# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Experimental Results
\begin{table}
\begin{tabular}{c c c c} \hline
**Evaluated Models** & **MOS (\(\uparrow\))** & **MCD (\(\downarrow\))** & **WER (\(\downarrow\))** \\ \hline GT & \(4.32\pm 0.05\) & - & 1.79\% \\ Free-VC & \(3.63\pm 0.04\) & 7.11 & 13.28\% \\ Diff-VC & \(3.87\pm 0.04\) & 6.79 & 11.27\% \\ DDDM-VC & \(3.91\pm 0.05\) & 6.44 & 5.24\% \\ Voiceflow-VC & \(3.98\pm 0.04\) & 6.37 & 4.37\% \\ \hline MPFM-VC (ours) & \(4.05\pm 0.05\) & 6.23 & 4.23\% \\ \hline \end{tabular}
\end{table} 
Table 1: Results of voice conversion quality evaluation. These findings suggest that the combination of flow-based modeling and speaker-guided adversarial training in MPFM-VC may support improved speaker similarity while maintaining acceptable naturalness and synthesis stability in voice conversion tasks. **(3) Student's \(t\)-test of voice conversion**  
To validate the reliability of the subjective evaluation results, we conducted two-sample Student's \(t\)-tests between each pair of evaluated systems for both MOS and SMOS metrics. In the \(p\)-value heatmaps, lower (blue) values indicate stronger statistical significance. As shown in Figure 9, for MOS, our proposed method MPFM-VC exhibited statistically significant improvements (\(p<0.05\)) over all baseline models, including Free-VC, Diff-VC, DDDM-VC, and Voiceflow-VC. The p-values were all extremely small (e.g., 2.3 \(\times\) 10\({}^{-69}\) between MPFM-VC and DDDM-VC), confirming that the perceived improvements in naturalness were not due to chance. Similarly, for SMOS, MPFM-VC also achieved statistically significant differences against all other systems, with p-values often smaller than \(1\times 10^{-100}\). This reinforces the claim that our method provides more consistent and perceptually closer timbre to the target speaker than competing approaches. Notably, even between Voiceflow-VC and other strong baselines like DDDM-VC, the differences were statistically significant, which highlighted the effectiveness of flow-matching-based methods in general. However, MPFM-VC still surpassed Voiceflow-VC with clear statistical confidence. These results demonstrated that the performance gains observed in MOS and SMOS were both consistent and statistically significant, further supporting the effectiveness of our proposed multi-dimensional perception flow-matching strategy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluated Models** & **SMOS \((\uparrow)\)** & **SECS \((\uparrow)\)** \\ \hline Free-VC & 3.28 \(\pm\) 0.05 & 0.72 \\ Diff-VC & 3.51 \(\pm\) 0.03 & 0.79 \\ DDDM-VC & 3.67 \(\pm\) 0.04 & 0.81 \\ Voiceflow-VC & 3.79 \(\pm\) 0.04 & 0.82 \\ \hline MPFM-VC (ours) & 3.83 \(\pm\) 0.04 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of voice conversion timbre similarity evaluation. Figure 8: Results of voice conversion timbre similarity evaluation. **(4) Quality evaluation of singing voice conversion**  
In the context of voice conversion, singing voice conversion is generally more challenging than standard speech conversion due to its inherently richer pitch variations, timbre stability, and prosodic complexity. We adopted the same set of evaluation metrics used in speech conversion to comprehensively evaluate the performance of different models on the singing voice conversion task, including the subjective Mean Opinion Score (MOS) and objective indicators such as Mel Cepstral Distortion (MCD) and the Word Error Rate (WER). As shown in Table 3, MPFM-VC achieved strong results on the singing voice conversion task, with a MOS of 4.12, an MCD of 6.32, and a WER of 4.86%. Through multi-dimensional feature perception modeling, MPFM-VC adapted to melodic variations and pitch fluctuations typically found in singing voices, producing outputs that exhibited improved fluency and preserve audio clarity. Compared with Free-VC, MPFM-VC achieved slightly better naturalness scores, possibly due to its use of flow matching, which may enhance dynamic feature modeling.