Paper Citation Record · LEDGER

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People

As of 16 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2505.08215.

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

pith.paper-citation-record.v1
2505.08215 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:05:08.043093Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

26 of 26 outbound references displayed

verified exact1
verified fuzzy17
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation db5ab85f-0681-46f3-a0d6-9e826e380caa · outbound

This paper cites The 1st clarity prediction challenge: A machine learning challenge for hearing aid intelligibility prediction,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People The 1st clarity prediction challenge: A machine learning challenge for hearing aid intelligibility prediction,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.348241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 303064ba-d3d1-4613-8903-a1dea47c6afb · outbound

This paper cites The 2nd clarity prediction challenge: A machine learning challenge for hearing aid intelligibility prediction,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People The 2nd clarity prediction challenge: A machine learning challenge for hearing aid intelligibility prediction,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.337840Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7337b018-3db1-4775-8594-50b23e5f72ba · outbound

This paper cites The hearing-aid speech perception index (haspi),.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People The hearing-aid speech perception index (haspi),

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.326620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9e957f6b-4299-4421-9c9c-e9494ef8cb92 · outbound

This paper cites Effect of hearing aid technology level and individual characteristics on listener outcome measures.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Effect of hearing aid technology level and individual characteristics on listener outcome measures

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.315851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bddb5b74-ae7b-410b-bf25-07e3e40d31f2 · outbound

This paper cites Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.305600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08d3e387-d16e-40bc-9eaf-a19b54498bb2 · outbound

This paper cites Measuring speech intelligibility and hearing-aid benefit using everyday conversational sentences in real-world environments,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Measuring speech intelligibility and hearing-aid benefit using everyday conversational sentences in real-world environments,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.295197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4fa1103e-6dba-47cb-a729-245f8bc92ba5 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People On the Opportunities and Risks of Foundation Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:07.975115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 285c4474-d92c-4ba4-b5bf-2ba6b90c2247 · outbound

This paper cites wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:07.978684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b402ce4-1bd0-459e-91ae-db3e68b990f6 · outbound

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

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Hubert: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.284082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e6607902-73c4-45b2-b7ed-44f8713b8081 · outbound

This paper cites Language Models are Few-Shot Learners.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Language Models are Few-Shot Learners

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:07.985968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c95049bc-57ca-42ab-bd1e-567814fb4ff3 · outbound

This paper cites Superb: Speech processing universal performance benchmark,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Superb: Speech processing universal performance benchmark,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.272114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b3d12d3a-590c-4fbe-ac3f-801cd9b5b6c0 · outbound

This paper cites Speech foundation models on intelligibility prediction for hearing-impaired listeners,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Speech foundation models on intelligibility prediction for hearing-impaired listeners,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.260236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1612514c-da25-4508-924f-6720b0a68410 · outbound

This paper cites Non-intrusive speech intelligibility prediction for hearing- impaired users using intermediate asr features and human memory models,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Non-intrusive speech intelligibility prediction for hearing- impaired users using intermediate asr features and human memory models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.247935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e6f9e874-5a98-440f-a3b7-d35a3748549e · outbound

This paper cites What Do Speech Foundation Models Not Learn About Speech?.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People What Do Speech Foundation Models Not Learn About Speech?

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:08.000217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f05fbadc-06c5-4848-a241-bdf2f62b8303 · outbound

This paper cites Analysis of xls-r for speech quality assessment,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Analysis of xls-r for speech quality assessment,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.235399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 62578be3-561a-4009-a532-f413992788b2 · outbound

This paper cites Less is More: Accurate Speech Recognition & Translation without Web-Scale Data.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Less is More: Accurate Speech Recognition & Translation without Web-Scale Data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:08.007118Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 023063ee-082f-4dcd-92ad-bec6d07d9d4f · outbound

This paper cites Fast conformer with linearly scalable attention for efficient speech recognition,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Fast conformer with linearly scalable attention for efficient speech recognition,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.223461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8068e11b-974f-4c4d-a240-8a9c72468775 · outbound

This paper cites Attention is all you need,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Attention is all you need,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:08.014799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 269ef64d-6633-43c1-a128-71446fec4d2b · outbound

This paper cites Efficient Sequence Transduction by Jointly Predicting Tokens and Durations.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Efficient Sequence Transduction by Jointly Predicting Tokens and Durations

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:05:08.100108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 35aa6cdb-0900-4c75-bf86-adee178e2069 · outbound

This paper cites Robust speech recognition via large-scale weak supervision,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Robust speech recognition via large-scale weak supervision,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.204339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d0ab58a3-a910-41b6-a029-c039a1f497e7 · outbound

This paper cites Reproducing whisper-style training using an open-source toolkit and publicly available data,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Reproducing whisper-style training using an open-source toolkit and publicly available data,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.193062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0f249b6f-d04f-428b-95a2-7f5b4e6c5224 · outbound

This paper cites OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:08.028999Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d038561-f3d7-4d68-9545-e012578bd923 · outbound

This paper cites E-branchformer: Branchformer with enhanced merging for speech recognition,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People E-branchformer: Branchformer with enhanced merging for speech recognition,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.182441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b59ce549-454e-4d64-b33e-667528a8e654 · outbound

This paper cites Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T22:05:08.035877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29c7552d-3218-41c4-81e8-2d5c889733b8 · outbound

This paper cites Whisper-at: Noise- robust automatic speech recognizers are also strong general audio event taggers,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Whisper-at: Noise- robust automatic speech recognizers are also strong general audio event taggers,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.170781Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6f9a71df-2afc-4fbd-b416-0f3dbccfcbc9 · outbound

This paper cites Open automatic speech recognition leaderboard,.

Unveiling the Best Practices for Applying Speech Foundation Models to Speech Intelligibility Prediction for Hearing-Impaired People Open automatic speech recognition leaderboard,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:05:08.159432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.