# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Experimental Results
timbral variation with a multi-dimensional feature-aware flow-matching mechanism, potentially supporting more stable and consistent timbre rendering throughout the conversion process. **(6) Student's \(t\)-test of singing voice conversion**  
To further validate the significance of our model's performance in the singing voice conversion task, we conducted pairwise Student's t-tests on subjective evaluation metrics (MOS and SMOS). As shown in Figure 12, most p-values were significantly below 0.05, indicating that the differences in perceived speech quality and timbre similarity between MPFM-VC and other baselines were statistically significant. Notably, MPFM-VC consistently achieved statistically better results over all compared methods in both metrics. **(7) Robustness Evaluation under Low-Quality Conditions**  
In real-world applications, voice conversion systems must exhibit robustness to low-quality input data in order to maintain reliable performance under adverse conditions such \begin{table}
\begin{tabular}{c c c} \hline \hline
**Evaluated Models** & **SMOS**\((\uparrow)\) & **SECS**\((\uparrow)\) \\ \hline Free-VC & \(3.32\pm 0.05\) & 0.67 \\ Diff-VC & \(3.42\pm 0.04\) & 0.72 \\ DDDM-VC & \(3.57\pm 0.05\) & 0.77 \\ Voiceflow-VC & \(3.77\pm 0.05\) & 0.79 \\ \hline MPFM-VC (ours) & \(3.79\pm 0.03\) & 0.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of singing voice conversion timbre similarity evaluation. Figure 11: Results of singing voice conversion timbre similarity evaluation. as background noise, limited recording hardware, or unclear articulation by the speaker. To assess this capability, we additionally collected a set of 30 low-quality speech samples that incorporated common noise-related challenges, including mumbling, background reverberation, ambient noise, signal clipping, and low-bitrate encoding. As shown in Table 5 and the spectrograms in Figure 13, MPFM-VC maintained stable performance even under low-quality speech conditions. The generated spectrograms retained clear structural patterns, suggesting preserved synthesis quality despite degraded inputs, whereas other systems showed more degradation in terms of naturalness and timbre consistency. Diffusion-based models, such as Diff-VC and DDDM-VC, exhibited reduced performance under noise perturbation. Their spectrograms appeared blurred, and metrics such as MCD and WER indicated degradation. For instance, Diff-VC resulted in the highest WER (35.20%) and MCD (8.85), pointing to its sensitivity to noisy inputs. Voiceflow-VC, using rectified flow matching, showed relatively better robustness compared to diffusion-based baselines, achieving higher MOS (3.12) and SMOS (3.34) than Free-VC and DDDM-VC. However, its performance remained slightly lower than that of MPFM-VC in terms of intelligibility and timbre similarity. MPFM-VC achieved the highest scores across all evaluation metrics--including WER (15.37%), MCD (6.89), SMOS (3.53), and SECS (0.73). These results suggest that multi-dimensional feature modeling and content perturbation strategies may enhance resilience to noisy conditions and support more consistent and intelligible voice conversion in practical scenarios. It is worth noting that although Free-VC showed relatively weaker performance in previous experiments, it still performed more reliably than diffusion-based architectures under low-quality speech conditions. Its generated spectrograms exhibited limited blurring, which may suggest some noise tolerance associated with the end-to-end variational autoencoder (VAE)-based modeling approach. However, its SECS score remained notably lower than that of MPFM-VC, suggesting possible challenges in preserving accurate timbre. Voiceflow-VC, which leverages rectified flow matching for generation, showed improved robustness compared to diffusion-based models such as Diff-VC and DDDM-VC. It achieved competitive intelligibility and timbre similarity scores, and its spectrograms appeared more consistent under noise.