserving language content
after conversion. To address these issues, we devise a cascaded modular system
leveraging self-supervised discrete speech units as language representation.
These discrete units provide duration information essential for rhythm
modeling. Our system first extracts utterance-level prosody and speaker
representations from the raw waveform. Given the prosody representation, a
prosody predictor estimates pitch, energy, and duration for each discrete unit
in the utterance. A synthesizer further reconstructs speech based on the
predicted prosody, speaker representation, and discrete units. Experiments show
that our system outperforms previous approaches in naturalness,
intelligibility, speaker transferability, and prosody transferability. Code and
samples are publicly available.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound