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StableToken: A Noise-Robust Semantic Speech Tokenizer for Resilient SpeechLLMs

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.5Download Report PDF
speech tokenizernoise robustnessaudiomulti-modalityspeech language modeling
Abstract:

Prevalent semantic speech tokenizers, designed to capture linguistic content, are surprisingly fragile. We find they are not robust to meaning-irrelevant acoustic perturbations; even at high Signal-to-Noise Ratios (SNRs) where speech is perfectly intelligible, their output token sequences can change drastically, increasing the learning burden for downstream LLMs. This instability stems from two flaws: a brittle single-path quantization architecture and a distant training signal indifferent to intermediate token stability. To address this, we introduce StableToken, a tokenizer that achieves stability through a consensus-driven mechanism. Its multi-branch architecture processes audio in parallel, and these representations are merged via a powerful bit-wise voting mechanism to form a single, stable token sequence. StableToken sets a new state-of-the-art in token stability, drastically reducing Unit Edit Distance (UED) under diverse noise conditions. This foundational stability translates directly to downstream benefits, significantly improving the robustness of SpeechLLMs on a variety of tasks.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces StableToken, a consensus-driven multi-branch tokenizer designed to stabilize semantic speech tokens under acoustic noise. It resides in the 'Consensus-Driven Multi-Branch Tokenization' leaf, which contains only two papers: StableToken itself and FUSE. This is a notably sparse research direction within the broader taxonomy of 36 papers across 17 leaf nodes, suggesting that multi-branch consensus mechanisms for tokenization remain relatively underexplored compared to single-path quantization approaches, which contain four papers.

The taxonomy reveals that StableToken sits at the intersection of architectural innovation and noise robustness. Neighboring leaves include 'Single-Path Quantization Approaches' with methods like Noise-Robust Discrete Units and 'Dual-Stream Semantic-Acoustic Decomposition' exemplified by Speechx. The 'Noise Robustness Enhancement Techniques' branch explores complementary strategies such as denoising via acoustic token prediction and self-supervised pre-training. StableToken diverges from these by embedding robustness directly into the tokenizer architecture through parallel branches and bit-wise voting, rather than relying on separate enhancement modules or data augmentation alone.

Among the three contributions analyzed, the literature search examined 13 candidates total. The voting-LFQ module was compared against 3 candidates with no refutations found, while the consensus-driven training strategy was assessed against 10 candidates, also yielding no clear prior work overlap. The core StableToken system itself was not directly compared to candidates in the refutation analysis. This limited search scope—focused on top-K semantic matches—suggests that while no immediate overlaps were detected, the analysis does not claim exhaustive coverage of all possible prior work in multi-branch tokenization or voting mechanisms.

Based on the 13 candidates examined, StableToken appears to occupy a relatively novel position within the sparse consensus-driven tokenization space. However, the limited search scope and the presence of only one sibling paper (FUSE) in the taxonomy leaf indicate that a broader literature review—particularly in adjacent fields like ensemble methods or multi-view learning—would be necessary to fully assess the originality of the bit-wise voting mechanism and multi-branch design.

Taxonomy

Core-task Taxonomy Papers
36
3
Claimed Contributions
13
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: noise-robust semantic speech tokenization for speech language models. The field addresses how to convert continuous speech into discrete tokens that preserve semantic content while remaining stable under acoustic degradation. The taxonomy reveals several complementary directions: Semantic Speech Tokenization Architectures explores foundational designs for extracting meaningful units from audio, including consensus-driven multi-branch approaches like StableToken[0] and FUSE[33] that aggregate information from multiple pathways to improve stability. Noise Robustness Enhancement Techniques focuses on methods that explicitly harden tokenizers against environmental corruption, such as Noise-Robust Discrete Units[3] and domain-specific pretraining strategies like Robust Data2VEC[25]. Generative Speech Enhancement with Semantic Modeling investigates how semantic representations can guide enhancement or denoising, exemplified by Gense[4] and Acoustic Token Denoising[5]. Evaluation Methodologies and Benchmarks provide standardized testbeds like CodecBench[8] to measure robustness, while Downstream Application Domains and Domain-Specific Applications examine how these tokenizers perform in real-world settings such as telephony, search-and-rescue, or VoIP transcription. A central tension in the field is whether to build robustness into the tokenization architecture itself or to rely on separate enhancement modules. Works like Speechx[1] and SimpleSpeech[9] pursue end-to-end designs that jointly model acoustic and semantic features, whereas others such as DC-Spin[6] and SAC[14] emphasize disentangling content from noise at the representation level. StableToken[0] sits within the consensus-driven multi-branch cluster, sharing conceptual ground with FUSE[33] by leveraging multiple encoding pathways to stabilize semantic tokens under noise. Compared to single-pathway methods like Noise-Robust Discrete Units[3], which directly train on noisy data, StableToken[0] emphasizes architectural redundancy to achieve robustness. This positions it as a middle ground between purely data-driven hardening and explicit enhancement preprocessing, offering a design philosophy that balances architectural complexity with generalization across diverse noise conditions.

Claimed Contributions

StableToken: a noise-robust semantic speech tokenizer

The authors propose StableToken, a new semantic speech tokenizer designed to be robust against acoustic noise. It uses a multi-branch architecture with a bit-wise voting mechanism to produce stable token sequences even under noisy conditions, addressing the fragility of existing semantic tokenizers.

0 retrieved papers
voting-LFQ module with bit-level majority voting

The authors introduce a novel multi-branch quantization architecture that extends the LFQ algorithm with a differentiable bit-level majority voting mechanism. This provides fine-grained error correction at the bit level rather than coarse token level, enabling robust representation learning with negligible inference overhead.

3 retrieved papers
consensus-driven training strategy with multi-view inputs

The authors develop a training strategy that presents clean audio to most branches and perturbed audio to a minority, creating a stable reference. A consensus loss is applied to enforce agreement across branches, addressing the problem of distant supervisory signals in tokenizer training.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

StableToken: a noise-robust semantic speech tokenizer

The authors propose StableToken, a new semantic speech tokenizer designed to be robust against acoustic noise. It uses a multi-branch architecture with a bit-wise voting mechanism to produce stable token sequences even under noisy conditions, addressing the fragility of existing semantic tokenizers.

Contribution

voting-LFQ module with bit-level majority voting

The authors introduce a novel multi-branch quantization architecture that extends the LFQ algorithm with a differentiable bit-level majority voting mechanism. This provides fine-grained error correction at the bit level rather than coarse token level, enabling robust representation learning with negligible inference overhead.

Contribution

consensus-driven training strategy with multi-view inputs

The authors develop a training strategy that presents clean audio to most branches and perturbed audio to a minority, creating a stable reference. A consensus loss is applied to enforce agreement across branches, addressing the problem of distant supervisory signals in tokenizer training.

Table of Contents