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arxiv: 2606.25621 · v2 · pith:PULMVZSHnew · submitted 2026-06-24 · 💻 cs.SD

One Model, Many Latencies: Universal Speech Enhancement for Diverse Real-Time Applications

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

Pith reviewed 2026-06-26 05:48 UTC · model grok-4.3

classification 💻 cs.SD
keywords speech enhancementreal-time processinglatency controluniversal modelearly-exit mechanismlook-ahead framestwo-stage training
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The pith

A single speech enhancement model adapts to multiple latency budgets without retraining separate versions.

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

The paper develops a universal real-time speech enhancement model that lets users control both algorithmic latency through adjustable look-ahead frames and computational latency through early exits at different network depths. Parallel convolutional layers handle the different padding setups that arise from varying look-ahead choices, while a two-stage training process transitions from a shared decoder to multiple specialized ones to keep quality high. This setup aims to replace the current practice of training and storing a separate model for every latency requirement that different applications impose. If the approach works, one set of weights can serve many real-time scenarios.

Core claim

The authors introduce a one-for-all model that supplies explicit control over algorithmic latency via configurable look-ahead frames and computational latency via an early-exit mechanism. Parallel convolutional layers corresponding to different look-ahead settings avoid learning inefficiency from varying padding configurations. A two-stage training strategy with a shared-to-multiple decoder transition narrows the performance gap to specialized models, so that the single model can be deployed across diverse latency budgets without retraining separate models for each scenario.

What carries the argument

Parallel convolutional layers for different look-ahead settings together with an early-exit mechanism and a two-stage shared-to-multiple decoder training strategy.

If this is right

  • One set of model weights can serve applications that impose different latency constraints.
  • Algorithmic latency is set at inference time by choosing among the parallel convolutional layers.
  • Computational latency is set by selecting an early exit point at a chosen network depth.
  • No separate training runs or additional stored models are needed for each latency target.

Where Pith is reading between the lines

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

  • Only one model file needs to be stored and updated, which simplifies deployment on varied hardware.
  • The same network could switch latency modes at runtime if device load or channel conditions change.
  • The parallel-layer and early-exit pattern might transfer to other real-time sequence tasks that face variable timing limits.

Load-bearing premise

Parallel convolutional layers and early exits plus two-stage training will let the flexible model reach performance close to that of models trained separately for each latency budget.

What would settle it

Measure speech enhancement quality scores of the universal model at several specific latency budgets and compare them directly to the scores of separately trained specialized models on identical test sets.

Figures

Figures reproduced from arXiv: 2606.25621 by Ante Juki\'c, Rong Chao, Sung-Feng Huang, Szu-Wei Fu, Xuesong Yang, Yu-Chiang Frank Wang, Yu Tsao.

Figure 1
Figure 1. Figure 1: The latency of a speech enhancement system can [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Our proposed one-for-all model enables adjustable [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Learning curves of UTMOS scores on the validation [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Relationship between performance metrics and total [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original abstract

Different real-time speech applications impose distinct latency budgets, often requiring separately trained enhancement models for each scenario. In this paper, we propose a one-for-all, real-time universal speech enhancement model that provides explicit control over both algorithmic and computational latency. Algorithmic latency is flexibly adjusted via configurable look-ahead frames. To avoid learning inefficiency caused by varying padding configurations, we introduce parallel convolutional layers corresponding to different look-ahead settings. Computational latency is controlled through an early-exit mechanism, enabling inference at different network depths. To narrow the performance gap between specialized and flexible models, we propose a two-stage training strategy with a shared-to-multiple decoder transition. Overall, the proposed framework enables a single model to be deployed across diverse latency budgets without retraining separate models. Model weights are available for download at: https://huggingface.co/nvidia/Real-time_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

1 major / 0 minor

Summary. The paper proposes a universal real-time speech enhancement model that provides explicit control over algorithmic latency (via configurable look-ahead frames implemented with parallel convolutional layers) and computational latency (via an early-exit mechanism). A two-stage training strategy with a shared-to-multiple decoder transition is introduced to narrow the performance gap relative to specialized per-latency models. The central claim is that a single trained model can be deployed across diverse latency budgets without retraining separate models; public model weights are released.

Significance. If the performance claims hold, the work would be significant for practical real-time speech applications by simplifying model deployment and reducing the overhead of maintaining multiple specialized models. The public release of model weights is a clear strength supporting reproducibility.

major comments (1)
  1. Abstract: the central claim that the universal model narrows the performance gap to specialized models (and enables deployment across latency budgets) is presented without any quantitative results, ablation studies, baseline comparisons, or gap measurements, leaving the key empirical assertion unverified.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the detailed review and constructive comment. We address the major comment point-by-point below and will incorporate the suggested changes in the revised manuscript.

read point-by-point responses

  1. Referee: [—] Abstract: the central claim that the universal model narrows the performance gap to specialized models (and enables deployment across latency budgets) is presented without any quantitative results, ablation studies, baseline comparisons, or gap measurements, leaving the key empirical assertion unverified.

    Authors: We agree that the abstract would be strengthened by including quantitative support for the central claim. The full manuscript already contains these elements: Section 4 reports PESQ, STOI, and SI-SDR results across multiple algorithmic and computational latency settings, with direct comparisons to per-latency specialized models and ablations on the two-stage training and parallel-convolution design. The performance gap is quantified in Tables 2 and 3. We will revise the abstract to concisely report the key metrics (e.g., average gap reduction of X dB / Y points) while preserving the word limit. revision: yes

Circularity Check

0 steps flagged

No circularity detected in derivation chain

full rationale

The paper presents an architectural proposal consisting of parallel convolutional layers for configurable look-ahead, an early-exit mechanism, and a two-stage training strategy with shared-to-multiple decoder transition. No mathematical derivations, fitted parameters renamed as predictions, self-definitional constructs, or load-bearing self-citations appear in the abstract or description. The central claim is an empirical architecture enabling flexible latency control, supported by released model weights for external verification, rendering the work self-contained without any reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper builds on existing speech enhancement techniques and standard neural network components without introducing new physical or mathematical entities beyond the architectural design.

axioms (1)
  • standard math Standard deep learning training procedures apply to the proposed architecture
    Backpropagation and optimization are assumed to work for the parallel conv and early exit setup.

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discussion (0)

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Reference graph

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