SSL-GMMVC: Interpretable Voice Conversion via Locally Linear GMM Transforms in Self-Supervised Representation Space
Abstract
We introduce SSL-GMMVC, an interpretable voice conversion method in self-supervised speech space. The method models paired source-target features with a Gaussian mixture model and performs conversion as a posterior-weighted sum of affine transforms. This yields locally linear transformations that adapt to heterogeneous feature-space structure while remaining analytically tractable. Through objective and subjective evaluations, we show that SSL-GMMVC improves speaker similarity with comparable intelligibility and naturalness, and that even a constrained covariance variant surpasses a deep learning baseline as the number of mixture components increases. Further analyses link component selection to phonetic structure and reveal interpretable scaling and rotation in the learned transforms. These findings highlight SSL-GMMVC as an effective, analyzable framework for voice conversion.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents SSL-GMMVC, an innovative voice conversion method that enhances speaker similarity while maintaining intelligibility and naturalness. The comprehensive evaluation of its performance and the interpretability of its transformations contribute significantly to the field of audio processing and machine learning.
Methodology
The proposed SSL-GMMVC method utilizes Gaussian Mixture Models (GMMs) to perform voice conversion in a self-supervised representation space. It innovatively replaces the single global mapping of previous methods with locally linear transformations, allowing for a more nuanced adaptation to the structure of the feature space. The methodology is well-structured, with clear definitions of the model architecture, learning process, and inference mechanisms. The use of both objective and subjective evaluations strengthens the methodology, providing a comprehensive understanding of the model's performance.
Results & evaluation
The experiments are rigorously designed, comparing SSL-GMMVC against both traditional and deep learning baselines across various configurations. The dataset is appropriate, comprising American English speech from the CMU ARCTIC corpus, and the evaluation metrics (EER for speaker similarity, WER for intelligibility, and MOS for naturalness) are relevant and robust. The results demonstrate that SSL-GMMVC outperforms LinearVC in specific settings and shows competitive performance against FreeVC, indicating its effectiveness in voice conversion tasks.
Reproducibility
The paper provides sufficient implementation details, including the extraction of SSL features and the training process for the GMM. The authors have made the code publicly available on GitHub, which enhances reproducibility. However, the paper could benefit from more detailed descriptions of the experimental setup and hyperparameter choices to facilitate replication.
Limitations
One limitation noted is the challenge of scaling the number of mixture components while maintaining stable parameter estimation in high-dimensional SSL space. Additionally, while the analysis of transformation matrices provides insights, it does not establish clear correspondences between rotational planes across speaker pairs, which could limit interpretability.
Broader impact
The findings from this research have significant implications for applications in voice conversion, including anonymization, language learning, and assistive technologies. The interpretable nature of the model could lead to advancements in understanding and manipulating speech features, potentially influencing future research in speech synthesis and transformation. The paper presents SSL-GMMVC, an innovative voice conversion method that enhances speaker similarity while maintaining intelligibility and naturalness. The comprehensive evaluation of its performance and the interpretability of its transformations contribute significantly to the field of audio processing and machine learning.
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