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arxiv: 2603.02641 · v2 · submitted 2026-03-03 · 💻 cs.SD

Rethinking Training Targets, Architectures and Data Quality for Universal Speech Enhancement

Szu-Wei Fu , Rong Chao , Xuesong Yang , Sung-Feng Huang , Ryandhimas E. Zezario , Rauf Nasretdinov , Ante Juki\'c , Yu Tsao
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Yu-Chiang Frank Wang
This is my paper

Pith reviewed 2026-05-15 17:10 UTC · model grok-4.3

classification 💻 cs.SD
keywords universal speech enhancementtraining targetsdistortion-perception tradeoffdata qualitydereverberationTTS data improvementlanguage-agnostic models
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The pith

Time-shifted anechoic clean speech as training target and two-stage framework reach state-of-the-art universal speech enhancement

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

The paper examines three problems in universal speech enhancement: selecting the right training target, managing the distortion-perception tradeoff, and balancing data scale against quality. It shows that time-shifted anechoic clean speech outperforms the conventional early-reflected speech target for both perceptual quality and downstream ASR accuracy. A two-stage architecture first secures perceptual quality then reduces distortion to the minimum possible at that level. Large uncurated training sets create a performance ceiling because models cannot remove subtle remaining artifacts. The resulting model leads the URGENT 2025 non-blind test set and generalizes across languages for cleaning TTS data.

Core claim

Time-shifted anechoic clean speech is a superior learning target to early-reflected speech for perceptual quality and ASR performance. Guided by distortion-perception tradeoff theory, a two-stage framework achieves minimal distortion at any chosen perceptual quality level. Large uncurated corpora impose a performance ceiling because models leave subtle artifacts intact. The approach sets new state-of-the-art results on the URGENT 2025 non-blind test set and shows strong language-agnostic generalization useful for improving TTS training data.

What carries the argument

Two-stage framework that first optimizes perceptual quality then refines for minimal distortion, using time-shifted anechoic clean speech as the learning target instead of early-reflected speech.

If this is right

  • Higher perceptual quality and better ASR accuracy hold across diverse degradation conditions.
  • The method directly improves quality of training data for text-to-speech systems.
  • Language-agnostic generalization allows deployment without language-specific retraining.
  • State-of-the-art results on the URGENT 2025 non-blind test set follow from the combined changes.

Where Pith is reading between the lines

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

  • Curation of training data may matter more than raw volume for future speech enhancement systems.
  • The same target-selection logic could be tested on music or environmental sound restoration.
  • TTS pipelines could incorporate this enhancement step as a standard preprocessing stage to raise synthesis fidelity.
  • The performance ceiling observed with uncurated data suggests systematic artifact audits before scaling datasets.

Load-bearing premise

Time-shifted anechoic clean speech is a universally superior learning target for perceptual quality and downstream ASR across all degradation conditions and datasets.

What would settle it

An experiment on the URGENT 2025 test set or another benchmark in which a model trained with early-reflected targets scores higher on perceptual metrics or lower ASR word error rate than the time-shifted anechoic version.

Figures

Figures reproduced from arXiv: 2603.02641 by Ante Juki\'c, Rauf Nasretdinov, Rong Chao, Ryandhimas E. Zezario, Sung-Feng Huang, Szu-Wei Fu, Xuesong Yang, Yu-Chiang Frank Wang, Yu Tsao.

Figure 1
Figure 1. Figure 1: Motivated by the distortion–perception tradeoff theory, the proposed two-stage framework integrates a frozen regression model with a residual generative model. According to the distortion–perception tradeoff theory (Blau & Michaeli, 2018), speech restoration also faces a funda￾mental trade-off between fidelity (preserving linguistic con￾tent, speaker identity, emotion, and accent) and percep￾tual quality. … view at source ↗
Figure 2
Figure 2. Figure 2: Histogram of VQScore for URGENT 2025 Challenge Track 1 subsets. Dashed lines indicate median scores. The URGENT 2025 Challenge (Track 1) provides approx￾imately 2,500 hours of speech from diverse sources, in￾cluding CommonVoice (Ardila et al., 2020), DNS5 (Dubey et al., 2024), MLS (Pratap et al., 2020), LibriTTS (Zen et al., 2019), VCTK (Veaux et al., 2013), WSJ (Garofolo et al., 1993), and EARS (Richter e… view at source ↗
Figure 3
Figure 3. Figure 3: Learning curves of UTMOS scores on the validation set under (a) different VQScore filtering thresholds and (b) different learning targets. Xiaobin (Rong et al., 2025)). The full leaderboard is pub￾licly available 1 and as presented in [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An example of a room impulse response, highlighting the time shift n0 introduced by the direct path [PITH_FULL_IMAGE:figures/full_fig_p014_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Example illustrating that GANs can focus on correcting over-smoothed regions while leaving other parts unchanged. The noisy speech is bandwidth-limited in the green box, corresponding to a less informative region. (a) Noisy (b) Clean (c) Regression model output (d) GAN correction output [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example illustrating that GANs can focus on correcting over-smoothed regions while leaving other parts unchanged. The noisy speech contains strong noise in the green box, corresponding to a less informative region. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Histogram of VQScore across different speech sources in the URGENT 2025 Challenge Track 1. The median of each data source is indicated by a dashed vertical line. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Enhanced spectrogram comparison between using time-shifted anechoic clean speech and early-reflected speech as learning targets. (a) and (b) correspond to the same noisy input, and (c) and (d) correspond to another noisy input. Both samples are drawn from the blind-test set [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Learning curves comparison on validation-set between pre-training with a regression loss followed by adversarial fine-tuning and our two-stage GAN correction. (a) Magnitude loss, (b) Phase loss, (c) Time loss, and (d) PESQ score. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Spectrogram comparison of a Japanese utterance (9997427445140542468.wav) from the FLEURS dataset. The original speech contains some very low-level stationary noise, which is commonly found in non-curated ’clean’ training data. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
read the original abstract

Universal Speech Enhancement (USE) aims to restore speech quality under diverse degradation conditions while preserving signal fidelity. Despite recent progress, key challenges in training target selection, the distortion--perception tradeoff, and data curation remain unresolved. In this work, we systematically address these three overlooked problems. First, we revisit the conventional practice of using early-reflected speech as the dereverberation target and show that it can degrade perceptual quality and downstream ASR performance. We instead demonstrate that time-shifted anechoic clean speech provides a superior learning target. Second, guided by the distortion--perception tradeoff theory, we propose a simple two-stage framework that achieves minimal distortion under a given level of perceptual quality. Third, we analyze the trade-off between training data scale and quality for USE, revealing that training on large uncurated corpora imposes a performance ceiling, as models struggle to remove subtle artifacts. Our method achieves state-of-the-art performance on the URGENT 2025 non-blind test set and exhibits strong language-agnostic generalization, making it effective for improving TTS training data. Model weights are available for download at: https://huggingface.co/nvidia/RE-USE.

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

2 major / 1 minor

Summary. The paper addresses challenges in universal speech enhancement by arguing that time-shifted anechoic clean speech is a superior training target compared to early-reflected speech, proposing a two-stage framework guided by distortion-perception tradeoff theory to minimize distortion at a given perceptual quality level, and analyzing trade-offs in training data scale versus quality. It claims state-of-the-art results on the URGENT 2025 non-blind test set, language-agnostic generalization, and utility for improving TTS training data, with model weights released publicly.

Significance. If the empirical results hold, the work could meaningfully advance universal speech enhancement by clarifying training target selection and data curation practices, with potential benefits for downstream ASR and TTS applications. The public release of model weights supports reproducibility and is a clear strength.

major comments (2)
  1. [Abstract] Abstract: the SOTA claim on the URGENT 2025 non-blind test set and the superiority of the time-shifted anechoic target are asserted without any quantitative metrics, delta scores, error bars, statistical significance tests, or baseline comparisons, which directly underpins the central claims about target selection and overall performance.
  2. [Abstract] Abstract and data-curation analysis: no cross-condition ablations or results on held-out languages/datasets are provided to support that the time-shifted anechoic target yields better perceptual quality and ASR performance across all listed degradations, leaving the language-agnostic generalization claim without load-bearing evidence.
minor comments (1)
  1. The manuscript would benefit from a dedicated results section or table presenting all quantitative evaluations, including ablations on the target choice, to allow readers to assess the reported gains.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We agree that the abstract requires quantitative support for the central claims and will revise it accordingly. We also address the generalization evidence below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the SOTA claim on the URGENT 2025 non-blind test set and the superiority of the time-shifted anechoic target are asserted without any quantitative metrics, delta scores, error bars, statistical significance tests, or baseline comparisons, which directly underpins the central claims about target selection and overall performance.

    Authors: We agree that the abstract should be self-contained with key quantitative results. In the revision we will insert specific metrics (e.g., PESQ, DNSMOS, and ASR WER deltas versus the strongest baselines on the URGENT 2025 non-blind set), including error bars from repeated runs and statistical significance indicators. These numbers are already reported with full tables and ablations in Sections 4 and 5; the abstract will now reference them directly. revision: yes

  2. Referee: [Abstract] Abstract and data-curation analysis: no cross-condition ablations or results on held-out languages/datasets are provided to support that the time-shifted anechoic target yields better perceptual quality and ASR performance across all listed degradations, leaving the language-agnostic generalization claim without load-bearing evidence.

    Authors: The full manuscript already contains multi-dataset results spanning several languages and degradation conditions that demonstrate consistent gains from the time-shifted anechoic target. To make this evidence more explicit for the language-agnostic claim, we will add a dedicated cross-condition ablation table and results on two additional held-out languages in the revised version. If the page limit permits, we will also include a short discussion of how the target choice interacts with each degradation type. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical target choice and framework rest on external benchmarks

full rationale

The paper's core contributions are empirical: an experimental comparison showing time-shifted anechoic speech outperforms early-reflected speech as a training target, a two-stage architecture guided by an external distortion-perception tradeoff theory, and an analysis of data scale versus quality. No equations, fitted parameters, or predictions are presented that reduce by construction to the inputs (e.g., no self-definitional targets or fitted quantities renamed as predictions). The SOTA result on the URGENT 2025 non-blind set and language-agnostic claims are evaluated against held-out external data rather than derived from self-citations or internal fits. The work is therefore self-contained against benchmarks with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper relies on the distortion-perception tradeoff theory as background and on the assumption that the URGENT 2025 test set is representative; no new free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Distortion-perception tradeoff theory governs the achievable operating points for enhancement models
    Invoked to justify the two-stage framework design

pith-pipeline@v0.9.0 · 5548 in / 1177 out tokens · 28514 ms · 2026-05-15T17:10:59.572989+00:00 · methodology

discussion (0)

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Reference graph

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