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arxiv: 2606.02220 · v2 · pith:W3GX2KZWnew · submitted 2026-06-01 · 📡 eess.AS

SiamCTC: Learning Speech Representations through Monotonic Temporal Alignment

SooHwan Eom , Mark Hasegawa-Johnson , Chang D. Yoo This is my paper

Pith reviewed 2026-06-28 12:50 UTC · model grok-4.3

classification 📡 eess.AS
keywords self-supervised speech learningSiamese networksCTC losstemporal alignmentspeaking rate robustnessmonotonic alignment
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The pith

SiamCTC replaces frame-wise matching with CTC-based monotonic alignments to learn speech representations robust to speed changes.

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

The paper introduces SiamCTC, a self-supervised framework that pairs Siamese networks with Connectionist Temporal Classification loss. Instead of forcing exact frame-by-frame correspondence between augmented views of an utterance, CTC computes flexible yet order-preserving alignments. This change lets the model tolerate speed perturbations and other timing shifts while keeping linguistic content consistent across views. Experiments indicate the resulting representations transfer better to downstream tasks that involve varied speaking rates.

Core claim

By training Siamese networks with CTC loss rather than strict frame-level alignment, SiamCTC produces speech representations that accommodate temporal augmentations such as speed changes while maintaining temporal coherence and linguistic invariance.

What carries the argument

CTC loss applied between the two branches of a Siamese network to compute monotonic alignments between temporally differing realizations of the same utterance.

If this is right

  • Representations become more adaptable to speed perturbations without explicit frame correspondence.
  • Temporal coherence is retained while frame-wise constraints are relaxed.
  • Downstream performance improves specifically on tasks with diverse speaking rates.

Where Pith is reading between the lines

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

  • The same CTC-Siamese pattern could be tested on other ordered data such as video frames or sensor streams where timing varies.
  • It may reduce reliance on carefully tuned speed-augmentation schedules during pretraining.
  • Systems using these representations might require less speaker-specific fine-tuning for rate variation.

Load-bearing premise

The monotonic alignments found by CTC will keep enough linguistic content the same across speaking styles without adding alignment mistakes that harm the learned representations.

What would settle it

A controlled test in which SiamCTC representations yield lower accuracy than strict frame-aligned Siamese baselines on a speaking-rate-varied downstream task would contradict the central claim.

Figures

Figures reproduced from arXiv: 2606.02220 by Chang D. Yoo, Mark Hasegawa-Johnson, SooHwan Eom.

Figure 1
Figure 1. Figure 1: An overview of SiamCTC framework. Overview of our proposed SiamCTC framework. The model processes two views of the same input sequence X and X˜, where one undergoes speed/tempo perturbation. Both sequences pass through a shared encoder fθ to produce representation Z and Z˜. The framework optimizes three learning objectives: (1) CTC loss (LCT C ) for monotonic alignment learning, (2) KL divergence loss (LKL… view at source ↗
Figure 2
Figure 2. Figure 2: Phoneme Error Rate (PER) on LibriSpeech test-clean for different speed factors. The blue circle line represents Hu￾BERT, and the orange square line represents SiamCTC. Across all speed factors, SiamCTC consistently achieves a lower PER than HuBERT. At the original speed (1.0), HuBERT obtains a PER of 5.41%, whereas SiamCTC achieves 4.32%. When the audio is slowed down, HuBERT’s PER rises above 5.60%, peaki… view at source ↗
read the original abstract

Self-supervised speech representation learning has made significant progress through Siamese networks, which leverage different views of the same input. However, existing methods often require frame-wise alignment between these views, overlooking the broader linguistic context invariance across different speaking styles. We introduce SiamCTC, a framework that integrates Siamese networks with Connectionist Temporal Classification (CTC) to learn speech representations without strict frame-level correspondence. By employing CTC loss to establish flexible, monotonic alignments between differing temporal realizations of the same content, SiamCTC accommodates speed perturbations and other temporal augmentations. This design relaxes frame-wise constraints while preserving temporal coherence and enhancing robustness to speaking-rate variations in downstream tasks. Our experiments demonstrate that SiamCTC leads to more adaptable speech representations, particularly at diverse speaking rates.

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 / 0 minor

Summary. The paper proposes SiamCTC, a Siamese-network framework that replaces frame-wise alignment with CTC loss to establish flexible monotonic alignments between temporally augmented views of the same utterance. The central claim is that this design accommodates speed perturbations while preserving temporal coherence and yielding more robust speech representations for downstream tasks at varying speaking rates.

Significance. If the claimed alignment mechanism can be shown to avoid injecting alignment errors that degrade linguistic invariance, the approach would offer a concrete relaxation of the strict temporal correspondence assumption common in current contrastive speech SSL methods, with potential gains in robustness to rate variation.

major comments (2)
  1. Abstract (and entire provided text): no formulation is given for how CTC loss is computed between the two Siamese branches in the absence of external labels. It is therefore impossible to determine whether one view is treated as a pseudo-label sequence, how blank tokens are handled, or whether an auxiliary term is used to prevent collapse; without these details the central claim that the resulting alignments remain content-preserving cannot be evaluated.
  2. Abstract: the assertion that CTC alignments 'preserve temporal coherence' while accommodating speed perturbations is stated without any derivation, loss equation, or ablation that would demonstrate the alignments remain monotonic and linguistically faithful rather than latching onto spurious temporal patterns under realistic rate changes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the review and the constructive feedback on SiamCTC. We address each major comment below and will revise the manuscript to improve clarity and provide the requested details.

read point-by-point responses

  1. Referee: Abstract (and entire provided text): no formulation is given for how CTC loss is computed between the two Siamese branches in the absence of external labels. It is therefore impossible to determine whether one view is treated as a pseudo-label sequence, how blank tokens are handled, or whether an auxiliary term is used to prevent collapse; without these details the central claim that the resulting alignments remain content-preserving cannot be evaluated.

    Authors: We agree that an explicit formulation of the inter-branch CTC loss is essential for evaluating the method. In the revised manuscript we will add a dedicated subsection with the full loss equation, showing that the output sequence from one branch is used as the target for CTC alignment on the other branch (no external labels), standard blank-token handling per the CTC forward algorithm, and no auxiliary collapse-prevention term. The monotonicity of CTC is relied upon to keep alignments content-preserving. revision: yes

  2. Referee: Abstract: the assertion that CTC alignments 'preserve temporal coherence' while accommodating speed perturbations is stated without any derivation, loss equation, or ablation that would demonstrate the alignments remain monotonic and linguistically faithful rather than latching onto spurious temporal patterns under realistic rate changes.

    Authors: The manuscript already contains empirical results on downstream ASR performance across multiple speaking rates that support robustness. However, we acknowledge the absence of an explicit derivation or dedicated ablation on alignment fidelity. In the revision we will insert a short theoretical paragraph deriving monotonicity from the CTC dynamic-programming recursion and add an ablation that measures alignment error under controlled speed perturbations. revision: yes

Circularity Check

0 steps flagged

No circularity: design choice presented without derivation or fitted predictions

full rationale

The paper introduces SiamCTC as a framework that integrates Siamese networks with CTC loss to enable flexible monotonic alignments for speech representations under temporal augmentations. No equations, derivation steps, or quantitative predictions are shown in the abstract or description. The central claim is a methodological assertion about relaxing frame-wise constraints while preserving coherence, not a result derived from inputs by construction or self-citation. No load-bearing steps reduce to fitted parameters renamed as predictions or self-referential definitions. The derivation chain is absent, rendering circularity assessment inapplicable; the work is self-contained as an empirical design proposal.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Only the abstract is available; no explicit free parameters, axioms, or invented entities are stated. The design implicitly assumes that CTC loss can produce useful alignments without additional regularization or that monotonicity is sufficient to maintain content invariance.

axioms (1)
  • domain assumption CTC loss produces alignments that preserve linguistic content invariance across temporal augmentations
    Invoked in the abstract description of SiamCTC design; no supporting derivation or evidence supplied.

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discussion (0)

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Reference graph

Works this paper leans on

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    Introduction Self-supervised learning (SSL) for speech representation learns from unlabeled data, using the input signal itself as the super- visory signal. By avoiding manual transcriptions, SSL enables deep neural network training on large-scale raw speech corpora, providing effective pre-training and robust representations for downstream tasks such as ...

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    SiamCTC This section introduces SiamCTC, a novel framework for learn- ing speech representations invariant to temporal variations by combining a Siamese encoder with a Connectionist Temporal Classification (CTC) [17] alignment head. Auxiliary alignment consistency and temporal contrastive losses further refine align- ment and prevent collapse. The overall...

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    Experimental Details 4.1. Datasets We use LibriSpeech [29] for both pre-training and downstream fine-tuning. LibriSpeech is a widely used corpus of approxi- mately 1,000 hours of read English audiobooks derived from LibriV ox, sampled at 16 kHz. It is partitioned into subsets des- ignated as “clean” or “other,” reflecting differences in recording quality ...

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    Experiment Results 5.1. Main Results Table 1 summarizes our results in phoneme recognition (PR) and automatic speech recognition (ASR) on LibriSpeech, com- 1https://huggingface.co/s3prl/converted_ ckpts/resolve/main/hubert_base_ls960.pt 2https://huggingface.co/s3prl/converted_ ckpts/resolve/main/wavlm_base.pt 3https://github.com/s3prl/s3prl Table 2:Ablati...

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    Conclusion We proposed SiamCTC, a self-supervised learning framework that merges Siamese encoding with a Connectionist Tempo- ral Classification (CTC) based alignment objective as its core mechanism to handle temporal perturbations in speech. This is complemented by temporal InfoNCE (TINCE) loss and an alignment consistency loss to prevent representation ...

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