# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Ablation Experiments
Meanwhile, the SMOS and SECS remained largely stable, implying that the perturbation strategy primarily contributed to robustness rather than timbre consistency. Compared to the traditional SpecAugment method, which yielded inferior results likely due to random masking, the proposed strategy offered more structured perturbation. Similarly, the FIF approach from MaskCycleGAN-VC also enhanced robustness, though it resulted in slight reductions in MOS and MCD. Nevertheless, FIF performed comparably to our approach in content preservation and timbre similarity under certain conditions. Overall, the results suggest that the content perturbation strategy provides meaningful improvements in stability and generalization, particularly under noisy or out-of-domain scenarios, and can be a valuable addition for real-world voice conversion systems. **(3) Adversarial training mechanism based on voiceprint**  
In the voice conversion task, the strategy of adversarial training on speaker embeddings was designed to enhance the timbre similarity to the target speaker while suppressing residual speaker identity information from the source speaker. This ensured that the converted speech better matched the target speaker's voice without compromising the overall speech quality. To evaluate the effectiveness of that strategy, we conducted an ablation study by removing the adversarial training module and comparing its impact on timbre similarity and overall speech quality in the converted outputs. Moreover, we incorporated the conditioning strategy from Voicebox [36] into our baseline model MPFM-VC. The method involved using the mel spectrogram of a target speaker as a prompt, which was injected into each perceptual block to enhance speaker similarity. As shown in Table 7, the speaker adversarial training module appeared to contribute substantially to timbre matching in the voice conversion task. When this component was removed, both SMOS and SECS scores decreased--from 3.83 to 3.62 and from 0.84 to 0.73, respectively--suggesting reduced alignment with the target speaker's timbre and an increase in residual characteristics from the source speaker. In contrast, the MOS and MCD metrics remained largely unchanged, indicating that the adversarial mechanism had a limited impact on overall audio quality. Interestingly, WER showed a slight improvement, which may reflect enhanced intelligibility. However, this change could be associated with a loss of timbre specificity, as the model could default to producing more neutral or averaged vocal characteristics. While higher speaker similarity could be achieved with Voicebox-based methods, they relied on the mel spectrogram of a reference utterance, which might introduce unrelated acoustic information and affect both quality and intelligibility. Overall, adversarial training helped improve timbre consistency by suppressing residual speaker identity cues in the content representation. While some perceptual quality scores improved when this component was omitted, these gains came at the expense of precise speaker identity transfer. Therefore, in practical applications, adversarial training \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Evaluated Metrics** & **MPFM-VC (w/VA)** & \
\begin{tabular}{c} **MPFM-VC** \\ **(w/o VA)** \\ \end{tabular} &
\begin{tabular}{c} **MPFM-VC** \\ **(w/o Voicebox)** \\ \end{tabular} \\ \hline MOS & \(4.05\pm 0.03\) & \(3.82\pm 0.04\) & \(3.72\pm 0.03\) \\ MCD & 6.23 & 6.37 & 6.82 \\ WER & 4.23\% & 3.72\% & 5.11\% \\ SMOS & \(3.83\pm 0.05\) & \(3.62\pm 0.06\) & \(3.92\pm 0.04\) \\ SECS & 0.84 & 0.73 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Ablation experimental results of adversarial training method for voiceprint features. remains essential to achieving high-quality voice conversion, ensuring that the generated speech is not only intelligible but also accurately timbre-matched to the intended target speaker.