Paper Citation Record · LEDGER

Employing self-supervised learning models for cross-linguistic child speech maturity classification

As of 22 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2506.08999.

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

pith.paper-citation-record.v1
2506.08999 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:00:36.166342Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:00:35.983170Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T05:00:36.230638Z

Reference resolution

37 of 37 outbound references displayed

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External citation measurements

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Outbound references

Observation 86011ef0-4888-4b68-b10f-005ea13987e8 · outbound

This paper cites By about 6-7 months of age, infants start producing sounds that contain both consonant and vowel elements, form- ing what are known as canonical syllables [1].

Employing self-supervised learning models for cross-linguistic child speech maturity classification By about 6-7 months of age, infants start producing sounds that contain both consonant and vowel elements, form- ing what are known as canonical syllables [1]

Reference 1

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Observation 904fc73a-c1e4-432b-8098-8e373ba13e7b · outbound

This paper cites Employing self-supervised learning models for cross-linguistic child speech maturity classification.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Employing self-supervised learning models for cross-linguistic child speech maturity classification

Reference 2

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Observation 31b9f34e-9b16-4794-a71b-877c43f75863 · outbound

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Employing self-supervised learning models for cross-linguistic child speech maturity classification Unresolved cited work

Reference 3

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Observation 3b49e479-252f-4373-b34b-d01b73b9a1af · outbound

This paper cites canonical.

Employing self-supervised learning models for cross-linguistic child speech maturity classification canonical

Reference 4

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Employing self-supervised learning models for cross-linguistic child speech maturity classification Unresolved cited work

Reference 5

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Observation 6ce6a6c7-a067-4155-b315-5d79f79cbfb2 · outbound

This paper cites an unresolved cited work.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Unresolved cited work

Reference 6

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Observation 6a4cfab3-d8f7-4007-b9da-78440062f7a7 · outbound

This paper cites Automatic Detec- tion of Speech Sound Disorder in Child Speech Using Posterior- based Speaker Representations,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Automatic Detec- tion of Speech Sound Disorder in Child Speech Using Posterior- based Speaker Representations,

Reference 7

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Observation 6acb9936-18e8-4f32-ad58-3b75f09cc1aa · outbound

This paper cites Maddieson,Patterns of Sounds, ser.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Maddieson,Patterns of Sounds, ser

Reference 8

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Observation 51e89b60-0d57-4829-a2e2-b44df2a706a9 · outbound

This paper cites Adaptation to Language: Evidence from Babbling and First Words in Four Languages,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Adaptation to Language: Evidence from Babbling and First Words in Four Languages,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.642744Z

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Observation 4529afbf-b16e-4862-9b1d-3d192fd37877 · outbound

This paper cites Towards Better Do- main Adaptation for Self-Supervised Models: A Case Study of Child ASR,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Towards Better Do- main Adaptation for Self-Supervised Models: A Case Study of Child ASR,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.627227Z

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Observation 46d2ea94-088b-448f-9300-57e0c05c8253 · outbound

This paper cites Self-supervised learning for infant cry analysis,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Self-supervised learning for infant cry analysis,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.612244Z

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Observation 14c8821e-b20e-4dd4-80d9-bb1fe4d7e6ee · outbound

This paper cites Enhancing Child V ocalization Classification with Phonetically-Tuned Em- beddings for Assisting Autism Diagnosis,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Enhancing Child V ocalization Classification with Phonetically-Tuned Em- beddings for Assisting Autism Diagnosis,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.597234Z

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Observation baedfd89-2308-41d1-b83c-996e0a5ea40d · outbound

This paper cites VCMNet: Weakly Supervised Learning for Auto- matic Infant V ocalisation Maturity Analysis,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification VCMNet: Weakly Supervised Learning for Auto- matic Infant V ocalisation Maturity Analysis,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.583249Z

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Observation ddaee483-ffff-48cf-8551-f1fc6d5e77ab · outbound

This paper cites Automated Classification of Children’s Linguistic versus Non- Linguistic V ocalisations,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Automated Classification of Children’s Linguistic versus Non- Linguistic V ocalisations,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.569012Z

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Observation afc9f3f6-ba7f-4234-b326-e6219368611e · outbound

This paper cites The INTERSPEECH 2019 Computational Par- alinguistics Challenge: Styrian Dialects, Continuous Sleepiness, Baby Sounds & Orca Activity,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification The INTERSPEECH 2019 Computational Par- alinguistics Challenge: Styrian Dialects, Continuous Sleepiness, Baby Sounds & Orca Activity,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.554982Z

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Observation 9b41bf30-0d9e-4f7c-9401-defba10d549f · outbound

This paper cites Vali- dating a model to detect infant crying from naturalistic audio,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Vali- dating a model to detect infant crying from naturalistic audio,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.540684Z

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Observation d1180e4b-4848-4a40-8a9e-87baf6813d72 · outbound

This paper cites Towards Ro- bust Family-Infant Audio Analysis Based on Unsupervised Pre- training of Wav2vec 2.0 on Large-Scale Unlabeled Family Au- dio,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Towards Ro- bust Family-Infant Audio Analysis Based on Unsupervised Pre- training of Wav2vec 2.0 on Large-Scale Unlabeled Family Au- dio,

Reference 17

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Observation c6861cdf-9025-4e11-9fee-6b379916301e · outbound

This paper cites Casillas, P.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Casillas, P

Reference 18

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Observation c857169f-4f8d-4cd2-aaa4-335f96f67059 · outbound

This paper cites Cychosz,Cychosz HomeBank Corpus, 2018.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Cychosz,Cychosz HomeBank Corpus, 2018

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.497579Z

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Observation b5d387b2-1b49-42a3-bbe1-45fc55e9df98 · outbound

This paper cites Bergelson,Bergelson Seedlings HomeBank Corpus, 2017.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Bergelson,Bergelson Seedlings HomeBank Corpus, 2017

Reference 20

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Observation 53647e4b-1cf6-4964-a874-55d178193371 · outbound

This paper cites Cristia and H.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Cristia and H

Reference 21

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Observation 45489cbb-9ba6-494d-938c-57ada58c34f9 · outbound

This paper cites Warlaumont, G.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Warlaumont, G

Reference 22

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Observation 6c7df858-58a4-429e-b211-1f50d4511d21 · outbound

This paper cites Scaff, J.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Scaff, J

Reference 23

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Observation 58f72986-c63b-409a-bd41-8e0d987bb20a · outbound

This paper cites HomeBank, an online repository of daylong child-centered audio recordings,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification HomeBank, an online repository of daylong child-centered audio recordings,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.424517Z

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Observation e931cc40-72bd-4ee0-b1da-18806b72cdf8 · outbound

This paper cites Reliability of the LENA Lan- guage Environment Analysis System in young children’s natural home environment,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Reliability of the LENA Lan- guage Environment Analysis System in young children’s natural home environment,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.410220Z

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Observation 28f48bdf-9258-40e7-8452-26ea5cccf5dd · outbound

This paper cites An open-source voice type classifier for child-centered daylong recordings.

Employing self-supervised learning models for cross-linguistic child speech maturity classification An open-source voice type classifier for child-centered daylong recordings

Reference 26

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Observation a76bc93f-6016-4650-b4e4-0fd9be51b267 · outbound

This paper cites V o- cal development in a large-scale crosslinguistic corpus,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification V o- cal development in a large-scale crosslinguistic corpus,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.396493Z

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Observation e9fb7115-f2fd-4251-9539-1453e162b28f · outbound

This paper cites The development of canonical proportion continues past toddlerhood,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification The development of canonical proportion continues past toddlerhood,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.381838Z

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Observation 428a4c40-abca-4d81-8280-3c317260ee24 · outbound

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

Employing self-supervised learning models for cross-linguistic child speech maturity classification Lib- rispeech: An ASR corpus based on public domain audio books

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.367689Z

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Observation a4ed96fe-b35a-4439-85ed-6a86b71a92a9 · outbound

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

Employing self-supervised learning models for cross-linguistic child speech maturity classification Wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Represen- tations,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.353598Z

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Observation c9fe5afc-f2e8-48f7-b19e-af5637ad34a4 · outbound

This paper cites Using Attention Networks and Adversar- ial Augmentation for Styrian Dialect Continuous Sleepiness and Baby Sound Recognition,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Using Attention Networks and Adversar- ial Augmentation for Styrian Dialect Continuous Sleepiness and Baby Sound Recognition,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.338665Z

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Observation ebb33f31-5038-424b-9a8d-463b893d0e27 · outbound

This paper cites Keesing, Y.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Keesing, Y

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.324240Z

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Observation 75800e75-d982-4095-b049-a1e12a1f8afe · outbound

This paper cites Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby & Orca Sounds,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Using Fisher Vector and Bag-of-Audio-Words Representations to Identify Styrian Dialects, Sleepiness, Baby & Orca Sounds,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.309350Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 23894912-9126-4518-ad87-e52475ac0d16 · outbound

This paper cites Com- bining Clustering and Functionals based Acoustic Feature Rep- resentations for Classification of Baby Sounds,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Com- bining Clustering and Functionals based Acoustic Feature Rep- resentations for Classification of Baby Sounds,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.294450Z

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Observation 0ad9b81d-f1bf-4e40-b07e-62914fe2fbe4 · outbound

This paper cites Measuring nominal scale agreement among many raters,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Measuring nominal scale agreement among many raters,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.280190Z

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Observation 57f5f2ee-bf06-44a3-b1a3-2760e3e9a976 · outbound

This paper cites Characteriza- tion of children’s verbal input in a forager-farmer population us- ing long-form audio recordings and diverse input definitions,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Characteriza- tion of children’s verbal input in a forager-farmer population us- ing long-form audio recordings and diverse input definitions,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.266300Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 454c2d11-e366-4785-8c08-21d659365ff5 · outbound

This paper cites Speech Maturity Dataset: A cross- cultural corpus of naturalistic child and adult vocalizations,.

Employing self-supervised learning models for cross-linguistic child speech maturity classification Speech Maturity Dataset: A cross- cultural corpus of naturalistic child and adult vocalizations,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T05:00:36.251656Z

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Pith citing papers

Observation 904fc73a-c1e4-432b-8098-8e373ba13e7b · inbound