Zero-Shot Voice Conversion with Content-Biased C-DSVAE
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Solution Overview
Problem
Existing voice conversion systems face challenges in zero-shot scenarios due to the need for aligned speaker pairs during training, limitations in generalization, and the reduction of phonetic-structure information in content embeddings, especially in non-parallel data settings.
Innovation Solution
A conditional disentangled sequential variational auto-encoder (C-DSVAE) is employed, which uses a shared encoder to separate speaker and content embeddings, applies a content bias to reshape the content embedding, and incorporates a KL-Divergence loss for improved phonetic information preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a disentangled sequential variational autoencoder (DSVAE) is used to separate content and speaker embeddings, then zero-shot voice conversion becomes feasible, but the randomness of initialized prior distributions causes content embedding to lose phonetic-structure information
Solution Approach 1:
The patent applies preliminary action by pre-training the content encoder using forced alignment and pseudo-labels before the main zero-shot VC training. This preliminary step establishes accurate phonetic-structure information in the content embedding, preventing the information loss that would otherwise occur during the subsequent training phases. The content bias is computed and applied in advance to guide the content encoder learning.
Solution Approach 2:
The patent introduces content bias as an intermediary element that mediates between the random prior distribution and the content embedding. This content bias, derived from forced alignment or pseudo-labels, acts as a guiding signal that preserves phonetic-structure information while allowing the variational autoencoder framework to function. It bridges the gap between the stochastic nature of VAEs and the need for structured phonetic representation.
2Measurement precision
If pre-trained speaker models and positive pairs are required during training, then speaker embedding accuracy improves, but the system complexity and data requirements increase
Solution Approach 1:
The patent segments the training process into distinct phases: pre-training with positive pairs to establish speaker representation, and fine-tuning in a zero-shot setting. This segmentation allows the system to benefit from supervised speaker embedding learning while ultimately achieving unsupervised adaptation. The content and speaker encoding tasks are also segmented into separate encoders with dedicated loss functions.
Solution Approach 2:
The patent employs dynamic training strategies where the model transitions from a supervised pre-training phase with positive pairs to an unsupervised zero-shot phase. The content encoder and speaker encoder are dynamically updated with different objectives at different stages. The content bias computation is dynamically adjusted based on the training progress, allowing the system to adapt its complexity requirements over time.
Data Source
AI summary
A method, system, apparatus, and computer-readable medium for voice conversion using a conditional disentangled sequential variational auto-encoder (C-DSVAE) is provided. The method, performed by at least one processor, includes receiving input speech segments, encoding the input speech segments via a shared encoder to generate a speaker embedding and a content embedding, and encoding a posterior distribution of the speaker embedding via a speaker encoder and encoding a posterior distribution of the content embedding via a content encoder to obtain encoded results. The method further includes enabling a content bias, reshaping the content embedding using the content bias, and generating a reconstructed speech output based on the encoded results and the reshaped content embedding.