# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 4 Methods
### Adversarial Training Mechanism Based on Voiceprints  
In voice conversion (VC) models, a core goal of the content encoder is to extract speaker-independent linguistic representations. However, due to the frequent reuse of the same speaker for both target and reference signals during training, the encoder often inadvertently captures speaker-specific cues. This speaker information leakage can result in generated speech that retains residual timbral characteristics of the reference speaker, ultimately reducing naturalness and target speaker fidelity.  
To mitigate this issue, we adopted an adversarial disentanglement strategy to explicitly suppress voiceprint information in the content representation. This approach, originally introduced in prior VC models such as StarGAN-VC [47] and CycleGAN-VC [48], has proven effective in decoupling speaker identity from linguistic content. In our work, we adapted this established technique to the flow-matching framework, integrating a speaker classifier trained adversarially against the content encoder. This enabled the model to better isolate linguistic information while preserving control over the target speaker's timbre in the final generation stage.  
As illustrated in Figure 7, the overall adversarial training framework consisted of three main components: the content encoder (\(E_{content}\)) from the flow-matching module, a gradient reversal layer (GRL), and a speaker classifier (\(Classifier_{spk}\)). Specifically, within the acoustic model, the input content representation, \(I_{content}\), was first processed by the content encoder, \(Encoder_{content}\), to generate a speaker-independent content embedding:  
\[E_{content}=\text{Encoder}_{content}(I_{content}) \tag{10}\]  
To disentangle speaker-related information from the content encoder, we designed a speaker classifier composed of three linear layers with ReLU activation functions. This classifier aimed to predict the speaker identity, \(P_{spk}\), from the content embedding, \(E_{content}\), and guided the training process by comparing the prediction with the ground-truth speaker embedding, \(I_{spk}\).  
To enforce feature disentanglement, a gradient reversal layer (GRL) was inserted before the speaker classifier. During forward propagation, the GRL passed the content representation unchanged. However, during backpropagation, it reversed the gradient direction and scaled it by a tunable adversarial coefficient \(\lambda\):  
\[\frac{\partial L_{spk}}{\partial I_{content}}\leftarrow-\lambda\cdot\frac{ \partial L_{spk}}{\partial I_{content}} \tag{11}\]  
where \(\lambda\) denotes the adversarial coefficient, which controls the strength of gradient reversal.  
The speaker classifier, \(Classifiersb\), was optimized based on this mechanism to accurately predict the speaker identity by minimizing the loss, \(L_{spk}\), while the content encoder, \(Encoder_{content}\), was trained adversarially to maximize that loss--effectively preventing the classifier from extracting speaker-specific cues. This adversarial game facilitates the disentanglement of speaker information from content embeddings. The final training objective was defined as  
\[\min_{Encoder_{content}}\max_{Classifier_{spk}}L_{spk}=\frac{P_{spk}\cdot I_{ spk}}{|P_{spk}|\times|I_{spk}|} \tag{12}\]  
Figure 7: Adversarial training mechanism based on voiceprint.