{"as_of":"2026-08-16T22:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:387367ca78a3861fd4f7ad289d61e170c315938fe88abc5ac8f6fa34b159eb63","coverage":[{"denominator":11,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":11,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:00:59.321654Z","state":"measured"},{"denominator":13,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":13,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:00:59.263827Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-05-10T10:24:22.076360Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11572","snapshot_observed_at":"2026-08-15T21:00:59.263827Z","title":"Dataset A stratified 10% sample was extracted from the fair speech dataset to benchmark ASR models on the ASR-FAIRBENCH leaderboard","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.263827Z"},"links":{"cited_paper":"/paper/2505.11572","citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:df1da7e90bc725939156860179eccc714ee9f637eebe757b8f2cb6fc9b54eb53","observation_id":"9837e751-2b81-4558-acf8-c2f82c2a2d15","resolution":{"observed_at":"2026-08-15T21:00:59.263827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"cited_work":{"arxiv_id":"2505.11572","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2505.11572","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Asr-fairbench: Measuring and benchmarking equity across speech recognition systems","venue":null,"work_id":"35625744-d9e6-4e06-a159-f5f21e61e353","year":2025},"citing_paper":{"arxiv_id":"2604.14548","last_updated":"2026-04-20T07:51:45Z","snapshot_observed_at":"2026-08-15T11:16:26.086489Z","submitted_at":"2026-04-16T02:24:59Z","title":"VoxSafeBench: Not Just What Is Said, but Who, How, and Where","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-05-10T10:19:28.041282Z"},"links":{"cited_paper":"/paper/2505.11572","citing_paper":"/paper/2604.14548"},"observation_digest":"sha256:295abe97c1d7abba6007c00458b6f6acf1ad9e26012d1264fb820b75a49763b9","observation_id":"c4f62e71-3d25-4eca-92f3-d2fdff392312","resolution":{"observed_at":"2026-05-10T10:24:22.079219Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2505.11572/citation-record","integrity":"/paper/2505.11572/integrity","json":"/paper/2505.11572/citation-record.json","paper":"/paper/2505.11572"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.527171Z","title":"However, despite impressive strides in overall accuracy, these systems of- ten exhibit significant performance disparities across diverse demographic groups","venue":null,"work_id":"5b752166-7075-499f-b4bd-fe930cbdd2ba","year":null},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.256145Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:747d15957b421a85302f48f3797bbf7a00dfb7801d3a92d0ab2be011a19bbe05","observation_id":"99cc254e-3ecf-49a6-bf89-d1972131c542","resolution":{"observed_at":"2026-08-15T21:00:59.533207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11572","snapshot_observed_at":"2026-08-15T21:00:59.263827Z","title":"Dataset A stratified 10% sample was extracted from the fair speech dataset to benchmark ASR models on the ASR-FAIRBENCH leaderboard","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.263827Z"},"links":{"cited_paper":"/paper/2505.11572","citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:df1da7e90bc725939156860179eccc714ee9f637eebe757b8f2cb6fc9b54eb53","observation_id":"9837e751-2b81-4558-acf8-c2f82c2a2d15","resolution":{"observed_at":"2026-08-15T21:00:59.263827Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.512623Z","title":"Built with React.js, leveraging an NVIDIA T4 GPU for inference, it of- fers an interactive platform for model submissions, performance analysis, and real-time leaderboard tracking","venue":null,"work_id":"2430f40e-f285-4378-8cb2-a1992ad0cd30","year":null},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.270205Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:1454adcba1724690ed5809b9e6ef9212fbed9ad6ca0dad0d6059af9eb8f599a5","observation_id":"8e9a807f-717c-4a81-8dcb-3f7da1aa56ff","resolution":{"observed_at":"2026-08-15T21:00:59.517607Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.497420Z","title":"Despite higher 1https://huggingface.co/spaces/satyamr196/ASR-FairBench 2https://github.com/SatyamR196/ASR-FairBench (a) ASR-F AIRBENCHleaderboard (b) Summarized results view","venue":null,"work_id":"7a89aaaf-c126-4f27-9a2c-ffd60303cb28","year":null},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.277548Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:351b292b373048c064f668c811f2de086657c6627f3663e4897402a2fdf0d6c3","observation_id":"2e3559ac-5ebb-4d99-9c85-90dc1ad90d50","resolution":{"observed_at":"2026-08-15T21:00:59.503021Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.476840Z","title":"Effects of talker dialect, gender & race on accuracy of bing speech and youtube automatic captions","venue":null,"work_id":"fdf2c9f7-a06a-408f-8a36-f782fda57642","year":2017},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.282491Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:776f387e1313fd9436e6f30f1d4d9a0df603d79f3c63b0b66dc698dfcc4ed017","observation_id":"cfc7689a-6888-422b-bffb-5e488ddcbca1","resolution":{"observed_at":"2026-08-15T21:00:59.484218Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2103.15122","last_updated":"2021-04-01T09:12:22Z","snapshot_observed_at":"2026-08-16T18:35:47.895141Z","submitted_at":"2021-03-28T12:52:03Z","title":"Quantifying Bias in Automatic Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.15122","snapshot_observed_at":"2026-08-15T21:00:59.289473Z","title":"Quan- tifying bias in automatic speech recognition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.289473Z"},"links":{"cited_paper":"/paper/2103.15122","citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:31ec38c5e30b695abd70fd9cceae709b31821410aec57909f3bfa2a4d038fb1c","observation_id":"d267d497-0d84-47ae-92fb-bf46a61e906e","resolution":{"observed_at":"2026-08-15T21:00:59.289473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.462826Z","title":"A deep dive into the disparity of word error rates across thousands of nptel mooc videos,","venue":null,"work_id":"393bbb7c-bae9-4a5f-9174-7c16533fb317","year":2024},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.296205Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:e6f43ed7e5c2c5dd776f8d8a3be8c01aa8d6eafc2d33276e0acf76fa0a46a902","observation_id":"b4480df1-6d53-4f3c-a957-e65a67ebdebc","resolution":{"observed_at":"2026-08-15T21:00:59.467725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.13788","last_updated":"2024-12-18T12:31:31Z","snapshot_observed_at":"2026-08-15T16:21:07.032033Z","submitted_at":"2024-12-18T12:31:31Z","title":"Open Universal Arabic ASR Leaderboard","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.13788","snapshot_observed_at":"2026-08-15T21:00:59.303453Z","title":"Open universal arabic asr leaderboard,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.303453Z"},"links":{"cited_paper":"/paper/2412.13788","citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:61c6d1168da6058d3550634abb46b55f1a409bd4fbd57b6f383542db6514b57f","observation_id":"3d34d9b3-e01e-4ea0-af8a-f43f2635d890","resolution":{"observed_at":"2026-08-15T21:00:59.303453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.447895Z","title":"Open automatic speech recognition leaderboard,","venue":null,"work_id":"9a567ec7-f615-4364-8453-d7d876676ca7","year":2023},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.310087Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:ce363f27a72b7c4de1356bc5d993a727762e78dff88bd4ce07163f1f9aa2c685","observation_id":"d4101789-f09a-4560-aae7-07bf18482e2e","resolution":{"observed_at":"2026-08-15T21:00:59.452396Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:00:59.428797Z","title":"Racial disparities in automated speech recognition,","venue":null,"work_id":"06781421-4f5f-4b0f-bea5-b6770d2fc1a7","year":2020},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.316000Z"},"links":{"citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:7d6b66afc2494be5262496d4d32c4a006db3339e7b4384c900b0268687fe1bdc","observation_id":"60c8a55f-f009-4c89-84d5-46364cc91673","resolution":{"observed_at":"2026-08-15T21:00:59.435357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12734","last_updated":"2024-08-22T20:55:17Z","snapshot_observed_at":"2026-08-16T13:24:28.104959Z","submitted_at":"2024-08-22T20:55:17Z","title":"Towards measuring fairness in speech recognition: Fair-Speech dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12734","snapshot_observed_at":"2026-08-15T21:00:59.321654Z","title":"Towards measuring fairness in speech recognition: Fair-speech dataset,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T21:00:59.321654Z"},"links":{"cited_paper":"/paper/2408.12734","citing_paper":"/paper/2505.11572"},"observation_digest":"sha256:f02951b6a4b9d5482f0ce42a8e8ee9c39e350de81762b04bfd101a4c524f16c2","observation_id":"2067908b-8826-465d-a960-0557251d5cf5","resolution":{"observed_at":"2026-08-15T21:00:59.321654Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.11572","last_updated":"2025-05-16T11:31:31Z","latest_version":1,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-15T20:54:52.435579Z","submitted_at":"2025-05-16T11:31:31Z","title":"ASR-FAIRBENCH: Measuring and Benchmarking Equity Across Speech Recognition Systems"},"reference_resolution":{"displayed":11,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":4,"verified_exact":0,"verified_fuzzy":7},"total_outbound_references":11},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 11 of 11 outbound references and 2 inbound Pith citation observations for arXiv:2505.11572."}