RIVET: Robust Idempotent Voice Attribute Editing
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 application does not change the result, i.e., f(f(x)) = f(x). Enforcing this property acts as an implicit regularizer that reduces sensitivity to mislabeled examples. We introduce RIVET, a training framework that incorporates an idempotency objective to ...
| Authors | Dareen Alharthi, Bhuvan Koduru, Rita Singh et al. |
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| Primary category | cs.SD |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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