Paper Citation Record · LEDGER

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery?

As of 21 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2507.19204.

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

pith.paper-citation-record.v1
2507.19204 v2

Coverage vector

measured 38 of 38 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:03:24.499058Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T22:51:43.039657Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

38 of 38 outbound references displayed

verified exact0
verified fuzzy36
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e4835061-27fc-4297-b507-bd30ee9c4cec · outbound

This paper cites Computational modeling of phonetic and lexical learning in early language acquisition: Existing models and future directions,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Computational modeling of phonetic and lexical learning in early language acquisition: Existing models and future directions,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:26.167039Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f4862cb-13c6-4f30-b289-6cc8f40d91da · outbound

This paper cites At 6–9 months, human infants know the meanings of many common nouns,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? At 6–9 months, human infants know the meanings of many common nouns,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:26.153337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b0e52605-9cee-4fee-a537-97dc89376833 · outbound

This paper cites Some critical developments in acquiring native language sound organization during the first year,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Some critical developments in acquiring native language sound organization during the first year,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:26.001416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 76609abd-65b4-4c3e-9b13-d29b8cf60abf · outbound

This paper cites Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Cognitive science in the era of artificial intelligence: A roadmap for reverse-engineering the infant language-learner,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.945195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b729d629-5605-4fca-b9a1-c1092ddb972c · outbound

This paper cites Automatic speech recognition for under-resourced languages: A survey,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Automatic speech recognition for under-resourced languages: A survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.932638Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bfeb242f-e23b-4ee8-b789-4a98b45c1853 · outbound

This paper cites Unsupervised pattern discovery in speech,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised pattern discovery in speech,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.707084Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2023fd75-b065-4929-9a75-18268cda1cd5 · outbound

This paper cites Unsupervised discovery of recurring speech patterns using probabilistic adaptive metrics,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised discovery of recurring speech patterns using probabilistic adaptive metrics,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.695863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6b508661-60ff-4a0f-a8ec-0e9e919846d0 · outbound

This paper cites Spoken- term discovery using discrete speech units,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Spoken- term discovery using discrete speech units,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.685456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2a1174f-7f78-4b8f-9f1a-a8526db9503b · outbound

This paper cites Unsupervised lexicon discovery from acoustic input,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised lexicon discovery from acoustic input,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.674165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3585d24-4b39-4e44-85a1-040a737cf94b · outbound

This paper cites DP-Parse: Finding word boundaries from raw speech with an instance lexicon,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? DP-Parse: Finding word boundaries from raw speech with an instance lexicon,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.661884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c14141c9-dd38-480e-ae6a-3cda313826de · outbound

This paper cites Double articulation analyzer with prosody for unsupervised word and phone discovery,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Double articulation analyzer with prosody for unsupervised word and phone discovery,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.611680Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3e3cec19-2293-439f-84db-110ceb60be6f · outbound

This paper cites Unsupervised word discovery from speech using automatic segmentation into syllable-like units,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised word discovery from speech using automatic segmentation into syllable-like units,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.462265Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4cabbb2c-985e-4f08-bcda-a6151bd85c9c · outbound

This paper cites Self-expressing autoencoders for unsupervised spoken term discovery,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Self-expressing autoencoders for unsupervised spoken term discovery,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.362183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ebf14840-69e3-4d19-bf78-3c322cb1bf9b · outbound

This paper cites Word segmentation on discovered phone units with dynamic programming and self-supervised scoring,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Word segmentation on discovered phone units with dynamic programming and self-supervised scoring,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.349373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 828b63a6-3e21-424b-86fe-ec0232f3cb4a · outbound

This paper cites Revisiting speech segmentation and lexicon learning with better features.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Revisiting speech segmentation and lexicon learning with better features

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T18:03:24.032458Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04d72da2-751f-4c8e-95e9-548d38d8d716 · outbound

This paper cites A segmental framework for fully-unsupervised large-vocabulary speech recognition,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? A segmental framework for fully-unsupervised large-vocabulary speech recognition,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.338021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e86459f4-a566-4142-8c2a-e64ea150c72f · outbound

This paper cites Seg- mental contrastive predictive coding for unsupervised word segmentation,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Seg- mental contrastive predictive coding for unsupervised word segmentation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.325898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8515698e-5b65-4e53-b193-583f5cb34bbc · outbound

This paper cites Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Contrastive prediction strategies for unsupervised segmentation and categorization of phonemes and words,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.313208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fd0dea7a-0a9e-44d7-bc05-54b39febb0b8 · outbound

This paper cites An embedded segmental K-means model for unsupervised segmentation and clustering of speech,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? An embedded segmental K-means model for unsupervised segmentation and clustering of speech,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.300406Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bdf8617c-34c1-4cef-8411-a109ff991c2e · outbound

This paper cites What do self- supervised speech models know about words?.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? What do self- supervised speech models know about words?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:03:24.052742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5d85df8f-1f3d-40c3-8c5d-3dc50e83f814 · outbound

This paper cites The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? The Zero Resource Speech Challenge 2020: Discovering discrete subword and word units,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.280019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 55aec640-bb07-4e68-a6cf-de742179d3a6 · outbound

This paper cites Unsupervised word discovery: Boundary detection with clustering vs. dynamic programming,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised word discovery: Boundary detection with clustering vs. dynamic programming,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.268777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c6df0261-f0e9-44da-8d64-75ff9dc4abea · outbound

This paper cites Self-supervised speech representation learning: A review,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Self-supervised speech representation learning: A review,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.257064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation efb1e9e7-bff0-43f2-84bc-0ab0c950d567 · outbound

This paper cites A computational model for unsupervised word discovery,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? A computational model for unsupervised word discovery,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.098777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 72f56e68-6427-465f-9fbf-c1548754826a · outbound

This paper cites Unsupervised word discovery from speech using automatic segmentation into syllable-like units,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised word discovery from speech using automatic segmentation into syllable-like units,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.017972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 43cf381c-6ade-4b1c-8e6c-c09e0c406d6a · outbound

This paper cites Gibbs sampling for the uninitiated,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Gibbs sampling for the uninitiated,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.005405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 18a2b00a-9764-43a0-bd81-3970ad6c13bf · outbound

This paper cites HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.993612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9d7a9fc0-75df-4129-80b2-ceae13f99dee · outbound

This paper cites Analyzing acoustic word embeddings from pre-trained self-supervised speech models,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Analyzing acoustic word embeddings from pre-trained self-supervised speech models,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.968977Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8cf82ab5-1b0b-4509-a4f9-f0a4de95a3de · outbound

This paper cites Word- level acoustic modeling with convolutional vector regression,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Word- level acoustic modeling with convolutional vector regression,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.953779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation db08adaa-563a-4bbb-b4e7-4ba9ad25d55c · outbound

This paper cites Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Fixed-dimensional acoustic embeddings of variable-length segments in low-resource settings,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.941786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d789f689-6d98-42a9-aa1e-a2d0a8502b5c · outbound

This paper cites Unsupervised lexical clustering of speech segments using fixed-dimensional acoustic embeddings,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Unsupervised lexical clustering of speech segments using fixed-dimensional acoustic embeddings,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:25.189829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 50e3dea5-b32d-47a1-b397-2e0ed9e1b40a · outbound

This paper cites Word discovery in visually grounded, self- supervised speech models,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Word discovery in visually grounded, self- supervised speech models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.927784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c88df96-e511-4e77-a9f6-9c7e7b1977e0 · outbound

This paper cites LibriSpeech: An ASR corpus based on public domain audio books,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? LibriSpeech: An ASR corpus based on public domain audio books,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.789272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9fa13a77-9e21-4ba2-8c37-aa56616c35ed · outbound

This paper cites An improved speech segmentation quality measure: the R-value,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? An improved speech segmentation quality measure: the R-value,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.681221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 95b9943d-dd51-4c8e-95b0-5b90f8c8482d · outbound

This paper cites On the robust automatic segmentation of spontaneous speech,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? On the robust automatic segmentation of spontaneous speech,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.669662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f5658963-2859-412e-a84e-f31de7c18945 · outbound

This paper cites Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? Bridging the gap between speech technology and natural language processing: an evaluation toolbox for term discovery systems,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.658012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3227de31-df6b-43f7-8300-a64707467467 · outbound

This paper cites A comparison of discrete and soft speech units for improved voice conversion,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? A comparison of discrete and soft speech units for improved voice conversion,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.981781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9c009f01-d33e-4077-b336-5c4c7d0fbf2d · outbound

This paper cites The Zero Resource Speech Challenge 2019: TTS without T,.

Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery? The Zero Resource Speech Challenge 2019: TTS without T,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:03:24.638821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 8ec74310-3cdb-4aec-b1cb-b84f63f40ed8 · inbound

ZeroSyl: Simple Zero-Resource Syllable Tokenization for Spoken Language Modeling cites this paper.

ZeroSyl: Simple Zero-Resource Syllable Tokenization for Spoken Language Modeling Should Top-Down Clustering Affect Boundaries in Unsupervised Word Discovery?

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T22:51:43.039657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.