ECAPA-TDNN embedding geometry impacts voice conversion quality
Understanding Hyperspherical Geometry of ECAPA-TDNN Embedding and Its Impact on Zero-Shot Voice Conversion
SoundArtificial Intelligence
Summary
Voice conversion systems rely on models that recognize different speakers by encoding their voices. The authors studied the underlying geometry of these speaker models called ECAPA-TDNN, which represent speakers as points on a high-dimensional sphere. They found that standard training tends to cluster these points too closely, limiting the system’s ability to handle new speakers. To improve this, the authors tried new ways to spread the points more evenly, which helped make voice conversion work better for speakers the system had never heard before.
What this means in practice
- •For voice technology engineers: Improve zero-shot voice conversion systems by enhancing speaker encoder robustness to unseen speakers using geometric regularization methods on ECAPA-TDNN embeddings.$Commercial implications: This enables more reliable voice transformation products for personalized speech synthesis and voice assistants.
- •For speech recognition developers: Optimize speaker recognition models by controlling the geometric arrangement of classifier prototypes to balance accuracy and robustness.
Authors
Mathilde Abrassart, Nicolas Obin, Axel Roebel
Abstract
Angular-margin speaker encoders are widely used in voice conversion, yet the geometry of their classifier prototypes remains poorly understood. We analyze ECAPA-TDNN classifier prototypes as points on the unit hypersphere and characterize their organization using rotation-invariant angular statistics together with global and local effective dimensionality measures. Our analysis shows that standard training can induce angular concentration and a substantial reduction in effective dimensionality. To address this, we investigate two geometric regularization strategies (hinged Riesz log-energy and effective-dimension maximization) applied to classifier prototypes to encourage more uniform hyperspherical coverage. The resulting prototype sets exhibit higher effective dimensionality and improved isotropy, with configuration-dependent effects on speaker-recognition performance. When the corresponding ECAPA-TDNN models are used as speaker encoders for Fast-VGAN, the regularized systems also exhibit improved robustness in zero-shot voice conversion, particularly for previously unseen speakers.