RIVET: Robust Idempotent Voice Attribute Editing
📰 ArXiv cs.AI
Learn how RIVET improves voice attribute editing with idempotency, making it robust to noisy labels
Action Steps
- Apply idempotency to voice attribute editing models to improve robustness
- Use RIVET to modify voice attributes such as age and gender while preserving speaker identity
- Evaluate the performance of RIVET on large-scale speech datasets with noisy annotations
- Compare the results of RIVET with other voice attribute editing models
- Implement RIVET in a speech generation pipeline to improve the quality of edited voices
Who Needs to Know This
Speech recognition and generation teams can benefit from RIVET to improve the robustness of their models, especially when dealing with large-scale datasets with noisy annotations
Key Insight
💡 Idempotency can improve the robustness of voice attribute editing models to noisy labels
Share This
🗣️ Improve voice attribute editing with RIVET, a robust idempotent model for noisy labels 📊
Key Takeaways
Learn how RIVET improves voice attribute editing with idempotency, making it robust to noisy labels
Full Article
Title: RIVET: Robust Idempotent Voice Attribute Editing
Abstract:
arXiv:2606.19629v1 Announce Type: cross Abstract: Voice attribute editing models modify characteristics such as age and gender while preserving speaker identity. In large-scale speech datasets, however, attribute annotations are often noisy or inconsistent, which can cause conditional generative models to produce unstable edits. In this work, we show that idempotency provides an effective mechanism for improving robustness to noisy labels. An idempotent operator is one for which repeated applica
Abstract:
arXiv:2606.19629v1 Announce Type: cross Abstract: Voice attribute editing models modify characteristics such as age and gender while preserving speaker identity. In large-scale speech datasets, however, attribute annotations are often noisy or inconsistent, which can cause conditional generative models to produce unstable edits. In this work, we show that idempotency provides an effective mechanism for improving robustness to noisy labels. An idempotent operator is one for which repeated applica
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