# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Ablation Experiments  
**(1) Prosodic feature verification in multi-dimensional perception network**  
In this study, we refined the existing Classifier-Free Guidance (CFG) inference strategy, as shown in Equation (7), by increasing the weight of prosodic features in the MDPN. This adjustment enabled a more effective analysis of the impact of prosody on the generated speech through spectrogram-based evaluations. We set the weight of the prosodic matrix in the prosody-based CFG to 0.5 to compare its effect with the version without prosody CFG. As illustrated in the Figure 14, we randomly selected two pairs of singing samples for a more intuitive comparison. In the versions with prosody CFG, the high-frequency regions appeared more enriched, indicating a greater vocal openness and increased vocal brightness. Additionally, the spectrogram showed brighter formant structures for each syllable, suggesting that the prosody CFG enhanced the emphasis and articulation of stressed syllables. **(2) Content perturbation-based training enhancement method**  
In the voice conversion task, the content perturbation-based training augmentation strategy was designed to improve model generalization by introducing controlled perturbations to content representations during training. That approach aimed to reduce inference-time artifacts such as unexpected noise and improve the overall stability of the converted speech. To validate the effectiveness of this method, we conducted an ablation study by removing the content perturbation mechanism and observing its impact on speech conversion performance. In addition, we also applied the methods of two related works to our MPFM-VC as the baseline. SpecAugment [51] randomly adds a mask to the input mel spectrogram, and Maskcyclegan-VC [52] uses a filling in frames (FIF) method to mask the  
Figure 13: Spectrograms under low-quality conditions. (**a**) Free-VC, (**b**) Diff-VC, (**c**) DDDM-VC, (**d**) Voiceflow-VC, (**e**) MPFM-VC. mel spectrogram, which is similar to the method in this paper. The difference lies in that it enhances the spectrum, while the method in this paper enhances the content representation. A test set consisting of 100 out-of-distribution audio samples with varying durations was used to simulate real-world scenarios under complex conditions. The following evaluation metrics were determined: MOS, MCD, WER, SMOS, SECS, and the frequency of plosive artifacts per 10 s in converted speech (Badcase). As shown in Table 6, the content perturbation-based training augmentation appeared to contribute to improved stability and robustness in the voice conversion model. When this component was removed, the Badcase rate increased noticeably (from 0.39 to 1.52 per 10 s), suggesting a higher occurrence of artifacts such as plosive noise or interruptions in more acoustically challenging cases. Additionally, MCD and WER both rose slightly, which may indicate reduced fidelity and intelligibility. Interestingly, the MOS improved slightly in the absence of perturbation, possibly due to better in-domain fitting, though this came at the cost of generalization across longer \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Evaluated Metrics** & **MPFM-VC** & **MPFM-VC** & **MPFM-VC** & **MPFM-VC** \\  & **(w/CP)** & **(w/o CP)** & **(w/SpecAugment)** & **(w/FIF)** \\ \hline Badcase & 0.39 & 1.52 & 21.23 & 0.42 \\ MOS & \(3.85\pm 0.04\) & \(3.92\pm 0.05\) & \(3.22\pm 0.03\) & \(3.75\pm 0.04\) \\ MCD & 6.32 & 6.47 & 7.82 & 6.82 \\ WER & 3.87\% & 3.92\% & 11.94\% & 3.94\% \\ SMOS & \(3.79\pm 0.06\) & \(3.72\pm 0.04\) & \(3.41\pm 0.05\) & \(3.81\pm 0.04\) \\ SECS & 0.81 & 0.82 & 0.63 & 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of ablation experiments for content perturbation-based training enhancement method. Figure 14: Prosodic feature verification in multi-dimensional perception network. (**a**) With prosodic CFG voice 1, (**b**) without prosodic CFG voice 1, (**c**) with prosodic CFG voice 2, (**d**) without prosodic CFG voice 2.  
or mismatched utterances.