Paper Citation Record · LEDGER

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study

As of 15 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2606.31722.

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

pith.paper-citation-record.v1
2606.31722 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-01T05:52:02.121828Z

measured 15 of 15 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 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

15 of 15 outbound references displayed

verified exact4
verified fuzzy11
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 611edda9-0e49-42b4-96c4-5ddf852b0c81 · outbound

This paper cites Attention is all you need.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Attention is all you need

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.571580Z

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 5c1a6edb-f924-4127-8618-9872a7f56a7d · outbound

This paper cites Very Deep Self-Attention Networks for End-to-End Speech Recognition.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Very Deep Self-Attention Networks for End-to-End Speech Recognition

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-07-01T10:05:41.161352Z

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 208f97fa-66f6-4f4b-96b3-bf4145972688 · outbound

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

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Robust speech recognition via large-scale weak supervi- sion,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.566206Z

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 cd175024-d7a1-4c27-8bd6-670546339b55 · outbound

This paper cites Qwen3-ASR Technical Report.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Qwen3-ASR Technical Report

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-07-01T10:05:41.158998Z

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 d0023b5b-2281-4e4f-aba0-1c13872f1ce8 · outbound

This paper cites Speech technology for automatic recognition and assessment of dysarthric speech: An overview,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Speech technology for automatic recognition and assessment of dysarthric speech: An overview,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.566790Z

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 da3e01b8-85d9-42b3-adbb-fad6d9c93951 · outbound

This paper cites Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Enhancing Pre-trained ASR System Fine-tuning for Dysarthric Speech Recognition using Adversarial Data Augmentation

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:05:41.153617Z

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 325e5acb-f634-40b8-866e-52dc5a11e0ce · outbound

This paper cites Towards inclusive ASR: Investigating voice conversion for dysarthric speech recognition in low-resource languages,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Towards inclusive ASR: Investigating voice conversion for dysarthric speech recognition in low-resource languages,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.560117Z

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 fcf84ff4-faed-4358-bd22-c631acd20d35 · outbound

This paper cites Improved dysarthric speech to text conversion via TTS personalization,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Improved dysarthric speech to text conversion via TTS personalization,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.556069Z

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 39468b4d-7370-4800-af10-41806d10d408 · outbound

This paper cites Personalizing ASR for dysarthric and accented speech with limited data,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Personalizing ASR for dysarthric and accented speech with limited data,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.561996Z

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 4d52979a-9c39-4934-a707-fa215c4b1c2e · outbound

This paper cites The universal personal- izer: Few-shot dysarthric speech recognition via meta-learning,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study The universal personal- izer: Few-shot dysarthric speech recognition via meta-learning,

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-07-01T10:05:41.156369Z

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 978eace8-61e8-4d39-8506-27b069ce8c09 · outbound

This paper cites Lora: Low-rank adaptation of large language models.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study Lora: Low-rank adaptation of large language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.551632Z

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 2d827d15-92d9-4198-bb40-91eb17d67c76 · outbound

This paper cites TEQST: Tool to easily quench speechdata thirst,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study TEQST: Tool to easily quench speechdata thirst,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.552161Z

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 08d045fc-cd19-4a38-93fd-4e3dc1cc9bcd · outbound

This paper cites faster-whisper,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study faster-whisper,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.557501Z

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 620baf6b-7182-468e-bc6d-3b97f1eb578b · outbound

This paper cites The opennmt neural machine translation toolkit: 2020 edition,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study The opennmt neural machine translation toolkit: 2020 edition,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.562282Z

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 44052273-4d5c-469d-8d5b-c2c614314fe1 · outbound

This paper cites CTranslate2: Fast inference engine for transformer models,.

Adapting Foundation ASR Models to Dysarthric Speech: A Case Study CTranslate2: Fast inference engine for transformer models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-06T20:22:53.559959Z

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

No inbound Pith citation observations are available.