# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Experimental Results  
**(1) Quality evaluation of voice conversion**  
The primary goals of the voice conversion (VC) task are to transform the speaker's timbre while preserving the original linguistic content and maximize the naturalness and intelligibility of the generated speech. To this end, we evaluated the proposed method on the speech test set by using both subjective and objective metrics. Subjective evaluation was conducted based on Mean Opinion Score (MOS) tests, while objective performance was quantified by using the Mel Cepstral Distortion (MCD) and Word Error Rate (WER). This combination provided a comprehensive assessment of the effectiveness of different voice conversion approaches. As shown in Table 1, the proposed MPFM-VC algorithm achieved the highest scores among the evaluated methods in terms of naturalness (MOS), audio fidelity (MCD), and speech clarity (WER). Compared with existing models, MPFM-VC exhibited relatively consistent performance across multiple evaluation metrics, suggesting improved robustness under varying data conditions. In particular, MPFM-VC showed an \(11.57\%\) increase in MOS compared to Free-VC, which may reflect better handling of speech continuity and prosodic variation while reducing artifacts commonly associated with end-to-end VITS-based frameworks. Compared with diffusion-based models such as Diff-VC and DDDM-VC, MPFM-VC reported lower MCD and WER values, indicating that it may better preserve semantic content and enhance the intelligibility of the converted speech. Furthermore, compared to Voiceflow-VC, a recent baseline based on rectified flow matching, MPFM-VC achieved marginal improvements across all three evaluation metrics. Although Voiceflow-VC already produces competitive results in terms of synthesis quality and efficiency, the inclusion of multi-dimensional feature modeling and content perturbation in MPFM-VC may contribute to enhanced spectral accuracy and prosodic consistency, which could lead to improved intelligibility and speaker similarity. Overall, these results suggest that the proposed architectural components in MPFM-VC--particularly the integration of auxiliary acoustic features and robustness-aware training strategies--may enhance the model's adaptability to various acoustic conditions and speaker characteristics. **(2) Timbre similarity evaluation of voice conversion**  
The goal of speaker similarity evaluation is to assess a voice conversion method's ability to preserve the timbre consistency of the target speaker. In this study, we adopted two metrics for analysis: Similarity MOS (SMOS) and Speaker Embedding Cosine Similarity (SECS). As shown in Table 2 and Figure 8, MPFM-VC exhibited relatively high speaker consistency in the speaker similarity evaluation task, achieving the highest SMOS (3.83) and SECS (0.84) scores. This suggests that MPFM-VC may better preserve the timbral characteristics of the target speaker during conversion. Compared with Free-VC, MPFM-VC enhanced the model's adaptability to target speaker embeddings through multi-dimensional feature perception modeling, thereby improving post-conversion timbre similarity. Although Diff-VC benefited from diffusion-based generation, which improved overall audio quality to some extent, it failed to sufficiently disentangle speaker identity features, resulting in residual characteristics from the source speaker in the converted speech. While DDDM-VC introduced feature disentanglement mechanisms that improved speaker similarity, it still fell short compared with MPFM-VC. MPFM-VC showed slightly improved results over Voiceflow-VC by incorporating adversarial training tailored to speaker embeddings, which more effectively suppressed residual speaker leakage and refined speaker identity alignment.