Multi-level Temporal-channel Speaker Retrieval for Robust Zero-shot Voice Conversion
Zero-shot voice conversion (VC) converts source speech into the voice of any
desired speaker using only one utterance of the speaker without requiring
additional model updates. Typical methods use a speaker representation from a
pre-trained speaker verification (SV) model or learn speaker representation
during VC training to achieve zero-shot VC. However, existing speaker modeling
methods overlook the variation of speaker information richness in temporal and
frequency channel dimensions of speech. This insufficient speaker modeling
hampers the ability of the VC model to accurately represent unseen speakers who
are not in the training dataset. In this study, we present a robust zero-shot
VC model with multi-level temporal-channel retrieval, referred to as MTCR-VC.
Specifically, to flexibly adapt to the dynamic-variant speaker characteristic
in the temporal and channel axis of the speech, we propose a novel fine-grained
speaker modeling method, called temporal-channel retrieval (TCR), to find out
when and where speaker information appears in speech. It retrieves
variable-length speaker representation from both temporal and channel
dimensions under the guidance of a pre-trained SV model. Besides, inspired by
the hierarchical process of human speech production, the MTCR speaker module
stacks several TCR blocks to extract speaker representations from
multi-granularity levels. Furthermore, to achieve better speech disentanglement
and reconstruction, we introduce a cycle-based training strategy to simulate
zero-shot inference recurrently. We adopt perpetual constraints on three
aspects, including content, style, and speaker, to drive this process.
Experiments demonstrate that MTCR-VC is superior to the previous zero-shot VC
methods in modeling speaker timbre while maintaining good speech naturalness.