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 application does not change the result, i.e., f(f(x)) = f(x). Enforcing this property acts as an implicit r
The proliferation of conditional generative models in AI and the inherent noisiness of real-world speech datasets necessitate more robust editing mechanisms.
Improving voice attribute editing robustness allows for more reliable and controlled synthetic media generation, with implications for accessibility, entertainment, and digital identity.
Conditional generative AI models for voice attributes can now be developed with greater resilience to imperfect training data, leading to more stable and predictable outputs.
- · AI researchers in generative models
- · Synthetic voice developers
- · Media and entertainment industry
- · Accessibility technology providers
More consistent and higher-quality voice attribute transfer and editing become achievable.
The ability to generate or modify voices for specific applications (e.g., dubbing, virtual assistants) becomes more reliable and scalable.
Enhanced realism in synthetic speech could further blur the lines between human and AI-generated content, raising new ethical considerations.
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