# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
###### Abstract  
Voice conversion (VC) is an advanced technology that enables the transformation of raw speech into high-quality audio resembling the target speaker's voice while preserving the original linguistic content and prosodic patterns. In this study, we propose a voice conversion algorithm, Multi-Dimensional Perception Flow Matching (MPFM-VC). Unlike traditional approaches that directly generate waveform outputs, MPFM-VC models the evolutionary trajectory of mel spectrograms with a flow-matching framework and incorporates a multi-dimensional feature perception network to enhance the stability and quality of speech synthesis. Additionally, we introduce a content perturbation method during training to improve the model's generalization ability and reduce inference-time artifacts. To further increase speaker similarity, an adversarial training mechanism on speaker embeddings is employed to achieve effective disentanglement between content and speaker identity representations, thereby enhancing the timbre consistency of the converted speech. Experimental results for both speech and singing voice conversion tasks show that MPFM-VC achieves competitive performance compared to existing state-of-the-art VC models in both subjective and objective evaluation metrics. The synthesized speech shows improved naturalness, clarity, and timbre fidelity in both objective and subjective evaluations, suggesting the potential effectiveness of the proposed approach. v 2025 15 5503 2025 15 5503 13 May 2025 13 Maya mel spectrogram, which is subsequently converted into a waveform [19; 20; 21] by a neural vocoder. While cascaded models often achieve superior audio quality compared with end-to-end systems, they are generally more vulnerable to noise and less robust under domain-shift conditions. Despite progress in the field, several challenges remain, particularly in real-world applications. First, VC systems are highly sensitive to noise, especially when handling low-quality or degraded speech inputs. Second, distributional shifts between the training data and inference environment can lead to severe performance degradation. Third, many current methods struggle to disentangle speaker identity from linguistic content, resulting in feature entanglement and information leakage, which can compromise speaker anonymity or target similarity. To guide our investigation, we formulate the following research hypotheses: (1) Incorporating flow matching into the voice conversion framework is hypothesized to enhance naturalness and stability due to its capacity for continuous distribution alignment. (2) Augmenting training with perturbations in content representations may support improved generalization and robustness, especially under mismatched or noisy conditions. (3) Introducing adversarial disentanglement of speaker features within the flow-matching architecture is expected to help suppress residual identity leakage more effectively, thereby improving speaker similarity in the converted speech. To address these limitations, we propose **MPFM-VC**, a novel **M**ulti-dimensional **P**erception **F**low **M**atching-based voice conversion algorithm. Instead of directly generating speech features or waveforms, MPFM-VC explicitly models the dynamic distribution transfer of speech representations, thereby improving robustness and audio quality under mismatched or noisy conditions. Specifically, MPFM-VC introduces the following core contributions:  
* **Multi-dimensional perception flow matching. ** We propose a flow-matching-based generation framework for voice conversion that integrates multi-dimensional acoustic features--such as pitch, energy, and prosody--into the flow trajectory modeling process. This design is expected to improve robustness and facilitate more fine-grained and stable mel-spectrogram synthesis. * **Content perturbation training.