# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Experimental Results
Diffusion-based methods like Diff-VC and DDDM-VC tended to introduce some degree of over-smoothing, which may affect fine acoustic detail retention. Voiceflow-VC, as a recent flow-matching approach, achieved competitive performance (MOS: 3.93, WER: 5.56%), showing effective modeling of singing voices. However, MPFM-VC reported consistently higher scores across all metrics, potentially due to the integration of multi-dimensional conditioning and perturbation training. Additionally, we randomly selected a singing voice segment for spectrogram comparison, as shown in Figure 10. The spectrogram generated by MPFM-VC appeared sharper and more structured, which may be attributed to the multi-dimensional perception network that enabled a more accurate reconstruction of detailed features such as vibrato and articulation. Voiceflow-VC also showed strong performance in maintaining temporal and \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluated Models** & **MOS (\(\uparrow\))** & **MCD (\(\downarrow\))** & **WER (\(\downarrow\))** \\ \hline GT & \(4.39\pm 0.04\) & - & 2.12\% \\ Free-VC & \(3.52\pm 0.04\) & 7.09 & 18.28\% \\ Diff-VC & \(3.73\pm 0.05\) & 6.99 & 13.27\% \\ DDDM-VC & \(3.81\pm 0.04\) & 6.64 & 7.24\% \\ Voiceflow-VC & \(3.93\pm 0.04\) & 6.44 & 5.56\% \\ \hline MPFM-VC (ours) & \(4.12\pm 0.06\) & 6.32 & 4.86\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of singing voice conversion quality evaluation. Figure 9: Student’s \(t\)-test of voice conversion. (**a**) \(p\)-value heatmap of MOS, (**b**) \(p\)-value heatmap of SMOS. spectral structure, outperforming Free-VC and diffusion-based models in some aspects. However, it captured slightly fewer high-frequency harmonics and transient variations compared to MPFM-VC, which may be important for expressive singing voice synthesis. These findings support the potential of the proposed method in producing perceptually improved results in singing voice conversion. **(5) Timbre similarity evaluation of singing voice conversion**  
Timbre similarity in singing voices is a critical metric for evaluating a model's ability to preserve the target singer's vocal identity during conversion. Compared with normal speech, singing involves more complex pitch variations, formant structures, prosodic patterns, and timbre continuity, which pose additional challenges for accurate speaker similarity modeling. In this study, we performed a comprehensive evaluation by using both subjective SMOS (Similarity MOS) and objective SECS (Speaker Embedding Cosine Similarity) to assess the effectiveness of different methods in capturing and preserving timbral consistency in singing voice conversion. As shown in Table 4 and Figure 11, MPFM-VC achieved superior performance in singing voice timbre similarity evaluation. It achieved higher scores in both SMOS (3.79) and SECS (0.81) than the compared baselines, suggesting improved preservation of the target singer's timbral identity. In singing voice conversion, Free-VC and Diff-VC showed limitations in disentangling content and speaker representations, which may result in timbre mismatches with the target voice. Although the diffusion-based DDDM-VC model partially solved this issue, it continued to exhibit timbre smoothing effects, which may reduce the perceptual uniqueness of the generated singing voices. Voiceflow-VC showed improved timbre consistency over these baselines and achieved competitive SMOS and SECS scores. However, it achieved slightly lower scores than MPFM-VC, especially in cases requiring preservation of fine speaker-specific details during dynamic singing phrases. In contrast, MPFM-VC incorporated an adversarial speaker disentanglement strategy, which helped reduce residual source speaker cues and contributed to improved alignment with the target singer's timbre. Additionally, MPFM-VC enabled the fine-grained modeling of  
Figure 10: Spectrogram comparison for the same voice input. (**a**) Free-VC, (**b**) Diff-VC, (**c**) DDDM-VC, (**d**) Voiceflow-VC, (**e**) MDFM-VC.