Paper Citation Record · LEDGER

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

As of 15 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2505.23378.

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

pith.paper-citation-record.v1
2505.23378 v3

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:49:55.124828Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:49:51.541392Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T12:49:55.494626Z

Reference resolution

34 of 34 outbound references displayed

verified exact1
verified fuzzy27
unresolved4
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc24006d-ff39-4e67-be3c-f87ab9eeed47 · outbound

This paper cites an unresolved cited work.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:50:01.455456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation dab8d448-1cec-40e7-985e-cf48e657a5fc · outbound

This paper cites Dataset Our analysis uses data from a large-scale longitudinal study of shift workers [7], comprising 1,185 participants monitored over a two-week period.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Dataset Our analysis uses data from a large-scale longitudinal study of shift workers [7], comprising 1,185 participants monitored over a two-week period

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:50:01.204022Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 764cf45e-f415-4cc1-a98e-e085fbca11fc · outbound

This paper cites The target yi j can represent either time since sleep (regression) or a bi- nary fatigued/non-fatigued state (classification).

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models The target yi j can represent either time since sleep (regression) or a bi- nary fatigued/non-fatigued state (classification)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:50:00.998008Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ab7a38a9-aea2-4d6f-aa6a-b8ee618290e3 · outbound

This paper cites Model Performance We observed that, overall, prediction accuracy increases with the number of available observations per speaker (Figure 2).

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Model Performance We observed that, overall, prediction accuracy increases with the number of available observations per speaker (Figure 2)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:50:00.789831Z

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No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ea970a3c-3a8a-4ab9-99e0-9dfb3251511e · outbound

This paper cites an unresolved cited work.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:50:00.538111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 8c6f58ce-dafc-45d8-862f-a017e411e304 · outbound

This paper cites Continuous speech-based fatigue detection and transition state prediction for air traffic controllers,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Continuous speech-based fatigue detection and transition state prediction for air traffic controllers,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:58.616423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e962c80c-e723-4c5c-9892-c0e29767d4bd · outbound

This paper cites Is fatigue a disease- specific or generic symptom in chronic medical conditions?.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Is fatigue a disease- specific or generic symptom in chronic medical conditions?

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:50:00.268946Z

Source-reported events for the cited work

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Observation 614f7a38-f914-4f51-b7e3-c4b02eb82b61 · outbound

This paper cites Mental health consequences of shift work: an updated review,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Mental health consequences of shift work: an updated review,

Reference 8

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Source-reported events for the cited work

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Observation dd72b49a-433e-4b50-8c49-621ae0f33ec5 · outbound

This paper cites Fatigue monitoring through wearables: A state-of-the-art review,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Fatigue monitoring through wearables: A state-of-the-art review,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:59.603200Z

Source-reported events for the cited work

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Observation cc6810f6-3161-4891-b742-db3301498a20 · outbound

This paper cites Smartphone- based human fatigue level detection using machine learning ap- proaches,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Smartphone- based human fatigue level detection using machine learning ap- proaches,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:59.212558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c93ad97d-ed12-4c53-8945-0aed01fcb00a · outbound

This paper cites Prediction of sleepi- ness ratings from voice by man and machine,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Prediction of sleepi- ness ratings from voice by man and machine,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:58.914509Z

Source-reported events for the cited work

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Observation e56d9a33-99af-406e-90cc-661667c302cc · outbound

This paper cites Meta-learning in healthcare: A survey,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Meta-learning in healthcare: A survey,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:57.768576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 58336f76-1108-4b47-9ba9-e9816cc8b2cd · outbound

This paper cites Predicting different dimensions of fatigue from speech data: a longitudinal study in shift workers,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Predicting different dimensions of fatigue from speech data: a longitudinal study in shift workers,

Reference 13

Resolution: verified exact; doi, observed 2026-08-07T12:49:55.314347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fe227cf4-32c7-4d67-a0c4-b7a21ef54f4f · outbound

This paper cites Random-effects models for longitu- dinal data,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Random-effects models for longitu- dinal data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:58.381177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ede5ac86-e2a0-4b80-bf3e-f149523a8d59 · outbound

This paper cites Federated learning in mobile edge networks: A comprehensive survey,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Federated learning in mobile edge networks: A comprehensive survey,

Reference 15

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Source-reported events for the cited work

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Observation 335a3840-cd79-47dd-8ae2-fd1b240e9b52 · outbound

This paper cites Meta- learning in neural networks: A survey,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Meta- learning in neural networks: A survey,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:58.109699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 126738bc-3a07-4b72-987e-03ba8b2e51ab · outbound

This paper cites Learning to gen- eralize: Meta-learning for domain generalization,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Learning to gen- eralize: Meta-learning for domain generalization,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:57.829105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f0839579-c1b6-4682-a700-2332cc44093b · outbound

This paper cites Fatigue and its management in the workplace,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Fatigue and its management in the workplace,

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6c08e4a4-8cd9-4b06-bedf-a66c0f648679 · outbound

This paper cites Meta-health: learning-to-learn (meta- learning) as a next generation of deep learning exploring health- care challenges and solutions for rare disorders: a systematic anal- ysis,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Meta-health: learning-to-learn (meta- learning) as a next generation of deep learning exploring health- care challenges and solutions for rare disorders: a systematic anal- ysis,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:57.638364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 81158ef0-cde4-4087-a217-fb551bfdd242 · outbound

This paper cites Meta- learning for low-resource speech emotion recognition,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Meta- learning for low-resource speech emotion recognition,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:57.506522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 3dbf9589-7b7b-41eb-b556-5372a87e248d · outbound

This paper cites Prototypical networks for few-shot learning,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Prototypical networks for few-shot learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:57.372965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a6396400-5d61-4d44-b894-69a32534e92b · outbound

This paper cites Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Pretraining Data Mixtures Enable Narrow Model Selection Capabilities in Transformer Models

Reference 22

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Source-reported events for the cited work

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Observation d63afc40-f788-4aa4-892a-7198ee5999c9 · outbound

This paper cites Linear mixed effects models,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Linear mixed effects models,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:57.250010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 52d0f308-02cd-49b7-8d70-7fc4f686691d · outbound

This paper cites Universal paralinguistic speech representations using self-supervised con- formers,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Universal paralinguistic speech representations using self-supervised con- formers,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.319616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation e4cf310f-54d6-4b54-92f7-0570d092f036 · outbound

This paper cites Cambridge, UK: Cambridge Univer- sity Press, 1999.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Cambridge, UK: Cambridge Univer- sity Press, 1999

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.998954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6ba94c3f-dda6-4fa3-a3ab-3bfbdd44a61e · outbound

This paper cites The north wind versus a wolf: Short texts for the description and measurement of english pronunciation,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models The north wind versus a wolf: Short texts for the description and measurement of english pronunciation,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.877188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2fcedcba-ae75-4600-ac7f-004789a2a162 · outbound

This paper cites Fairbanks,Voice and Articulation Drillbook.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Fairbanks,Voice and Articulation Drillbook

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.728304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 400a3d47-079c-4c4c-8026-b78f8b69dd29 · outbound

This paper cites Analysis of phonetic balance in standard english passages,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Analysis of phonetic balance in standard english passages,

Reference 29

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Source-reported events for the cited work

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Observation 44cae838-8c89-46fb-8be3-28c4189bf18a · outbound

This paper cites Trillsson: Distilled universal par- alinguistic speech representations,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Trillsson: Distilled universal par- alinguistic speech representations,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.450505Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9e6eca70-83ca-4af5-b640-2d0b3380fa0a · outbound

This paper cites Ridge regression: Biased estima- tion for nonorthogonal problems,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Ridge regression: Biased estima- tion for nonorthogonal problems,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.164847Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 48ca6f82-d3f6-4de4-9aec-71781d37e93c · outbound

This paper cites Language models are few-shot learners,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Language models are few-shot learners,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.029312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 235367d2-ae59-45d3-9d57-7e6501c86853 · outbound

This paper cites Revealing con- founding biases: A novel benchmarking approach for aggregate- level performance metrics in health assessments,.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Revealing con- founding biases: A novel benchmarking approach for aggregate- level performance metrics in health assessments,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:55.839081Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 5378425f-ef28-4dde-83e8-6ea3bea4ef54 · outbound

This paper cites Available: https://books.google.co.uk/books?id= qN1ZAAAAMAAJ.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Available: https://books.google.co.uk/books?id= qN1ZAAAAMAAJ

Reference 1960

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:49:56.604971Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 617d5351-b908-4c1a-8852-c5d45cd7358e · outbound

This paper cites Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models.

Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Models

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T12:49:55.608499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Pith citing papers

Observation 617d5351-b908-4c1a-8852-c5d45cd7358e · inbound