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arxiv: 2606.19688 · v2 · pith:P3VHJM7Inew · submitted 2026-06-18 · 💻 cs.SD · eess.AS

Latency-Configurable Streaming Speech Enhancement via Asymmetric Temporal Padding

Yunsik Kim , Yoonyoung Chung This is my paper

Pith reviewed 2026-06-26 16:23 UTC · model grok-4.3

classification 💻 cs.SD eess.AS
keywords streaming speech enhancementasymmetric temporal paddinglatency configurationdual-buffer streamingPESQcausal modelsVoiceBank+DEMAND
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The pith

Asymmetric temporal padding lets one speech enhancement backbone produce models at any latency from 12.5 to 75 ms.

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

The paper demonstrates that streaming speech enhancement no longer needs to be locked into a binary causal or non-causal choice. A single training hyperparameter controls how much future context is available through asymmetric temporal padding in convolutions. Dual-buffer streaming with selective state updates keeps past and future information separate so that training and real-time inference stay consistent. From one 1.37-million-parameter network this produces a continuous family of models whose quality, measured by PESQ on VoiceBank+DEMAND, rises steadily as the allowed latency increases.

Core claim

LaCo-SENet uses asymmetric temporal padding to redistribute past and future context according to a chosen latency target, paired with dual-buffer streaming at input and feature levels and selective state updates that block future-frame leakage. The result is a single backbone that, after one training run, can be deployed at any latency between 12.5 ms and 75 ms, delivering PESQ scores from 3.35 up to 3.43; the fully causal 12.5 ms version already equals or exceeds earlier causal systems that required 46.5 ms.

What carries the argument

Asymmetric temporal padding that redistributes past and future context in convolutions according to a single training hyperparameter.

If this is right

  • The same fixed-size backbone supports the full range of latencies without retraining.
  • PESQ rises monotonically from 3.35 at 12.5 ms to 3.43 at 75 ms.
  • Fully causal operation at 12.5 ms already matches prior causal state-of-the-art results obtained at 46.5 ms.
  • Selective state updates maintain training-inference consistency across all chosen latencies.

Where Pith is reading between the lines

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

  • A single trained network could be deployed across devices that tolerate different maximum delays without maintaining separate models.
  • The same padding and buffering logic could be tested on other streaming audio tasks such as source separation where latency must also be traded against quality.
  • Reducing the need to train and store one model per target latency would lower both training compute and storage requirements for production systems.

Load-bearing premise

Asymmetric padding and dual buffers ensure future information never leaks into the streaming state and produce no new artifacts when the model runs in real time.

What would settle it

Run the trained model at a latency different from the one used in training and check whether PESQ drops or audible artifacts appear that were absent in the original training-inference match.

Figures

Figures reproduced from arXiv: 2606.19688 by Yoonyoung Chung, Yunsik Kim.

Figure 1
Figure 1. Figure 1: Overview of LaCo-SENet. (a) Dual-buffer streaming architecture: the STFT context buffer provides encoder lookahead at the input level; the feature buffer provides decoder lookahead; and state buffers preserve past context. (b) Training with asymmetric temporal padding (PL, PR), redistributing past and future context while preserving the receptive field. (c) Streaming inference with chunk-wise processing (c… view at source ↗
Figure 3
Figure 3. Figure 3: Steady-state streaming RTF vs. chunk size C (frames) for three total lookahead values Lenc+Ldec ∈ {0, 10, 30} [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
read the original abstract

Streaming speech enhancement requires balancing algorithmic latency against quality, yet existing approaches largely treat this as a binary causal versus non-causal choice. LaCo-SENet addresses this issue with two mechanisms parameterized by a single training-time hyperparameter. First, asymmetric temporal padding redistributes past and future context in convolutions, enabling systematic latency configuration. Second, dual-buffer streaming combines state buffers for past context with lookahead buffers that supply future context at both the input and feature levels. Selective state updates also prevent future-frame leakage into the streaming state, ensuring training-inference consistency. On VoiceBank+DEMAND, a fixed-budget (1.37M parameters) backbone yields a family of models spanning 12.5-75.0 ms, with PESQ rising from 3.35 to 3.43. At just 12.5 ms (fully causal), a PESQ of 3.35 matches or exceeds the prior causal state-of-the-art (3.27 at 46.5 ms).

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 / 2 minor

Summary. The manuscript introduces LaCo-SENet, a latency-configurable streaming speech enhancement model. It proposes asymmetric temporal padding (controlled by a single training hyperparameter) to redistribute past/future context in convolutions, combined with dual-buffer streaming (state buffers for past context and lookahead buffers for future context at input and feature levels) and selective state updates to prevent future-frame leakage. On the VoiceBank+DEMAND dataset, a fixed 1.37M-parameter backbone produces models spanning 12.5–75 ms latency with PESQ scores from 3.35 to 3.43; the fully causal 12.5 ms variant is claimed to match or exceed prior causal SOTA (3.27 at 46.5 ms).

Significance. If the training-inference consistency holds, the approach provides a practical single-training method to generate a family of latency-tunable models without retraining, which addresses a real need in real-time speech enhancement. The direct empirical comparison against published prior causal SOTA numbers on a standard dataset is a concrete strength, though the absence of error bars and ablations limits how strongly the gains can be interpreted.

major comments (1)
  1. [Abstract/Method] Abstract and Method description: The central empirical claim (PESQ 3.35 at 12.5 ms fully causal matching prior SOTA) depends on asymmetric temporal padding + dual-buffer streaming + selective state updates producing identical outputs at train and inference time with no future-frame leakage. The manuscript describes the buffers and selective-update rule but supplies no side-by-side numerical verification (e.g., L2 or max-abs difference between padded training forward pass and streaming inference on identical inputs). This verification is load-bearing for the headline result.
minor comments (2)
  1. [Experiments] Experiments section: Reported PESQ values lack error bars, standard deviations, or details on the number of runs; ablation studies on the individual contributions of asymmetric padding versus dual buffers are not described.
  2. [Abstract] Abstract: The reference to 'prior causal state-of-the-art (3.27 at 46.5 ms)' is not accompanied by a citation or model name in the provided text.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for highlighting the importance of explicit verification for the training-inference consistency claim, which is central to our contribution. We address this point directly below.

read point-by-point responses

  1. Referee: [Abstract/Method] Abstract and Method description: The central empirical claim (PESQ 3.35 at 12.5 ms fully causal matching prior SOTA) depends on asymmetric temporal padding + dual-buffer streaming + selective state updates producing identical outputs at train and inference time with no future-frame leakage. The manuscript describes the buffers and selective-update rule but supplies no side-by-side numerical verification (e.g., L2 or max-abs difference between padded training forward pass and streaming inference on identical inputs). This verification is load-bearing for the headline result.

    Authors: We agree that the manuscript would be strengthened by including explicit numerical verification of equivalence. The mechanisms (asymmetric padding controlled by a single hyperparameter, dual buffers at input/feature levels, and selective state updates) are designed so that the streaming inference exactly replicates the padded training forward pass, with no future-frame information leaking into the state. In the revised manuscript we will add a new subsection (e.g., Section 3.4) containing side-by-side verification on held-out test utterances: we will report L2 norm and maximum absolute difference between the training padded output and the streaming output, confirming they match to machine precision (typically < 1e-7). This will directly substantiate the headline result. revision: yes

Circularity Check

0 steps flagged

Empirical results on external benchmarks; no derivation reduces to self-defined inputs

full rationale

The paper reports PESQ scores (3.35 to 3.43) obtained by training a fixed-parameter backbone on VoiceBank+DEMAND and evaluating at different latencies. These metrics are measured outputs against an external dataset and prior published numbers, not quantities defined by fitted parameters or reduced by the paper's own equations. The mechanisms (asymmetric padding, dual buffers, selective updates) are presented as design choices to achieve consistency, but the headline numbers do not reduce to those choices by construction. No self-citations are invoked as load-bearing uniqueness theorems. The absence of an explicit numerical train-inference match check is a verification gap rather than circularity in the reported results.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The approach relies on standard convolutional padding properties and buffer management; the main addition is the parameterization of context distribution by a single training hyperparameter. No new physical entities or ad-hoc constants beyond the latency control parameter are introduced.

free parameters (1)
  • latency configuration hyperparameter
    Single training-time value that sets the degree of asymmetric padding and lookahead buffer size, directly controlling the resulting algorithmic latency.
axioms (1)
  • standard math Convolutional layers admit asymmetric temporal padding that redistributes receptive field without changing kernel weights.
    Invoked to enable systematic latency control via padding choice.

pith-pipeline@v0.9.1-grok · 5702 in / 1291 out tokens · 31508 ms · 2026-06-26T16:23:49.114960+00:00 · methodology

discussion (0)

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Reference graph

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    Introduction Streaming speech enhancement is essential for real-time ap- plications such as telephony, conferencing, hearing aids, and on-device voice interfaces, where algorithmic latency directly governs system responsiveness [1, 2]. Within the 10–80 ms regime where most applications operate, quality generally im- proves with additional lookahead, yet e...

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    Latency-Configurable Streaming Speech Enhancement via Asymmetric Temporal Padding

    Related Work Streaming SE architectures.Diverse approaches address streaming speech enhancement, including DSP-DNN hy- brids [5], complex-valued convolutional-recurrent networks [6], perceptual deep filtering [7], temporal convolutional autoen- coders [4], Mamba-based models [8], and xLSTM-based de- signs [9]. Each is locked to a single latency point by i...

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    Asymmetric temporal padding We fix the total temporal padding while distributing it asym- metrically between past and future

    Proposed Method 3.1. Asymmetric temporal padding We fix the total temporal padding while distributing it asym- metrically between past and future. LetPdenote the per- side padding of a standard symmetric convolution, so that the total padding isP tot = 2P. Definingpadding ratioas r= (r L, rR)withr L +r R = 1, the temporal padding is dis- tributed as: PL =...

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    Experimental Setup We evaluated on V oiceBank+DEMAND [16] at 16 kHz, com- prising 11,572 training and 824 test utterances mixed with 10 noise types at four SNR levels

    Experiments 4.1. Experimental Setup We evaluated on V oiceBank+DEMAND [16] at 16 kHz, com- prising 11,572 training and 824 test utterances mixed with 10 noise types at four SNR levels. Figure 2:PESQ vs. algorithmic latency on Voice- Bank+DEMAND. LaCo-SENet (filled circles, connected) spans 12.5–200.0 ms with a constant 1.37M parameters. Open markers denot...

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    On V oiceBank+DEMAND, a fixed 1.37M-parameter architecture spans 12.5–75.0 ms (PESQ 3.35– 3.43) across padding ratios, surpassing prior causal models at lower latency

    Conclusion We presented LaCo-SENet, a dual-buffer streaming framework whose algorithmic latency is configurable via asymmetric tem- poral padding, with training–inference equivalence preserved by selective state updates. On V oiceBank+DEMAND, a fixed 1.37M-parameter architecture spans 12.5–75.0 ms (PESQ 3.35– 3.43) across padding ratios, surpassing prior ...

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    Code Availability The implementation is available athttps://github.com/ yskim3271/LaCo-SENet

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    Acknowledgments This work was supported by the National Research Founda- tion of Korea (NRF) grant funded by the Ministry of Science and ICT (RS-2025-00516311); by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (RS-2019-II191906, Artificial Intelligence Graduate School Program); ...

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