Vec-Tok-VC+: Residual-enhanced Robust Zero-shot Voice Conversion with Progressive Constraints in a Dual-mode Training Strategy (2406.09844v1)

Abstract: Zero-shot voice conversion (VC) aims to transform source speech into arbitrary unseen target voice while keeping the linguistic content unchanged. Recent VC methods have made significant progress, but semantic losses in the decoupling process as well as training-inference mismatch still hinder conversion performance. In this paper, we propose Vec-Tok-VC+, a novel prompt-based zero-shot VC model improved from Vec-Tok Codec, achieving voice conversion given only a 3s target speaker prompt. We design a residual-enhanced K-Means decoupler to enhance the semantic content extraction with a two-layer clustering process. Besides, we employ teacher-guided refinement to simulate the conversion process to eliminate the training-inference mismatch, forming a dual-mode training strategy. Furthermore, we design a multi-codebook progressive loss function to constrain the layer-wise output of the model from coarse to fine to improve speaker similarity and content accuracy. Objective and subjective evaluations demonstrate that Vec-Tok-VC+ outperforms the strong baselines in naturalness, intelligibility, and speaker similarity.

Overview of Vec-Tok-VC+: Residual-enhanced Robust Zero-shot Voice Conversion

The paper "Vec-Tok-VC+: Residual-enhanced Robust Zero-shot Voice Conversion with Progressive Constraints in a Dual-mode Training Strategy" by Linhan Ma et al. presents a novel approach to zero-shot voice conversion (VC). The model is designed to transform the voice of a source speaker into an unseen target speaker’s voice using an advanced method that requires only a three-second prompt from the target speaker. The key innovations include a residual-enhanced K-Means decoupler, a teacher-guided refinement process, and a multi-codebook progressive loss function.

Key Contributions

1. Residual-enhanced K-Means Decoupler: The model introduces a two-layer clustering process inspired by residual vector quantization (RVQ). This technique is employed to enhance the semantic content extraction by quantizing residual information, thereby mitigating semantic losses typically introduced in the decoupling process.
2. Teacher-guided Refinement: A dual-mode training strategy is implemented, which involves reconstruction and conversion modes. By incorporating a teacher-guided refinement process, the model is able to simulate the conversion behavior during training, effectively addressing the training-inference mismatch that plagues many VC systems.
3. Multi-codebook Progressive Loss Function: The paper proposes a novel loss function that constrains the layer-wise output progressively from coarse to fine-grained features, enhancing the quality of both speaker similarity and content accuracy.

Experimental Results

The model's performance was evaluated using both objective and subjective measures. In intra-lingual zero-shot VC scenarios, Vec-Tok-VC+ significantly outperformed baseline models such as LM-VC and SEF-VC. Notably, it achieved higher scores in naturalness (NMOS), speaker similarity (SMOS), and speaker embedding cosine similarity (SECS), while also demonstrating lower character error rates (CER) and word error rates (WER).

For cross-lingual VC tasks, despite a general degradation in performance across all models, Vec-Tok-VC+ maintained superior results compared to the baselines, reinforcing its robustness in converting speech across different languages.

Practical and Theoretical Implications

The proposed Vec-Tok-VC+ model addresses several critical issues in zero-shot VC:

• Data Efficiency: With the need for only a short target speaker prompt, Vec-Tok-VC+ reduces the dependency on large amounts of target speaker data, making it more practical for applications in diverse and data-scarce environments.
• Improved Decoupling and Feature Matching: The innovative use of residual-enhanced K-Means clustering and dual-mode training strategies improves the separation of speaker and linguistic features, leading to higher quality in converted speech.
• Speaker Timbre and Content Fidelity: By integrating a multi-codebook progressive loss, the model effectively balances the trade-offs between capturing detailed speaker timbre and preserving linguistic content, a common challenge in VC systems.

Speculation on Future Developments

Given the advancements introduced by Vec-Tok-VC+, future research could explore several directions:

• Scalability to Larger Datasets: Testing the model on more extensive and varied speech datasets could uncover further potential in generalizing across even more diverse linguistic landscapes.
• Integration with Real-time Systems: Optimizing the model for real-time applications could enable practical deployments in areas such as live speech translation or assistive technologies.
• Refinement of Teacher-guided Mechanisms: Further refinement of the teacher-guided refinement process could enhance the model's performance and robustness, making it more adaptable to various audio qualities and speaker conditions.

In conclusion, the Vec-Tok-VC+ model represents a significant step forward in zero-shot voice conversion by leveraging innovative strategies to enhance semantic content extraction, reduce training-inference mismatches, and improve overall model robustness. The compelling experimental results indicate its practical efficacy and potential for future enhancements.