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RIVET framework enhances voice editing robustness with idempotency objective

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new training framework called RIVET to improve the robustness of voice attribute editing models. This framework incorporates an idempotency objective, which ensures that repeated application of an editing function yields the same result, thereby reducing sensitivity to noisy or inconsistent attribute annotations in large-scale speech datasets. Evaluations on controlled label noise and the GLOBE dataset demonstrate that RIVET enhances editing success and better preserves speaker identity compared to standard training methods. AI

IMPACT Improves the reliability of voice editing tools by addressing issues with noisy data.

RANK_REASON Academic paper detailing a new method for voice attribute editing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

RIVET framework enhances voice editing robustness with idempotency objective

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Academic paper detailing a new method for voice attribute editing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Dareen Alharthi, Bhuvan Koduru, Rita Singh, Bhiksha Raj ·

    RIVET: Robust Idempotent Voice Attribute Editing

    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 condit…

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