Paper Citation Record · LEDGER

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models

As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2505.17446.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.17446 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:50:02.022181Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:49:57.859145Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T14:50:02.310312Z

Reference resolution

38 of 38 outbound references displayed

verified exact1
verified fuzzy30
unresolved7
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8cf812e2-ced0-49ae-874a-a2004ad39b8e · outbound

This paper cites Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models

Reference 1

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Observation 0c1fb68c-fbc5-45de-b901-2f473c3febeb · outbound

This paper cites Throughout this study, we used HuBERT [7] as an SSL model and extracted representations from the ninth layer.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Throughout this study, we used HuBERT [7] as an SSL model and extracted representations from the ninth layer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:09.461214Z

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Observation 139ffd34-fb6c-4203-bc97-72dd762a371d · outbound

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Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Unresolved cited work

Reference 3

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Observation fe7a207b-24d1-47c7-aa0f-b692ededf434 · outbound

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Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Unresolved cited work

Reference 4

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Observation 80c5be5a-d540-48db-a5fe-c97e88c5ee36 · outbound

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Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Unresolved cited work

Reference 5

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Observation 6f133b16-38b6-4852-aea0-89f614884e69 · outbound

This paper cites Dataset As a training set for SLM, we used LibriSpeech [17], a 960-hour English audiobook corpus.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Dataset As a training set for SLM, we used LibriSpeech [17], a 960-hour English audiobook corpus

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:08.949260Z

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Observation 0845f3d5-355d-4e8a-974b-cb9c7422d12a · outbound

This paper cites Figure 2 shows results on fixed boundary settings.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Figure 2 shows results on fixed boundary settings

Reference 7

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Observation c225dd39-0b87-4151-a99c-c7a4a2ac90da · outbound

This paper cites yonder".

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models yonder"

Reference 8

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Observation 564c9789-a8e0-4a74-943a-2d439fbab684 · outbound

This paper cites We conducted mul- tiple speech tokenizations based on the combination of the fixed/variable segmentation and the cluster size.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models We conducted mul- tiple speech tokenizations based on the combination of the fixed/variable segmentation and the cluster size

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:08.591058Z

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Observation 3fd089f2-4175-4795-b3fc-07373eed3187 · outbound

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Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Unresolved cited work

Reference 10

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Observation 87f1e936-e923-42bf-a50c-0b65c1b67fcc · outbound

This paper cites On Generative Spoken Language Modeling from Raw Audio,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models On Generative Spoken Language Modeling from Raw Audio,

Reference 11

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Observation 70af3af0-d481-4544-949c-607c1f9a2449 · outbound

This paper cites Textually Pretrained Speech Language Models,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Textually Pretrained Speech Language Models,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:08.028899Z

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Observation 5a3393b4-1624-43bc-8954-f4f254b0e0ba · outbound

This paper cites Audiolm: A language modeling approach to audio generation,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Audiolm: A language modeling approach to audio generation,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:07.884237Z

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Observation 2a8b485e-ab94-43da-9934-9c0545480a8d · outbound

This paper cites WavLLM: Towards Robust and Adaptive Speech Large Language Model,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models WavLLM: Towards Robust and Adaptive Speech Large Language Model,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:07.713715Z

Source-reported events for the cited work

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Observation 3d90e8be-6ab2-4752-bf34-49cb88ce772e · outbound

This paper cites Representation Learning with Contrastive Predictive Coding,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Representation Learning with Contrastive Predictive Coding,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:07.558127Z

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Observation 399a0d2d-8807-4c88-86db-a7dbe180c12f · outbound

This paper cites Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Represen- tations,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Represen- tations,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:07.379926Z

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Observation a2b91708-4c6d-450d-be67-836a8f2da1a2 · outbound

This paper cites HuBERT: Self-Supervised Speech Rep- resentation Learning by Masked Prediction of Hidden Units,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models HuBERT: Self-Supervised Speech Rep- resentation Learning by Masked Prediction of Hidden Units,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:07.145029Z

Source-reported events for the cited work

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Observation 5cb4db3e-8525-4a82-9f68-88280f53a06e · outbound

This paper cites The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models The Zero Resource Speech Benchmark 2021: Metrics and baselines for unsupervised spoken language modeling,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:06.928005Z

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Observation 1bc6e752-38ab-46c8-b9ff-bbc5bdd885e5 · outbound

This paper cites Generative Spoken Dialogue Language Model- ing,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Generative Spoken Dialogue Language Model- ing,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:06.699670Z

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Observation 0d1665fd-9f77-4ee3-8638-31813885e252 · outbound

This paper cites Direct Speech- to-Speech Translation With Discrete Units,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Direct Speech- to-Speech Translation With Discrete Units,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:06.423491Z

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Observation 1c98c5ae-2af5-432e-a24b-6fe9e72d7ae4 · outbound

This paper cites Text-free prosody-aware generative spoken language modeling,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Text-free prosody-aware generative spoken language modeling,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:06.151214Z

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Observation 00f4e789-1c8c-404f-ace8-d8e74dbf0b7e · outbound

This paper cites Attention is all you need,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Attention is all you need,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:05.922539Z

Source-reported events for the cited work

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Observation 2c6830ad-de9f-453a-9575-21f2fab6f352 · outbound

This paper cites Self-Supervised Speech Representations are More Phonetic than Semantic,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Self-Supervised Speech Representations are More Phonetic than Semantic,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:05.697064Z

Source-reported events for the cited work

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Observation 3639a5ac-d4ab-4078-8b27-57f965f6b5a0 · outbound

This paper cites Generative Spoken Language Model based on continuous word-sized audio tokens,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Generative Spoken Language Model based on continuous word-sized audio tokens,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:05.437143Z

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Observation 45e2ab50-0b6a-4966-8c70-02191b2dff11 · outbound

This paper cites SyllableLM: Learning Coarse Semantic Units for Speech Language Models.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models SyllableLM: Learning Coarse Semantic Units for Speech Language Models

Reference 25

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Source-reported events for the cited work

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Observation 4367e883-40a3-4b54-b078-4ae4544f7a68 · outbound

This paper cites Sylber: Syllabic Embedding Repre- sentation of Speech from Raw Audio,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Sylber: Syllabic Embedding Repre- sentation of Speech from Raw Audio,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:05.138108Z

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Observation 4635ecbd-ac80-4160-9225-3d93d1ffc5be · outbound

This paper cites Lib- rispeech: An ASR corpus based on public domain audio books,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Lib- rispeech: An ASR corpus based on public domain audio books,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:04.894664Z

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Observation 3efbaa60-489f-409b-80d8-cfd65dd5e073 · outbound

This paper cites Libri-light: A benchmark for ASR with limited or no super- vision,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Libri-light: A benchmark for ASR with limited or no super- vision,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:04.615415Z

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Observation 1e453aac-68e8-44d7-99a5-ce410e69dde7 · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models OPT: Open Pre-trained Transformer Language Models

Reference 29

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This paper cites ProsAudit, a prosodic benchmark for self-supervised speech models,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models ProsAudit, a prosodic benchmark for self-supervised speech models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:04.320994Z

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Observation 688f3b9f-cfa1-49ac-9e5f-3dba2828f275 · outbound

This paper cites A corpus and cloze evaluation for deeper understanding of commonsense stories,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models A corpus and cloze evaluation for deeper understanding of commonsense stories,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:04.110815Z

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Observation 5021f133-9058-4f65-ae6b-c35cee78d217 · outbound

This paper cites Praat: doing phonetics by com- puter [computer program]. version 6.4.27,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Praat: doing phonetics by com- puter [computer program]. version 6.4.27,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:03.857528Z

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Observation fc7b4e91-a9ec-48aa-a83e-67d0b3690784 · outbound

This paper cites Martinet, Elements of General Linguistics, ser.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Martinet, Elements of General Linguistics, ser

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:03.592334Z

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Observation 3805a6d2-15f9-4b42-9db8-6599ed20e1da · outbound

This paper cites Are Discrete Units Nec- essary for Spoken Language Modeling?.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Are Discrete Units Nec- essary for Spoken Language Modeling?

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:03.373953Z

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Observation 05e41d96-2af2-4730-a5b6-ac84e179efd6 · outbound

This paper cites Spirit LM: Interleaved Spoken and Written Language Model.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Spirit LM: Interleaved Spoken and Written Language Model

Reference 35

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Observation 47015db1-6fa3-4ab6-99af-e34b107b2ff2 · outbound

This paper cites Multi- resolution hubert: Multi-resolution speech self-supervised learn- ing with masked unit prediction,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Multi- resolution hubert: Multi-resolution speech self-supervised learn- ing with masked unit prediction,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:03.120389Z

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Observation a782d3a6-28f9-42e3-ba2e-e69c11c1d4c7 · outbound

This paper cites Self-supervised contrastive learning for unsupervised phoneme segmentation,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Self-supervised contrastive learning for unsupervised phoneme segmentation,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:02.865867Z

Source-reported events for the cited work

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Observation abf54504-99ea-4786-be68-a93e630cec9a · outbound

This paper cites Unsupervised word segmentation using temporal gradient pseudo-labels,.

Exploring the Effect of Segmentation and Vocabulary Size on Speech Tokenization for Speech Language Models Unsupervised word segmentation using temporal gradient pseudo-labels,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:50:02.620676Z

Source-reported events for the cited work

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Pith citing papers

Observation 8cf812e2-ced0-49ae-874a-a2004ad39b8e · inbound