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arxiv: 2606.19629 · v1 · pith:6VRHLFQOnew · submitted 2026-06-17 · 💻 cs.SD · cs.AI· cs.LG

RIVET: Robust Idempotent Voice Attribute Editing

Dareen Alharthi , Bhuvan Koduru , Rita Singh , Bhiksha Raj This is my paper

Pith reviewed 2026-06-26 18:49 UTC · model grok-4.3

classification 💻 cs.SD cs.AIcs.LG
keywords voice attribute editingidempotencylabel noise robustnessspeaker identity preservationconditional generative modelsspeech processingrobust training
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The pith

Enforcing idempotency during training makes voice attribute editing models less sensitive to noisy labels.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that voice attribute editing models can be made more robust to noisy or inconsistent attribute annotations by adding an idempotency objective to training. Idempotency requires that applying the editing function twice produces the same result as applying it once. This property functions as an implicit regularizer that reduces the model's dependence on mislabeled examples. The authors present RIVET as the framework implementing this objective and report improved editing success along with stronger speaker identity preservation relative to standard training, both under controlled noise and on the GLOBE dataset with natural annotation inconsistencies.

Core claim

The central claim is that idempotency provides an effective mechanism for improving robustness to noisy labels in conditional generative models for voice attribute editing. An idempotent operator satisfies f(f(x)) = f(x), and enforcing this property during training reduces sensitivity to mislabeled examples, yielding more stable edits that better preserve speaker identity than models trained without the objective.

What carries the argument

The idempotency objective, which enforces that repeated application of the attribute editing function produces no further change.

If this is right

  • Models trained with the objective become less sensitive to mislabeled attribute annotations.
  • Editing success rates rise compared with standard training under both synthetic and natural noise.
  • Speaker identity is preserved more reliably across repeated or noisy edits.
  • The same regularizer works on datasets with naturally occurring annotation inconsistencies.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same objective might stabilize other conditional generation tasks that rely on noisy attribute labels.
  • Idempotency training could serve as a lightweight alternative to explicit noise modeling or data cleaning pipelines.
  • The approach might interact with convergence behavior or hyperparameter choices in ways the current experiments do not measure.

Load-bearing premise

An idempotency objective can be added to the training of conditional generative models for voice editing without degrading performance on clean data or introducing optimization instabilities.

What would settle it

A controlled experiment showing that models trained with the idempotency objective achieve no higher editing success or speaker similarity than standard models when both are tested on datasets containing verified label noise would falsify the central claim.

Figures

Figures reproduced from arXiv: 2606.19629 by Bhiksha Raj, Bhuvan Koduru, Dareen Alharthi, Rita Singh.

Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Performance under increasing label noise on the EARS dataset. Models are trained on 7 hours of EARS with increasing noise levels and evaluated on a balanced 1-hour test set. We report cosine similarity between Titanet embeddings of the original and reverted speech (left) and attribute accuracy (right). RIVET maintains higher identity similarity and more stable performance than the baseline as noise increas… view at source ↗
Figure 3
Figure 3. Figure 3: Human evaluation of age and gender voice editing. Each sample was rated by five annotators with majority voting. RIVET improves editing success over the baseline. ECAPA classification losses, the flow likelihood loss, and the idempotency regularizer applied to both the speaker and speech encoders: Ltotal = LVITS+λfLflow+λaLage+λgLgender+λiLidemp, (10) where LVITS denotes the standard VITS generator and dis… view at source ↗
Figure 4
Figure 4. Figure 4: Cosine similarity between Titanet embeddings of the original speech and reconstructed samples for 20 speakers from the GLOBE test set over 20 reconstruction rounds. Each round uses the output of the previous round as input. The baseline shows rapid identity drift, while RIVET maintains higher simi￾larity to the original speaker across iterations. ning 164 global accents and a wide age range [5]. The train￾… view at source ↗
read the original 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 regularizer that reduces sensitivity to mislabeled examples. We introduce RIVET, a training framework that incorporates an idempotency objective to improve robustness to label noise. We evaluate RIVET under controlled label noise and on the GLOBE dataset with naturally noisy annotations. RIVET improves editing success and better preserves speaker identity than standard training, showing that idempotency improves robustness in voice editing models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper introduces RIVET, a training framework for conditional generative models in voice attribute editing that adds an idempotency objective (f(f(x)) = f(x)) to improve robustness to noisy or inconsistent attribute labels. It claims this acts as an implicit regularizer, yielding higher editing success and better speaker identity preservation than standard training, with evaluations under controlled label noise and on the naturally noisy GLOBE dataset.

Significance. If substantiated with quantitative evidence, the result would demonstrate a lightweight consistency-regularization technique applicable to label-noise issues common in large-scale speech datasets. The approach builds on standard ideas from consistency regularization but applies them specifically to idempotent operators in voice editing, which could extend to other conditional generation tasks.

major comments (1)
  1. [Abstract] Abstract: the claim that 'RIVET improves editing success and better preserves speaker identity than standard training' is asserted without any reported metrics, baselines, statistical tests, or experimental protocol. This absence is load-bearing for the central contribution, as the soundness of the performance gains cannot be assessed from the given text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback on the abstract. We agree that the central claims require clearer quantitative grounding even in the abstract and will revise accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'RIVET improves editing success and better preserves speaker identity than standard training' is asserted without any reported metrics, baselines, statistical tests, or experimental protocol. This absence is load-bearing for the central contribution, as the soundness of the performance gains cannot be assessed from the given text.

    Authors: We agree that the abstract should include concrete quantitative support for the performance claims. In the revised version we will add a sentence reporting the key improvements (e.g., absolute gains in editing success rate and speaker similarity on GLOBE) together with a brief reference to the evaluation setting. The full experimental protocol, baselines, and statistical details remain in Section 4; the abstract revision will simply make the headline result verifiable at a glance. revision: yes

Circularity Check

0 steps flagged

No significant circularity; idempotency objective is an independent training term

full rationale

The paper introduces an idempotency objective f(f(x)) = f(x) as an additional loss term for training conditional generative models on noisy voice attribute labels. This is presented as a standard consistency regularization technique applied to the editing task, with evaluation on controlled noise and the GLOBE dataset showing empirical gains in editing success and speaker preservation. No derivation reduces a claimed prediction or uniqueness result back to fitted parameters or self-citations; the central claim rests on the external definition of idempotency and standard training dynamics rather than any self-referential construction. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no explicit free parameters, axioms, or invented entities; the approach implicitly assumes standard conditional generative modeling setups whose details are not stated.

pith-pipeline@v0.9.1-grok · 5685 in / 1076 out tokens · 23759 ms · 2026-06-26T18:49:11.542930+00:00 · methodology

discussion (0)

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Reference graph

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    Recent generative models have made significant progress in enabling controllable speech editing through conditional synthesis and disentangled representations [1, 2, 3]

    Introduction V oice attribute editing aims to modify specific characteristics of a speech signal, such as age, gender, or accent, while pre- serving the speaker’s underlying identity. Recent generative models have made significant progress in enabling controllable speech editing through conditional synthesis and disentangled representations [1, 2, 3]. In ...

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    Our ex- periments focus on two attributes, age and gender

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    Idempotent Training LetF(·)denote the overall editing model

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    Results and Discussion 5.1. Evaluation Metrics We measure speaker identity preservation using cosine similar- ity between Titanet speaker embeddings [29] extracted from the original speech and the reverted speech. The reverted speech is obtained by first editing an attribute (e.g., age or gender) and then reversing the edit back to its original value. Thi...

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    We introduced RIVET, an end-to- end training framework that incorporates an idempotency con- straint into the latent representations of a conditional voice edit- ing model

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    All scientific content, including ideas, methods, experimental design, analysis, and results, was developed and verified by the authors

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