Researchers introduce RIVET, a training framework that uses idempotency constraints to improve voice attribute editing models' robustness to noisy or inconsistent labels in large-scale speech datasets. By enforcing the property that repeated applications produce identical results, the method acts as an implicit regularizer that reduces sensitivity to mislabeled training data while preserving speaker identity.
RIVET addresses a fundamental challenge in speech processing: training conditional generative models when annotation quality is inconsistent. Large-scale voice datasets often contain mislabeled or contradictory attribute information, which destabilizes models attempting to modify characteristics like age and gender. The idempotency principle—borrowed from mathematics and systems design—offers an elegant solution by constraining the model's behavior such that applying the same transformation twice yields no additional change. This mathematical constraint naturally penalizes the model for learning from noisy examples, effectively filtering out inconsistent training signals without explicit label correction.
The approach represents a meaningful advance in speech processing robustness. Rather than tackling label noise through data cleaning or sophisticated label correction algorithms, RIVET leverages a structural property that improves generalization. This technique has broader implications for conditional generative models across domains where large datasets contain inevitable annotation errors. The evaluation on both controlled synthetic noise and the naturally noisy GLOBE dataset demonstrates practical applicability rather than merely theoretical elegance.
For speech technology developers and AI researchers, RIVET offers a practical framework for improving model stability without requiring expensive data curation or manual annotation reviews. The method's ability to preserve speaker identity while editing attributes directly impacts voice conversion applications in entertainment, accessibility, and speech synthesis. As datasets scale and annotation consistency remains difficult to maintain, implicit regularization techniques like idempotency become increasingly valuable for production systems.
- →RIVET uses idempotency constraints as an implicit regularizer to improve robustness to noisy voice attribute labels.
- →The framework achieves better speaker identity preservation and editing success compared to standard training approaches.
- →Idempotent operators naturally filter sensitivity to mislabeled examples without explicit label correction mechanisms.
- →The method shows effectiveness on both synthetic controlled noise and naturally inconsistent real-world dataset annotations.
- →This approach has broader applicability to conditional generative models across domains with imperfect training data.