Content is What Remains: Invariant Speech Tokenization from Parallel Utterances

Bernhard Thallinger (1); Laurin Wagner (1); Mario Zusag (1) ((1) nyra labs); Miroslav Stankovic (1)

REVIEW 5 major objections 8 minor 1 cited by

Content is What Remains: Invariant Speech Tokenization from Parallel Utterances

T0 review · 5 major / 8 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read By aligning parallel utterances, PINT makes speech tokens carry content only, cutting speaker signal from 93.1% to 1.2%.

desk verdict PINT is a genuinely useful training recipe for speaker-invariant speech tokens, but the headline discrete-token gains are confounded by a codebook mismatch and the ABX claim in the abstract does not match the tables. read the letter →

arxiv 2607.19033 v1 pith:XLKSGGWK submitted 2026-07-21 cs.CL

Laurin Wagner (1) , Bernhard Thallinger (1) , Miroslav Stankovic (1) , Mario Zusag (1) ((1) nyra labs) This is my paper · ORCID

classification cs.CL

keywords speechtokenizationdiscreterepresentationsspeakerinvarianceparallelutterancesconditionalentropycompressionsemantictokensself-supervisedlearning

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Discrete speech tokens are hard for language models to predict, the paper argues, because the tokens retain speaker identity, prosody, and channel noise on top of the words themselves. PINT attacks that leakage at the encoder: it fine-tunes a self-supervised speech encoder so that recordings of the same transcript by different speakers converge to the same token sequence. The paper reports that this drops speaker probe accuracy from 93.1% to 1.2%, raises the token deduplication rate to 74.8% (versus 50.7% for the baseline encoder), and lets an identical 85-million-parameter language model reach test perplexity 1.95 instead of 2.78/2.67. If the claim is right, the binding constraint on speech token quality is nuisance invariance, not architecture size or codebook design.

What carries the argument

PINT (Parallel INvariant Tokenization) is the central object: a fine-tuning scheme wrapped around a self-supervised speech encoder. Its engine is a composite objective: a differentiable soft-DTW loss that warps and aligns full parallel utterances; a word-level contrastive loss built on forced-aligned word spans that attracts same-word representations and repels phonetically unrelated words; and a phoneme cross-entropy loss that anchors content. A second training stage learns the discrete codebook end-to-end with connectionist temporal classification, aligning all realizations of a group to one deduplicated reference sequence produced by an exponential-moving-average teacher. This composite is what operationalizes the paper's dual criterion of content capture plus nuisance invariance, driving the conditional entropy $H(z|c)$ toward zero.

What would settle it

Choose a transcript whose pronunciation differs systematically across dialects while remaining the same word, such as a cot/caught merger or rhotic versus non-rhotic 'car'; build parallel groups pairing those realizations, and test whether PINT keeps the two variants distinguishable in continuous embeddings and discrete tokens. If the variants collapse to identical tokens while an ABX phoneme test on that pair fails, the invariance loss has deleted genuine content.

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Extended reading notes

Core claim

The central claim is that a semantic speech token must satisfy two conditions at once: carry enough phonetic detail to reconstruct the content, and discard everything else, including speaker, emotion, channel, and noise. PINT treats parallel utterances as natural supervision: when several speakers say the same sentence under different conditions, linguistic content is the only factor they share, so aligning their representations leaves content as the residue. Sequence-level soft-DTW alignment and word-level contrastive losses pull identical content together in continuous space, a phoneme cross-entropy loss keeps phonetic detail from being deleted, and a second stage learns a discrete vocabulary by CTC-aligning every variant in a group to one shared, deduplicated token target. The claimed effect is that identical words collapse onto consistent token sequences, driving the conditional entropy $H(z|c)$ toward zero and making the sequences more compressible and more predictable.

Load-bearing premise

The load-bearing premise is that parallel recordings of the same transcript share nothing but linguistic content, so forcing their representations to align cannot erase content-bearing phonetic detail.

Editorial extensions

If this is right

An identical 85M-parameter transformer trained on PINT tokens reaches test perplexity 1.95 instead of 2.78/2.67, and matches the baseline's final perplexity in about 23x fewer steps, so speech language modeling becomes materially cheaper.
Speaker identity is nearly unrecoverable from the tokens (probe accuracy 1.2% versus 93.1%), and emotion accuracy falls from 55% to 32%, so downstream voice, accent, and emotion systems start from a cleaner content stream.
Because PINT preserves frame-level temporal alignment, its tokens can be dropped into codec pipelines as semantic targets in place of the noisier self-supervised features.
After deduplication and byte-pair encoding, PINT reaches 56 bits/s, approaching transcript BPE at 48 bits/s, while baselines stall at 107-108 bits/s.
Synthetic parallel speech plus real audio (the synth-aug variant) nearly matches full PINT on several metrics, suggesting the method can be extended to languages without parallel human recordings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If PINT tokens behave like a stable phonemic code, language models trained on them may follow text-like scaling laws; a direct test would compare token-level and character-level LM loss curves across model sizes.
The residual 32% emotion-probe accuracy suggests that some prosodic and affect information survives; an information-theoretic bound on how much content-relevant phonetic detail is lost would tell whether the invariance has gone too far.
Pairing PINT with a dedicated acoustic residual codec is a natural combination: if the semantic stream is near-zero entropy given content, the acoustic stream can be reserved for style and channel, potentially lowering total bitrate below current codecs.
The word-level contrastive loss may erase lexically contrastive dialectal distinctions; a controlled test with dialect pairs that differ in phoneme inventory would show whether the Jaccard-based negative threshold should be made accent-aware.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Read this one if you work on speech tokenization or speech LMs. The core idea—use parallel utterances as supervision to strip away speaker and channel variation, preserving content—is well executed and the continuous-side results are impressive. Speaker probe accuracy drops from 93.1% to 1.2%, DTW distances improve by roughly 20x, and the ablations show each loss term earns its place. The paper is clearly written, and the experiments honestly report both continuous and discrete results.

But there are three soft spots to keep in proportion. First, the abstract's "42% lower ABX error rate" is not supported by Table 2: the relative reductions are about 27–36% for HuBERT and smaller for WavLM. That looks like a numerical slip. Second, the main compression and LM-perplexity comparisons use a learned argmax head for PINT versus k-means for HuBERT and WavLM. That confound means part of the bitrate and perplexity gain could come from the codebook rather than from the invariance training. The continuous results (probes, DTW, WER/CER) are not confounded and do support the core claim, but the discrete-token comparison needs a control, e.g., k-means on PINT features. Third, content capture is only evaluated on LibriSpeech (standard US read speech), even though the model trains on accented corpora like EnDialects and ARCTIC. An accented held-out set would test the "content remains" claim where it matters most.

The stress-test worry about accent-dependent phonemic contrasts being deleted is real in principle, but with lambda_ce=10 the phoneme decoder strongly enforces dictionary targets, so the model is more likely to preserve standard phonemic distinctions than to collapse them. The bigger gap is simply that no accented content evaluation is reported, so we cannot tell.

Bottom line: this is a solid, worthwhile contribution with a clear methodology and a promising training recipe. It deserves a serious referee. I would recommend peer review with major revision: fix the ABX claim, add a matched-codebook comparison, report accented content metrics, and complete the hyperparameter details. Not a desk reject.

Referee Report

5 major / 8 minor

Summary. The paper proposes PINT (Parallel INvariant Tokenization), a method that fine-tunes a HuBERT encoder on parallel utterances—different speakers or conditions reading the same transcript—to produce discrete speech tokens that are invariant to speaker, emotion, and channel while preserving linguistic content. Stage A uses sequence-level soft-DTW, word-level contrastive, and phoneme-decoder cross-entropy losses; Stage B learns a shared discrete sequence per transcript group via CTC with a teacher-student scheme. Experiments report large reductions in speaker probe accuracy (93.1% to 1.2%), improvements in ABX discriminability, lower LM perplexity (1.95 vs. 2.78/2.67), and better bitrate compression than HuBERT/WavLM baselines. The paper argues that nuisance leakage, not architecture, is the main bottleneck for token sequence compressibility and predictability.

Significance. If the core claims hold, PINT addresses a genuine problem in speech tokenization—nuisance leakage inflating entropy in semantic tokens—and offers a practical recipe using parallel data. The paper includes several external evaluators (ASR CER/WER, ABX, speaker/emotion probes, LM perplexity) that go beyond self-referential invariance metrics, and it demonstrates a concrete downstream benefit (faster LM convergence). The work is positioned as orthogonal to codec architecture improvements, which gives it broad applicability. However, the significance is tempered by the fact that the key ablation—separating the effect of invariance from the effect of the learned discrete head—is missing, and the 'content remains' claim is not tested on the dialectal speech that the method is specifically designed to handle.

major comments (5)

[Abstract / Table 2] The abstract claims a "42% lower ABX error rate," but Table 2 reports across-speaker ABX errors of 0.066 for HuBERT, 0.059 for WavLM, and 0.042 for PINT. The largest relative reduction implied by these numbers is 36% (HuBERT vs PINT) and 29% (WavLM vs PINT); no comparison in the table yields 42%. Please correct the abstract or state explicitly which baseline and condition support the 42% figure.
[Table 4 / Section 3.3] The bitrate calculation in Table 4 is internally inconsistent with the stated formula "⌈log2 V⌉ × tok/s" in the table caption. With K=200, ⌈log2 200⌉ = 8 bits/token, so the RLE row for PINT (12.6 tok/s) should be approximately 101 bits/s, not the reported 152 bits/s; the same discrepancy appears for HuBERT (24.6/246 implies 10 bits/token) and WavLM (24.8/273 implies 11 bits/token). Please specify whether additional bits for run-length encodings are included and correct the table and the associated text (which quotes 152 bits/s) accordingly, so that the compression-efficiency comparison is reproducible.
[Section 2.4 / Section 3.3] The comparison between PINT and the baselines confounds the discrete coding scheme with invariance. PINT tokens come from a learned argmax/CTC head, whereas HuBERT/WavLM tokens are produced by k-means clustering on frozen features. The paper's central claim that "invariance, not architecture, is the binding constraint" is not supported by the current evidence because the codebook/head differs between conditions. Please add a control where baselines are discretized with the same learned head (or where PINT's continuous features are quantized with k-means) and report the resulting perplexity and bitrate; without such a control, the 27–30% perplexity reduction may be due to the learned codebook rather than to invariance.
[Section 2.1 / Table 2 / Section 3.1] The load-bearing premise that parallel utterances share only linguistic content is only validated on LibriSpeech, which is standard US read speech. For dialects with different phoneme inventories (e.g., bath/trap, cot/caught), the soft-DTW and word-level contrastive losses (Eqs. 1–2) together with the Stage B CTC objective actively collapse accent-specific phonemic contrasts; the only safeguard is the phoneme decoder's CE loss, whose targets come from accent-neutral transcript phonemization. The paper trains on EnDialects, ARCTIC, and CHAINS but reports no content-capture evaluation on accented held-out data. Please add an ASR/ABX evaluation on accented speech (e.g., EnDialects or ARCTIC test splits) and, ideally, a dialect-minimal-pair test to show that the method preserves genuine phonological content rather than deleting it.
[Section 3.2] The two invariance metrics—DTW cosine ratio and token edit distance between parallel utterances—are directly optimized by the Stage A soft-DTW loss and the Stage B CTC objective, respectively. Their improvement over baselines is therefore partly a reflection of optimization success rather than independent generalization. The external probes (speaker/emotion accuracy, ASR, ABX) are more convincing evidence, but the paper does not acknowledge this circularity. Please discuss this limitation and, if possible, report invariance on a condition or corpus not used in training to demonstrate generalization.

minor comments (8)

[Abstract] There is a missing space: "a98.7%relative reduction" should read "a 98.7% relative reduction".
[Section 2.2 / Section 2.4] Section 2.2 states that the id-sequence head outputs logits over K=200 codes plus a blank symbol, but Section 2.4 says "blanks are disallowed." Please clarify whether the blank symbol is part of the vocabulary in Stage B.
[Section 2.3] The claim that the word-level contrastive loss "prevents the trivial collapse that the sDTW loss alone would suffer from" is unclear: if sDTW alone would collapse all frames to a constant, the attraction term in Eq. (2) could also be satisfied trivially. Please specify how the combination of losses avoids a global collapse.
[Table 1] Several entries in the Hours column are malformed, e.g., "196.6109k" and "3.74.4k"; these should read "196.6 / 109k" and "3.7 / 4.4k" respectively.
[Section 3.1] The text says PINT achieves "substantially better ABX discriminability (0.040 vs. 0.042 across-speaker)"; a difference of 0.002 is small and the word "substantially" is overstated. Please reword.
[Section 3.2] There are typos: "atleast" should be "at least" and "sequence variablity" should be "sequence variability."
[Section 3.3] "PINTs supperior token consistency" should be "PINT's superior token consistency."
[References] Reference [9] (Xy-tokenizer) and reference [20] (Xy-tokenizer) are duplicates; please remove one and renumber.

Circularity Check

3 steps flagged · score 3.0 of 10

Two invariance metrics in Table 3 mirror the Stage A/Stage B training losses, but speaker/emotion probes, ABX, ASR, and LM perplexity keep the central claim independently supported.

fitted input called prediction [Eq. (1) in Section 2.3 vs. Section 3.2, Table 3 (DTW invariance column)]
"Parallel invariance (CSTR-VCTK test set): DTW cosine ratio (mean intra-group DTW-aligned cosine distance normalized by inter-group distance). [...] Lsdtw = 1/|P| Σ_{(i,j)∈P} d^sdtw_{ij} / max(T_i,T_j)."

The DTW cosine ratio used as invariance evidence is the same DTW-aligned distance that Eq. (1) explicitly minimizes across parallel utterances. Low DTW on held-out parallel groups therefore largely re-states that the Stage A optimization objective was met and generalized, rather than independently testing the invariant-content hypothesis. The held-out speaker split gives a generalization check, but this metric is not a separate test of the paper's central claim; that claim rests on the external speaker/emotion probes and downstream metrics.
fitted input called prediction [Section 2.4 (Stage B) vs. Section 3.2, Table 3 (Edit invariance column)]
"The student is trained with CTC to align every utterance in the group to this shared target. [...] average normalized edit distance between deduplicated token sequences of parallel utterances normalized by the longer sequences length."

Stage B's CTC loss directly optimizes the same quantity later reported as the Edit invariance metric: every utterance in a transcript group is trained to produce one shared deduplicated token sequence. Low test-set edit distance is therefore the expected readout of the fitted objective rather than an emergent discovery. The held-out speaker split qualifies this as a generalization check, but the metric itself is training-aligned by construction.

1 more flagged steps

fitted input called prediction [Section 2.1 (Noise as parallel data) vs. Section 3.2, Table 3 (Noise Ent/Ids)]
"We treat noise [26] as parallel data with empty transcripts during training. [...] Noise robustness: Entropy of the distribution of discrete id’s when feeding 2 noise classes [26] not seen in training through the encoders."

Because all noise clips are assigned the same empty transcript, they form a parallel group whose shared representation PINT is explicitly trained to produce; Stage B even assigns a shared deduplicated target for that group. The reported zero Ent/Ids for noise is thus the trained behavior, not an independent prediction. The only non-tautological component is that the two evaluation noise classes were not seen in training, which is a mild generalization test rather than an independent robustness demonstration.

full rationale

PINT's headline invariance claim is supported by external evaluators: held-out speaker and emotion probes, ABX discriminability, ASR CER/WER on LibriSpeech, and LM perplexity under an identical architecture are not direct training objectives. However, two of the 'invariance' metrics in Table 3 are near-duplicates of the Stage A and Stage B losses: soft-DTW distance is minimized by Eq. (1), and token edit distance is minimized by CTC to a shared deduplicated target. Reporting these as confirmatory evidence partly reports the optimization target itself. The noise-entropy result similarly follows from explicitly training noise as an empty-transcript parallel group. These are metric-level circularities, not derivation-level collapse: the central content-capture and compressibility claims still rest on independent benchmarks. No load-bearing self-citation was found; citations to soft-DTW, CTC, and teacher-student methods are external prior work. The accent-dependent content deletion concern raised by the skeptic is a correctness/robustness issue, not a circularity issue under the rules of this analysis.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on a small set of hand-tuned hyperparameters and domain assumptions about parallel data. No new physical entities are introduced. The most fragile inputs are the assumption that cross-speaker differences are pure nuisance and the undisclosed values of several training hyperparameters.

free parameters (4)

Loss weights lambda_sdtw, lambda_word, lambda_neg, lambda_ce = 0.5, 2, 1, 10
Hand-set scalar weights in Section 2.3 that balance alignment, contrastive, and phoneme losses; the central objective depends on them.
Discrete vocabulary size K = 200
Section 2.4 and Section 3 choose K=200 for comparability; it directly affects bitrate, perplexity, and codebook separability.
Jaccard threshold tau_neg for negative word pairs = not reported
Section 2.3 uses tau_neg to gate negatives in the word-level contrastive loss; without it the training setup is not fully specified.
Soft-DTW temperature and EMA teacher momentum = not reported
These hyperparameters in Sections 2.3 and 2.4 control the alignment softness and student-teacher dynamics but are not disclosed.

assumptions (4)

domain assumption When enough speakers utter the same words under varying conditions, linguistic content is the only shared factor.
Abstract and Section 2.1 treat parallel utterances as pure content supervision, but accents and speaking styles are also content-relevant.
domain assumption Parallel corpora have accurate transcripts and forced-aligned word boundaries.
Section 2.1 relies on MFA word-level timestamps for the contrastive loss; alignment errors would corrupt the positive and negative pairs.
domain assumption HuBERT-base features contain sufficient phonetic information that invariance training does not need to add capacity.
PINT starts from HuBERT-base and relies on its acoustic-phonetic coverage; if the base encoder is too weak, content capture would fail.
ad hoc to paper The word-level contrastive loss prevents trivial collapse of the soft-DTW objective.
Section 2.3 claims attraction-repulsion prevents collapse, but no formal guarantee or empirical probe of collapse is given.

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Pith. "Pith review of Content is What Remains: Invariant Speech Tokenization from Parallel Utterances." pith.science (2026). https://pith.science/paper/XLKSGGWK

@misc{pith2026260719033,
  author       = {Pith},
  title        = {Pith review of: Content is What Remains: Invariant Speech Tokenization from Parallel Utterances},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XLKSGGWK}},
  note         = {Machine review of arXiv:2607.19033}
}

read the original abstract

Discrete speech tokenizers aim to disentangle semantic from acoustic information, yet targets from self-supervised learning (SSL) models like HuBERT retain non-linguistic variation: speaker identity, prosody, and channel conditions leak into the tokens, inflating entropy. Our key insight is that when enough speakers utter the same words under varying conditions, linguistic content is the only shared factor. We propose PINT (Parallel INvariant Tokenization), which fine-tunes an SSL encoder with alignment losses across parallel utterances and augmentations to distill this shared residual. PINT collapses identical words onto consistent token sequences, drastically reducing conditional entropy. Unlike ASR text, PINT tokens preserve frame-level temporal grounding and serve as drop-in semantic targets for audio codecs. Experiments show a 98.7% relative reduction in speaker probe accuracy (93.1% to 1.2%), a 42% lower ABX error rate, and 27-30% lower LM perplexity versus baselines, confirming that the right invariance is key to efficient learning.

Figures

Figures reproduced from arXiv: 2607.19033 by the authors.

**Figure 1.** Training perplexity on LibriLight for an identical 85 M-parameter transformer LM on HuBERT L9, WavLM L12, and PINT L12 (k=200) tokens. Dashed lines: final test perplexity. PINT converges to 27–30 % lower perplexity, consistent with its superior sequence compressibility ( [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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ReLMCodec's preserve-control-refine design yields a single-codebook speech codec that improves the predictability-reconstruction trade-off at 650 and 800 bps, with gains in downstream TTS.

Reference graph

Works this paper leans on

52 extracted references · 33 canonical work pages · cited by 1 Pith paper

[1]

Discrete speech tokenization extends this premise to audio, supporting LM-based speech generation, di- alogue, and cross-modal reasoning [1, 2, 3, 4]

Introduction Subword tokenization in NLP succeeds partly because dis- cretization that respects a signal’s latent structure yields se- quences that are highly compressible and predictable for au- toregressive models. Discrete speech tokenization extends this premise to audio, supporting LM-based speech generation, di- alogue, and cross-modal reasoning [1,...

arXiv 2026
[2]

Method 2.1. Data and Parallel Supervision Training and evaluation draw on four categories of English speech, summarized in Table 1.True-parallel corpora(ARC- TIC, CHAINS, CSTR-VCTK, EnDialects, ESD, TIMIT) pro- vide multiple recordings of the same transcripts across speak- ers, accents, speaking styles, and emotional conditions, yield- ing naturally align...

work page
[3]

Further we evaluate the downstream compres- sion benefit unlocked by the above

Experiments We evaluate PINT along three axes that directly test the dual criterion: content capture (§3.1) and invariance and robust- ness (§3.2). Further we evaluate the downstream compres- sion benefit unlocked by the above. Baselines are HuBERT- base layer 9 and WavLM-base layer 12 since these layers have been found to correspond most closly to phonem...

work page
[4]

Conclusion PINT shows that enforcing invariance yields audio content com- pressibility close to text, providing a cleaner interface to NLP techniques. By treating parallel utterances as natural super- vision and jointly satisfying invariance and discriminability, the resulting tokens are speech-grounded and speaker-agnostic, with a 27–30 % perplexity redu...

work page
[5]

The experiments were run manually and results were manually verified

Generative AI Use Disclosure Some code used in the experiments was written with help from a coding assistant (Claude by Anthropic). The experiments were run manually and results were manually verified. Generative AI was also used in the formatting of tables and plots. The paper was manually written. The authors assume full responsibility and accountabilit...

work page
[6]

AudioLM: a language modeling approach to audio generation,

Z. Borsos, R. Marinier, D. Vincent, E. Kharitonov, O. Pietquin, M. Sharifi, D. Roblek, O. Teboul, D. Grangier, M. Tagliasac- chi, and N. Zeghidour, “AudioLM: a language modeling approach to audio generation,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2523–2533, 2023

work page 2023
[7]

SpeechGPT: Empowering large language models with intrinsic cross-modal conversational abilities,

D. Zhang, S. Li, X. Zhang, J. Zhan, P. Wang, Y . Zhou, and X. Qiu, “SpeechGPT: Empowering large language models with intrinsic cross-modal conversational abilities,” inFindings of the Association for Computational Linguistics: EMNLP 2023, H. Bouamor, J. Pino, and K. Bali, Eds. Singapore: Association for Computational Linguistics, Dec. 2023, pp. 15 757–15 ...

work page 2023
[8]

Moshi: a speech- text foundation model for real-time dialogue,

A. D ´efossez, L. Mazar ´e, M. Orsini, A. Royer, P. P ´erez, H. J ´egou, E. Grave, and N. Zeghidour, “Moshi: a speech- text foundation model for real-time dialogue,” 2024. [Online]. Available: https://arxiv.org/abs/2410.00037

arXiv 2024

Show all 52 references

[9]

Viola: Unified codec language models for speech recognition, synthesis, and translation,

T. Wang, L. Zhou, Z. Zhang, Y . Wu, S. Liu, Y . Gaur, Z. Chen, J. Li, and F. Wei, “Viola: Unified codec language models for speech recognition, synthesis, and translation,” 2023. [Online]. Available: https://arxiv.org/abs/2305.16107

2023 arXiv
[10]

SpeechTok- enizer: Unified speech tokenizer for speech language models,

X. Zhang, D. Zhang, S. Li, Y . Zhou, and X. Qiu, “SpeechTok- enizer: Unified speech tokenizer for speech language models,” inThe Twelfth International Conference on Learning Representa- tions, 2024

2024
[11]

AudioDec: An open-source streaming high-fidelity neural audio codec,

Y .-C. Wu, I. D. Gebru, D. Markovi´c, and A. Richard, “AudioDec: An open-source streaming high-fidelity neural audio codec,” in ICASSP 2023 - 2023 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), 2023

2023
[12]

Hubert: Self-supervised speech rep- resentation learning by masked prediction of hidden units,

W.-N. Hsu, B. Bolte, Y .-H. H. Tsai, K. Lakhotia, R. Salakhut- dinov, and A. Mohamed, “Hubert: Self-supervised speech rep- resentation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Pro- cessing, vol. 29, pp. 3451–3460, 2021

2021
[13]

Wavlm: Large-scale self-supervised pre-training for full stack speech processing,

S. Chen, C. Wang, Z. Chen, Y . Wu, S. Liu, Z. Chen, J. Li, N. Kanda, T. Yoshioka, X. Xiao, J. Wu, L. Zhou, S. Ren, Y . Qian, Y . Qian, J. Wu, M. Zeng, X. Yu, and F. Wei, “Wavlm: Large-scale self-supervised pre-training for full stack speech processing,” IEEE Journal of Selecte...

2022
[15]

ContentVec: An improved self-supervised speech representation by disentangling speakers,

K. Qian, Y . Zhang, H. Gao, J. Ni, C.-I. Lai, D. Cox, M. Hasegawa-Johnson, and S. Chang, “ContentVec: An improved self-supervised speech representation by disentangling speakers,” inProceedings of the 39th International Conference on Machine Learning, ser. Proceedings of Machi...

2022
[16]

Estimating the completeness of discrete speech units,

S.-L. Yeh and H. Tang, “Estimating the completeness of discrete speech units,”2024 IEEE Spoken Language Technology Work- shop (SLT), pp. 415–422, 2024

2024
[17]

Augmentation invariant discrete representation for generative spoken language modeling,

I. Gat, F. Kreuk, T. Anh Nguyen, A. Lee, J. Copet, G. Synnaeve, E. Dupoux, and Y . Adi, “Augmentation invariant discrete representation for generative spoken language modeling,” in Proceedings of the 20th International Conference on Spoken Language Translation (IWSLT 2023), E....

2023
[18]

Stabletoken: A noise-robust semantic speech tokenizer for resilient speechllms,

Y . Song, L. Zhang, C. Wu, A. Liu, W. Jia, H. Wang, and X. Zhou, “Stabletoken: A noise-robust semantic speech tokenizer for resilient speechllms,” 2025. [Online]. Available: https://arxiv.org/abs/2509.22220

2025 arXiv
[19]

Stab: Speech tokenizer assessment benchmark,

S. Vashishth, H. Singh, S. Bharadwaj, S. Ganapathy, C. Asawaro- engchai, K. Audhkhasi, A. Rosenberg, A. Bapna, and B. Ramab- hadran, “Stab: Speech tokenizer assessment benchmark,” 2024. [Online]. Available: https://arxiv.org/abs/2409.02384

2024 arXiv
[20]

Dc- spin: A speaker-invariant speech tokenizer for spoken language models,

H.-J. Chang, H. Gong, C. Wang, J. Glass, and Y .-A. Chung, “Dc- spin: A speaker-invariant speech tokenizer for spoken language models,” 08 2025, pp. 5723–5727

2025
[21]

Rethinking discrete speech representation tokens for accent generation,

J. Zhong, Y . Wang, K. Richmond, and P. Bell, “Rethinking discrete speech representation tokens for accent generation,”
[22]

Robust speech recognition via large- scale weak supervision,

A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever, “Robust speech recognition via large- scale weak supervision,” 2022. [Online]. Available: https: //arxiv.org/abs/2212.04356

2022 arXiv
[23]

Vevo: Controllable zero-shot voice imitation with self- supervised disentanglement,

X. Zhang, X. Zhang, K. Peng, Z. Tang, V . Manohar, Y . Liu, J. Hwang, D. Li, Y . Wang, J. Chan, Y . Huang, Z. Wu, and M. Ma, “Vevo: Controllable zero-shot voice imitation with self- supervised disentanglement,”ArXiv, vol. abs/2502.07243, 2025

2025 arXiv
[24]

Nast: Noise aware speech tokenization for speech language models,

S. Messica and Y . Adi, “Nast: Noise aware speech tokenization for speech language models,”ArXiv, vol. abs/2406.11037, 2024

2024 arXiv
[25]

The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,

S. R. Livingstone and F. A. Russo, “The ryerson audio-visual database of emotional speech and song (ravdess): A dynamic, multimodal set of facial and vocal expressions in north american english,”PLoS ONE, vol. 13, 2018

2018
[26]

Dualcodec: A low-frame-rate, semantically-enhanced neural audio codec for speech generation,

J. Li, X. Lin, Z. Li, S. Huang, Y . Wang, C. Wang, Z. Zhan, and Z. Wu, “Dualcodec: A low-frame-rate, semantically-enhanced neural audio codec for speech generation,”arXiv preprint arXiv:2505.13000, 2025

2025
[27]

Xy-tokenizer: Mitigating the semantic-acoustic conflict in low-bitrate speech codecs,

Y . Gong, L. Jin, R. Deng, D. Zhang, X. Zhang, Q. Cheng, Z. Fei, S. Li, and X. Qiu, “Xy-tokenizer: Mitigating the semantic-acoustic conflict in low-bitrate speech codecs,” 2025. [Online]. Available: https://arxiv.org/abs/2506.23325

2025 arXiv
[28]

Sac: Neural speech codec with semantic-acoustic dual-stream quantization,

W. Chen, X. Wang, R. Yan, Y . Chen, Z. Niu, Z. Ma, X. Li, Y . Liang, H. Wen, S. Yin, M. Tao, and X. Chen, “Sac: Neural speech codec with semantic-acoustic dual-stream quantization,”
[29]

The chains corpus: Characterizing individual speakers,

F. Cummins, M. Grimaldi, T. Leonard, and J. Simko, “The chains corpus: Characterizing individual speakers,”Proc. SPECOM, pp. 431–435, 01 2006

2006
[30]

CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit (version 0.92),

J. Yamagishi, C. Veaux, and K. MacDonald, “CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit (version 0.92),” 2019. [Online]. Available: https: //doi.org/10.7488/ds/2645

2019 doi
[31]

W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training,

Y .-A. Chung, Y . Zhang, W. Han, C.-C. Chiu, J. Qin, R. Pang, and Y . Wu, “W2v-bert: Combining contrastive learning and masked language modeling for self-supervised speech pre-training,”
[32]

Seen and unseen emo- tional style transfer for voice conversion with a new emotional speech dataset,

K. Zhou, B. Sisman, R. Liu, and H. Li, “Seen and unseen emo- tional style transfer for voice conversion with a new emotional speech dataset,” 02 2021

2021
[33]

Kokoro-82m,

Hexgrad, “Kokoro-82m,” https://huggingface.co/hexgrad/ Kokoro-82M, 2024, accessed: November 11, 2025

2024
[34]

Darpa timit acoustic phonetic con- tinuous speech corpus cdrom,

J. S. Garofolo, L. F. Lamel, W. M. Fisher, J. G. Fiscus, D. S. Pallett, and N. L. Dahlgren, “Darpa timit acoustic phonetic con- tinuous speech corpus cdrom,” 1993

1993
[35]

Learning sound event classifiers from web audio with noisy labels,

E. Fonseca, M. Plakal, D. P. W. Ellis, F. Font, X. Favory, and X. Serra, “Learning sound event classifiers from web audio with noisy labels,”ICASSP 2019 - 2019 IEEE International Confer- ence on Acoustics, Speech and Signal Processing (ICASSP), pp. 21–25, 2019

2019
[36]

Montreal forced aligner: Trainable text-speech align- ment using kaldi,

M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Son- deregger, “Montreal forced aligner: Trainable text-speech align- ment using kaldi,” 08 2017, pp. 498–502

2017
[37]

The cmu arctic speech databases,

J. Kominek and A. Black, “The cmu arctic speech databases,” SSW5-2004, 01 2004

2004
[38]

Con- nectionist temporal classification: Labelling unsegmented se- quence data with recurrent neural networks,

A. Graves, S. Fernandez, F. Gomez, and J. Schmidhuber, “Con- nectionist temporal classification: Labelling unsegmented se- quence data with recurrent neural networks,” inProceedings of the 23rd International Conference on Machine Learning, 2006, pp. 369–376

2006
[39]

Soft-dtw: a differentiable loss func- tion for time-series,

M. Cuturi and M. Blondel, “Soft-dtw: a differentiable loss func- tion for time-series,” inProceedings of the 34th International Conference on Machine Learning - Volume 70, ser. ICML’17. JMLR.org, 2017, p. 894–903

2017
[40]

Open-source multi-speaker corpora of the English accents in the British isles,

I. Demirsahin, O. Kjartansson, A. Gutkin, and C. Rivera, “Open-source multi-speaker corpora of the English accents in the British isles,” inProceedings of the Twelfth Language Resources and Evaluation Conference, N. Calzolari, F. B ´echet, P. Blache, K. Choukri, C. Cieri, T. D...

2020
[41]

Mean teachers are better role models: Weight-averaged consistency targets improve semi- supervised deep learning results,

A. Tarvainen and H. Valpola, “Mean teachers are better role models: Weight-averaged consistency targets improve semi- supervised deep learning results,” inNeural Information Process- ing Systems, 2017

2017
[42]

Learning disentangled speech representations,

Y . Brima, U. Krumnack, S. Pika, and G. Heidemann, “Learning disentangled speech representations,” 2025. [Online]. Available: https://arxiv.org/abs/2311.03389

2025 arXiv
[43]

Evaluating speech features with the minimal-pair ABX task: Analysis of the classical MFC/PLP pipeline,

T. Schatz, V . Peddinti, F. Bach, A. Jansen, H. Hermansky, and E. Dupoux, “Evaluating speech features with the minimal-pair ABX task: Analysis of the classical MFC/PLP pipeline,” inProc. Interspeech 2013, 2013, pp. 1781–1785

2013
[44]

Lib- rispeech: an asr corpus based on public domain audio books,

V . Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Lib- rispeech: an asr corpus based on public domain audio books,” in2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 2015, pp. 5206–5210

2015
[45]

Ted-lium 3: Twice as much data and corpus repartition for experiments on speaker adaptation,

F. Hernandez, V . Nguyen, S. Ghannay, N. Tomashenko, and Y . Est`eve, “Ted-lium 3: Twice as much data and corpus repartition for experiments on speaker adaptation,” inSpeech and Computer. Springer International Publishing, 2018, pp. 198–208

2018
[46]

Libri-light: A benchmark for asr with limited or no supervision,

J. Kahn, M. Riviere, W. Zheng, E. Kharitonov, Q. Xu, P. Mazare, J. Karadayi, V . Liptchinsky, R. Collobert, C. Fuegen, T. Likhomanenko, G. Synnaeve, A. Joulin, A. Mohamed, and E. Dupoux, “Libri-light: A benchmark for asr with limited or no supervision,” inICASSP 2020 - 2020 IE...

2020
[49]

Textless speech-to-speech translation on real data,

A. Lee, H. Gong, P.-A. Duquenne, H. Schwenk, P.-J. Chen, C. Wang, S. Popuri, Y . Adi, J. Pino, J. Gu, and W.-N. Hsu, “Textless speech-to-speech translation on real data,” in Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational L...

2022
[51]

Efficient self- supervised learning with contextualized target representations for vision, speech and language,

A. Baevski, A. Babu, W.-N. Hsu, and M. Auli, “Efficient self- supervised learning with contextualized target representations for vision, speech and language,” 2023. [Online]. Available: https://arxiv.org/abs/2212.07525

2023 arXiv
[53]

X-vectors: Robust dnn embeddings for speaker recognition,

D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudan- pur, “X-vectors: Robust dnn embeddings for speaker recognition,” in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, pp. 5329–5333

2018
[477]

Available: https://aclanthology.org/2023.iwslt-1

[Online]. Available: https://aclanthology.org/2023.iwslt-1. 46/

2023
[2021]

Available: https://arxiv.org/abs/2108.06209

[Online]. Available: https://arxiv.org/abs/2108.06209

arXiv
[2025]

Available: https://arxiv.org/abs/2510.16841

[Online]. Available: https://arxiv.org/abs/2510.16841
[2026]

Available: https://arxiv.org/abs/2601.19786

[Online]. Available: https://arxiv.org/abs/2601.19786

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

Discrete speech tokenization extends this premise to audio, supporting LM-based speech generation, di- alogue, and cross-modal reasoning [1, 2, 3, 4]

Introduction Subword tokenization in NLP succeeds partly because dis- cretization that respects a signal’s latent structure yields se- quences that are highly compressible and predictable for au- toregressive models. Discrete speech tokenization extends this premise to audio, supporting LM-based speech generation, di- alogue, and cross-modal reasoning [1,...

arXiv 2026

[2] [2]

Method 2.1. Data and Parallel Supervision Training and evaluation draw on four categories of English speech, summarized in Table 1.True-parallel corpora(ARC- TIC, CHAINS, CSTR-VCTK, EnDialects, ESD, TIMIT) pro- vide multiple recordings of the same transcripts across speak- ers, accents, speaking styles, and emotional conditions, yield- ing naturally align...

work page

[3] [3]

Further we evaluate the downstream compres- sion benefit unlocked by the above

Experiments We evaluate PINT along three axes that directly test the dual criterion: content capture (§3.1) and invariance and robust- ness (§3.2). Further we evaluate the downstream compres- sion benefit unlocked by the above. Baselines are HuBERT- base layer 9 and WavLM-base layer 12 since these layers have been found to correspond most closly to phonem...

work page

[4] [4]

Conclusion PINT shows that enforcing invariance yields audio content com- pressibility close to text, providing a cleaner interface to NLP techniques. By treating parallel utterances as natural super- vision and jointly satisfying invariance and discriminability, the resulting tokens are speech-grounded and speaker-agnostic, with a 27–30 % perplexity redu...

work page

[5] [5]

The experiments were run manually and results were manually verified

Generative AI Use Disclosure Some code used in the experiments was written with help from a coding assistant (Claude by Anthropic). The experiments were run manually and results were manually verified. Generative AI was also used in the formatting of tables and plots. The paper was manually written. The authors assume full responsibility and accountabilit...

work page

[6] [6]

AudioLM: a language modeling approach to audio generation,

Z. Borsos, R. Marinier, D. Vincent, E. Kharitonov, O. Pietquin, M. Sharifi, D. Roblek, O. Teboul, D. Grangier, M. Tagliasac- chi, and N. Zeghidour, “AudioLM: a language modeling approach to audio generation,”IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, pp. 2523–2533, 2023

work page 2023

[7] [7]

SpeechGPT: Empowering large language models with intrinsic cross-modal conversational abilities,

D. Zhang, S. Li, X. Zhang, J. Zhan, P. Wang, Y . Zhou, and X. Qiu, “SpeechGPT: Empowering large language models with intrinsic cross-modal conversational abilities,” inFindings of the Association for Computational Linguistics: EMNLP 2023, H. Bouamor, J. Pino, and K. Bali, Eds. Singapore: Association for Computational Linguistics, Dec. 2023, pp. 15 757–15 ...

work page 2023

[8] [8]

Moshi: a speech- text foundation model for real-time dialogue,

A. D ´efossez, L. Mazar ´e, M. Orsini, A. Royer, P. P ´erez, H. J ´egou, E. Grave, and N. Zeghidour, “Moshi: a speech- text foundation model for real-time dialogue,” 2024. [Online]. Available: https://arxiv.org/abs/2410.00037

arXiv 2024