Paper Citation Record · LEDGER

Word stress in self-supervised speech models: A cross-linguistic comparison

As of 7 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2507.04738.

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

pith.paper-citation-record.v1
2507.04738 v1

Coverage vector

measured 43 of 43 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:44:27.047982Z

Reference resolution

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation a6ae5f4c-a0d0-48b9-bf76-b2531fdbe84b · outbound

This paper cites Word stress in self-supervised speech models: A cross-linguistic comparison.

Word stress in self-supervised speech models: A cross-linguistic comparison Word stress in self-supervised speech models: A cross-linguistic comparison

Reference 1

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This paper cites This model is pre-trained on 500,000 hours of speech recordings across 128 languages, including the five languages featuring in our study.

Word stress in self-supervised speech models: A cross-linguistic comparison This model is pre-trained on 500,000 hours of speech recordings across 128 languages, including the five languages featuring in our study

Reference 2

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This paper cites Word stress representations in connected speech The results for the language-specific stress classification test are summarized in Figure 1.

Word stress in self-supervised speech models: A cross-linguistic comparison Word stress representations in connected speech The results for the language-specific stress classification test are summarized in Figure 1

Reference 3

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This paper cites Our study fo- cused on three languages with variable (i.e.

Word stress in self-supervised speech models: A cross-linguistic comparison Our study fo- cused on three languages with variable (i.e

Reference 4

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This paper cites Zuidema (Univ.

Word stress in self-supervised speech models: A cross-linguistic comparison Zuidema (Univ

Reference 5

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This paper cites wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,.

Word stress in self-supervised speech models: A cross-linguistic comparison wav2vec 2.0: A framework for self-supervised learning of speech repre- sentations,

Reference 6

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This paper cites Large-scale self-supervised speech representation learning for automatic speaker verification,.

Word stress in self-supervised speech models: A cross-linguistic comparison Large-scale self-supervised speech representation learning for automatic speaker verification,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:29.487423Z

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This paper cites Evaluating self-supervised speech representations for speech emotion recognition,.

Word stress in self-supervised speech models: A cross-linguistic comparison Evaluating self-supervised speech representations for speech emotion recognition,

Reference 8

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This paper cites Understanding intermediate layers using linear classifier probes.

Word stress in self-supervised speech models: A cross-linguistic comparison Understanding intermediate layers using linear classifier probes

Reference 9

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This paper cites What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties,.

Word stress in self-supervised speech models: A cross-linguistic comparison What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties,

Reference 10

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Observation 2e687ffe-b674-42f1-bf22-e04b355ae040 · outbound

This paper cites Domain- informed probing of wav2vec 2.0 embeddings for phonetic fea- tures,.

Word stress in self-supervised speech models: A cross-linguistic comparison Domain- informed probing of wav2vec 2.0 embeddings for phonetic fea- tures,

Reference 11

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This paper cites Self-supervised speech representations are more phonetic than semantic,.

Word stress in self-supervised speech models: A cross-linguistic comparison Self-supervised speech representations are more phonetic than semantic,

Reference 12

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This paper cites Wave to syn- tax: Probing spoken language models for syntax,.

Word stress in self-supervised speech models: A cross-linguistic comparison Wave to syn- tax: Probing spoken language models for syntax,

Reference 13

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This paper cites The processing of stress in end-to-end automatic speech recognition models,.

Word stress in self-supervised speech models: A cross-linguistic comparison The processing of stress in end-to-end automatic speech recognition models,

Reference 14

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This paper cites Gussenhoven, The phonology of tone and intonation.

Word stress in self-supervised speech models: A cross-linguistic comparison Gussenhoven, The phonology of tone and intonation

Reference 15

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This paper cites Rounds, Hungarian: An essential grammar.

Word stress in self-supervised speech models: A cross-linguistic comparison Rounds, Hungarian: An essential grammar

Reference 16

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This paper cites Booij, The phonology of Dutch.

Word stress in self-supervised speech models: A cross-linguistic comparison Booij, The phonology of Dutch

Reference 17

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This paper cites The role of strong syllables in segmen- tation for lexical access.

Word stress in self-supervised speech models: A cross-linguistic comparison The role of strong syllables in segmen- tation for lexical access

Reference 18

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This paper cites Bossong, B.

Word stress in self-supervised speech models: A cross-linguistic comparison Bossong, B

Reference 19

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Observation e49ccba1-f7d7-4192-bebf-4481e43be157 · outbound

This paper cites Acoustic correlates and perceptual cues of word and sentence stress,.

Word stress in self-supervised speech models: A cross-linguistic comparison Acoustic correlates and perceptual cues of word and sentence stress,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:28.408610Z

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Observation 11b6cc4a-653b-4ac1-b8c4-06eef7308bd6 · outbound

This paper cites Duration and intensity as physical correlates of lin- guistic stress,.

Word stress in self-supervised speech models: A cross-linguistic comparison Duration and intensity as physical correlates of lin- guistic stress,

Reference 21

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This paper cites Spectral balance as an acoustic correlate of linguistic stress,.

Word stress in self-supervised speech models: A cross-linguistic comparison Spectral balance as an acoustic correlate of linguistic stress,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:28.180957Z

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Observation 92affa01-8672-4c81-a6a7-8b48fe7e90ee · outbound

This paper cites Acoustic correlates of word stress: A cross-linguistic survey,.

Word stress in self-supervised speech models: A cross-linguistic comparison Acoustic correlates of word stress: A cross-linguistic survey,

Reference 23

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Observation 0c0960c1-9831-4fc3-9187-3c24ce7f2a4f · outbound

This paper cites Articulation rate and the duration of syllables and stress groups in connected speech,.

Word stress in self-supervised speech models: A cross-linguistic comparison Articulation rate and the duration of syllables and stress groups in connected speech,

Reference 24

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This paper cites Coarticulation and connected speech processes,.

Word stress in self-supervised speech models: A cross-linguistic comparison Coarticulation and connected speech processes,

Reference 25

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Observation bfb635dc-6b1e-4991-8cdf-497b21db75c4 · outbound

This paper cites Lexical stress in Polish: evidence from focus and phrase-position differentiated production data,.

Word stress in self-supervised speech models: A cross-linguistic comparison Lexical stress in Polish: evidence from focus and phrase-position differentiated production data,

Reference 26

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Observation 73b4fd5a-d50a-4ca6-b6f3-5dd6de06d95e · outbound

This paper cites Perception of pre- dictable stress: A cross-linguistic investigation,.

Word stress in self-supervised speech models: A cross-linguistic comparison Perception of pre- dictable stress: A cross-linguistic investigation,

Reference 27

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Observation 7b2f7ed4-6fcb-4a0d-8b0b-042ad61b9ba9 · outbound

This paper cites Wiese, The phonology of German.

Word stress in self-supervised speech models: A cross-linguistic comparison Wiese, The phonology of German

Reference 28

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This paper cites McMahon, An introduction to English phonology.

Word stress in self-supervised speech models: A cross-linguistic comparison McMahon, An introduction to English phonology

Reference 29

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This paper cites A grid theory of stress in Polish,.

Word stress in self-supervised speech models: A cross-linguistic comparison A grid theory of stress in Polish,

Reference 30

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Observation 845ae894-6d3c-49bf-8e4e-53e6d3f9e739 · outbound

This paper cites XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale.

Word stress in self-supervised speech models: A cross-linguistic comparison XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale

Reference 31

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This paper cites Common Voice: A Massively-Multilingual Speech Corpus.

Word stress in self-supervised speech models: A cross-linguistic comparison Common Voice: A Massively-Multilingual Speech Corpus

Reference 32

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This paper cites Multilingual processing of speech via web services,.

Word stress in self-supervised speech models: A cross-linguistic comparison Multilingual processing of speech via web services,

Reference 33

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Observation 717cd81a-0288-44ca-8d5c-c7b8316e5706 · outbound

This paper cites The CELEX lexical database (cd-rom),.

Word stress in self-supervised speech models: A cross-linguistic comparison The CELEX lexical database (cd-rom),

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.212284Z

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This paper cites A general method applicable to the search for similarities in the amino acid sequence of two proteins,.

Word stress in self-supervised speech models: A cross-linguistic comparison A general method applicable to the search for similarities in the amino acid sequence of two proteins,

Reference 35

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Observation 09831dd3-d584-41b9-840a-2f8c6801e1ff · outbound

This paper cites librosa: Audio and music signal analysis in python,.

Word stress in self-supervised speech models: A cross-linguistic comparison librosa: Audio and music signal analysis in python,

Reference 36

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Observation 3a7177e6-aeba-4e9f-b341-c554a208b3fe · outbound

This paper cites Comparison of the predicted and observed sec- ondary structure of T4 phage lysozyme,.

Word stress in self-supervised speech models: A cross-linguistic comparison Comparison of the predicted and observed sec- ondary structure of T4 phage lysozyme,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.163385Z

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Observation aefd1ef9-7a7d-4fe7-98e7-73a65352ebd3 · outbound

This paper cites Assessing the accuracy of prediction algorithms for classifica- tion: an overview,.

Word stress in self-supervised speech models: A cross-linguistic comparison Assessing the accuracy of prediction algorithms for classifica- tion: an overview,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.147089Z

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This paper cites The advantages of the Matthews cor- relation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,.

Word stress in self-supervised speech models: A cross-linguistic comparison The advantages of the Matthews cor- relation coefficient (MCC) over F1 score and accuracy in binary classification evaluation,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.131749Z

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This paper cites Layer-wise analysis of a self-supervised speech representation model,.

Word stress in self-supervised speech models: A cross-linguistic comparison Layer-wise analysis of a self-supervised speech representation model,

Reference 40

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This paper cites Wav2vec behind the scenes: How end2end models learn phonet- ics,.

Word stress in self-supervised speech models: A cross-linguistic comparison Wav2vec behind the scenes: How end2end models learn phonet- ics,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.105458Z

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Observation c279378d-2d07-4c4e-b878-2dbfc98eaa77 · outbound

This paper cites An information-theoretic analysis of self-supervised discrete repre- sentations of speech,.

Word stress in self-supervised speech models: A cross-linguistic comparison An information-theoretic analysis of self-supervised discrete repre- sentations of speech,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.089959Z

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Observation 83b49367-b0ef-4271-8a47-e593583a1034 · outbound

This paper cites Phonemic competition in end-to-end ASR models,.

Word stress in self-supervised speech models: A cross-linguistic comparison Phonemic competition in end-to-end ASR models,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:44:27.073651Z

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Pith citing papers

Observation a6ae5f4c-a0d0-48b9-bf76-b2531fdbe84b · inbound