{"as_of":"2026-08-15T18:21:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f7be37d6b51d88966bae4afe3abce5c65f2394ff39d86a9facb78ba0faaaecf3","coverage":[{"denominator":50,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":50,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:48:38.823233Z","state":"measured"},{"denominator":52,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":52,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+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":"2026-05-16T17:00:59.421441Z","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":"2026-05-16T17:01:07.560848Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"cited_work":{"arxiv_id":"2412.00055","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00055","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhijie Bao, Wei Chen, Shengze Xiao, Kuang Ren, Jiaao Wu, Cheng Zhong, Jiajie Peng, Xuanjing Huang, and Zhongyu Wei","venue":null,"work_id":"a44de7e8-c1ef-408d-b588-8b0a93c38406","year":2023},"citing_paper":{"arxiv_id":"2601.04638","last_updated":"2026-04-18T12:24:30Z","snapshot_observed_at":"2026-08-11T20:25:00.073106Z","submitted_at":"2026-01-08T06:14:58Z","title":"SpeechMedAssist: Efficiently and Effectively Adapting Speech Language Models for Medical Consultation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-16T17:00:59.421441Z"},"links":{"cited_paper":"/paper/2412.00055","citing_paper":"/paper/2601.04638"},"observation_digest":"sha256:94ec4133ec7f2021dd9de4735b86bc5bd0d7454eb042e614b1819dd7ff05f169","observation_id":"5e717426-5f47-440e-b410-00c0769664da","resolution":{"observed_at":"2026-05-16T17:01:07.563742Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"cited_work":{"arxiv_id":"2412.00055","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2412.00055","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Zhijie Bao, Wei Chen, Shengze Xiao, Kuang Ren, Jiaao Wu, Cheng Zhong, Jiajie Peng, Xuanjing Huang, and Zhongyu Wei","venue":null,"work_id":"a44de7e8-c1ef-408d-b588-8b0a93c38406","year":2023},"citing_paper":{"arxiv_id":"2604.23284","last_updated":"2026-04-25T12:57:25Z","snapshot_observed_at":"2026-08-15T04:34:47.620061Z","submitted_at":"2026-04-25T12:57:25Z","title":"Au-M-ol: A Unified Model for Medical Audio and Language Understanding","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-08T08:21:19.538868Z"},"links":{"cited_paper":"/paper/2412.00055","citing_paper":"/paper/2604.23284"},"observation_digest":"sha256:337737dbb60e2380c5cbbec93d97d01785de09a1ccd1e2e187a411b5d7c577c2","observation_id":"23a61bf4-11cd-4b3b-ad10-f557fefcd273","resolution":{"observed_at":"2026-05-11T20:41:10.506062Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.00055/citation-record","integrity":"/paper/2412.00055/integrity","json":"/paper/2412.00055/citation-record.json","paper":"/paper/2412.00055"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2302.04062","last_updated":"2025-04-04T09:34:37Z","snapshot_observed_at":"2026-08-13T12:48:48.004815Z","submitted_at":"2023-02-08T13:59:31Z","title":"Machine Learning for Synthetic Data Generation: A Review","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.04062","snapshot_observed_at":"2026-08-12T13:48:37.898838Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:37.898838Z"},"links":{"cited_paper":"/paper/2302.04062","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:49f44a61d5d3f37c805b3a4eb1034edd66c17e5212797eadd925e736a18ad879","observation_id":"fbae8e13-4575-4a35-8732-547c56753194","resolution":{"observed_at":"2026-08-12T13:48:37.898838Z","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":"2026-08-12T13:48:41.265724Z","title":"Fonseca and F","venue":null,"work_id":"1882604e-4edc-4cd3-ad1e-94ed5418e2c8","year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:37.903568Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:e22eb3f7b2819a64f784c0be81ba573b31a1f4bd6ef0cf31fbf085c064160330","observation_id":"eb50a450-75af-40a0-b41f-70721610b531","resolution":{"observed_at":"2026-08-12T13:48:41.270473Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15424","last_updated":"2023-08-27T04:59:59Z","snapshot_observed_at":"2026-08-14T09:27:02.874504Z","submitted_at":"2023-07-28T09:17:03Z","title":"Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis","version":2},"cited_work":{"arxiv_id":"2307.15424","doi":null,"metadata_source":"pith","pith_arxiv_id":"2307.15424","snapshot_observed_at":"2026-08-12T13:48:39.853337Z","title":"Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis","venue":"cs.LG","work_id":"b363a09b-7ebc-4f6e-b5e4-1b45f3c4120b","year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:37.907752Z"},"links":{"cited_paper":"/paper/2307.15424","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:16bdf1432dfdf2ee2c71f10c62644fe87c102fa24a12bad05c2690caca24816c","observation_id":"adf24a7d-a0ae-4700-ab55-9a57e3090ec1","resolution":{"observed_at":"2026-08-12T13:48:39.913484Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2301.13267","last_updated":"2023-01-30T20:23:26Z","snapshot_observed_at":"2026-08-14T09:27:08.916017Z","submitted_at":"2023-01-30T20:23:26Z","title":"ArchiSound: Audio Generation with Diffusion","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.13267","snapshot_observed_at":"2026-08-12T13:48:37.912046Z","title":"Schneider","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:37.912046Z"},"links":{"cited_paper":"/paper/2301.13267","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:614bd53537e0a5c1c032d9c0c823e318ba1d97518c6db6e517f3aa9e0c0fc69f","observation_id":"94354cc2-9b98-4316-9b17-341521a4bd2c","resolution":{"observed_at":"2026-08-12T13:48:37.912046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.07803","last_updated":"2021-06-14T23:26:44Z","snapshot_observed_at":"2026-08-13T19:03:49.934600Z","submitted_at":"2021-06-14T23:26:44Z","title":"SynthASR: Unlocking Synthetic Data for Speech Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.07803","snapshot_observed_at":"2026-08-12T13:48:37.916982Z","title":"Fazel, W","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:37.916982Z"},"links":{"cited_paper":"/paper/2106.07803","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:f171fe12582e2554f188923a9f4c455cbb46e274444526c9784b0d5ec8a4a761","observation_id":"a15ce408-4235-4ed1-add6-2f2d4331c2f8","resolution":{"observed_at":"2026-08-12T13:48:37.916982Z","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":"2026-08-12T13:48:41.125536Z","title":"Amazon Transcribe","venue":null,"work_id":"6eef8dac-296a-49a0-b975-a529ef1471fb","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:37.964584Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:620a307fc8098f650efe4811547b8dcb1beeea62e22463f2e6cbdbc4f0464184","observation_id":"3f7105f9-38f8-462c-af10-c4c6a2b873d6","resolution":{"observed_at":"2026-08-12T13:48:41.210206Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.944041Z","title":"Microsoft Azure Speech-to-Text","venue":null,"work_id":"b544238c-41f1-448b-bd10-410c1710c94c","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.030386Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:1e1f1190c01c42aa76d65fe153dd000caf48d80f5d46f3a205a720d20980f6ce","observation_id":"0775a5c1-ca71-4ec9-8676-5a19e7518867","resolution":{"observed_at":"2026-08-12T13:48:40.981508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.929724Z","title":null,"venue":null,"work_id":"a75899cf-24a5-4323-8363-4f709998f972","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.097472Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:29bee8d6a58ec0d2a0344062e4aa8fcab7d266f64f5a6066d6621f0545bc3cef","observation_id":"acb1aeca-d704-4532-98e0-5c6933c18f2d","resolution":{"observed_at":"2026-08-12T13:48:40.934138Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01620","last_updated":"2024-11-09T17:22:08Z","snapshot_observed_at":"2026-08-13T05:57:28.442493Z","submitted_at":"2024-04-02T04:07:22Z","title":"Voice EHR: Introducing Multimodal Audio Data for Health","version":3},"cited_work":{"arxiv_id":"2404.01620","doi":null,"metadata_source":"pith","pith_arxiv_id":"2404.01620","snapshot_observed_at":"2026-08-12T13:48:39.779832Z","title":"Voice EHR: Introducing Multimodal Audio Data for Health","venue":"cs.SD","work_id":"940430f8-a36f-4d40-8403-7a09f80ed7cb","year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.167430Z"},"links":{"cited_paper":"/paper/2404.01620","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:4bfad4934254f920efe22579f4624432629eaa10ee1f5ae50f6c451f7e98ca44","observation_id":"112015d1-23d6-4d3d-ab92-8f6fdbde3b3f","resolution":{"observed_at":"2026-08-12T13:48:39.785234Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00116","last_updated":"2024-07-02T06:51:09Z","snapshot_observed_at":"2026-08-12T23:33:38.219436Z","submitted_at":"2024-06-27T14:00:11Z","title":"Generative AI for Synthetic Data Across Multiple Medical Modalities: A Systematic Review of Recent Developments and Challenges","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00116","snapshot_observed_at":"2026-08-12T13:48:38.201081Z","title":"Ibrahim, Y","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.201081Z"},"links":{"cited_paper":"/paper/2407.00116","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:6111e9715a8941a95b08b9deb72d89d644f0dbe4d7a5db353e2ab29a318d2804","observation_id":"fccb8500-8424-443d-9239-118cee664f61","resolution":{"observed_at":"2026-08-12T13:48:38.201081Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.01842","last_updated":"2023-12-04T12:25:46Z","snapshot_observed_at":"2026-08-13T05:10:29.102424Z","submitted_at":"2023-12-04T12:25:46Z","title":"Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking","version":1},"cited_work":{"arxiv_id":"2312.01842","doi":null,"metadata_source":"pith","pith_arxiv_id":"2312.01842","snapshot_observed_at":"2026-08-12T13:48:39.695672Z","title":"Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking","venue":"cs.SD","work_id":"f5d5b079-0821-45e8-8d63-52d5c3d1cfa8","year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.206044Z"},"links":{"cited_paper":"/paper/2312.01842","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:ddc500b9ec5a06eeb628eeeeeb523fffc2ceb63e89179b2f342697837c50dd1b","observation_id":"3a5a328d-8f89-4741-be60-6ff807107304","resolution":{"observed_at":"2026-08-12T13:48:39.750658Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"gov/3238103","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:39.559399Z","title":"Goncalves, P","venue":null,"work_id":"1302aefa-06c7-4ffd-bc3e-d2000d7f0f67","year":2020},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.210908Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:ed3086c9f93a4d27741a4ff49db70399cc4c8c8c18245d43fb64b8127ddf5674","observation_id":"4a26355e-4472-49bb-8363-2e95247cb21e","resolution":{"observed_at":"2026-08-12T13:48:39.643334Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-319-57624-9_16","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:38.879663Z","title":"Yu and L","venue":null,"work_id":"1e38f670-e123-4dff-8794-e9a116cd1b7e","year":2017},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.215580Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:5561f22c091be1f6469fd6e623d8df2f1231b2fb4bf71775c3ff9101dfb39c39","observation_id":"981120dd-f24e-4b76-b92d-654e4c2d769d","resolution":{"observed_at":"2026-08-12T13:48:38.884058Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:38.220243Z","title":"Arriaga, A","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.220243Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:a417ced16a6fc09fc41038afeca1f748cfe6c8fc75741aff8f81b11bfa031052","observation_id":"48c74bd9-97d0-43b5-aafb-0470ad11339e","resolution":{"observed_at":"2026-08-12T13:48:38.220243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.04356","last_updated":"2022-12-06T18:46:04Z","snapshot_observed_at":"2026-08-15T12:11:16.120794Z","submitted_at":"2022-12-06T18:46:04Z","title":"Robust Speech Recognition via Large-Scale Weak Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.04356","snapshot_observed_at":"2026-08-12T13:48:38.224413Z","title":"Radford, J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.224413Z"},"links":{"cited_paper":"/paper/2212.04356","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:219516f85278a84cd73b7c04c254115a07dba4766d9af9ec12cc7466f26f452e","observation_id":"9e8f07bb-df5c-433e-9610-6d8c61bdf9fd","resolution":{"observed_at":"2026-08-12T13:48:38.224413Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.12807","last_updated":"2024-09-03T21:00:39Z","snapshot_observed_at":"2026-08-13T00:02:57.488035Z","submitted_at":"2024-05-21T13:58:17Z","title":"FAdam: Adam is a natural gradient optimizer using diagonal empirical Fisher information","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.12807","snapshot_observed_at":"2026-08-12T13:48:38.229216Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.229216Z"},"links":{"cited_paper":"/paper/2405.12807","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:01f965fbd68dc1ad864c2bcf0198503692324206891bd84b6db7554dd442498e","observation_id":"f02ce2cb-a966-450d-ab87-7b6420d6c494","resolution":{"observed_at":"2026-08-12T13:48:38.229216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.05084","last_updated":"2023-09-30T20:59:53Z","snapshot_observed_at":"2026-08-13T11:46:54.626048Z","submitted_at":"2023-05-08T22:54:07Z","title":"Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.05084","snapshot_observed_at":"2026-08-12T13:48:38.233334Z","title":"Rekesh, N","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.233334Z"},"links":{"cited_paper":"/paper/2305.05084","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:d27a375ff82b91ddab994bf511003a738a3abe38663de7a4faf3c8b5a1cb292b","observation_id":"dc935080-7836-458c-80f5-61a371cfb498","resolution":{"observed_at":"2026-08-12T13:48:38.233334Z","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":"document/1031712","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:39.299158Z","title":null,"venue":null,"work_id":"9142b2ab-2af1-46be-9b2e-e473a5de3689","year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.237769Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:35ded58455cd4a8f6b8dc837b9bc6d5cd714b2ac82918b6616b72a4408c50fcf","observation_id":"91a4a849-8249-4c07-903a-e3b03b5980c9","resolution":{"observed_at":"2026-08-12T13:48:39.347988Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.00070","last_updated":"2024-12-27T09:23:14Z","snapshot_observed_at":"2026-08-12T22:34:33.517850Z","submitted_at":"2024-09-30T12:11:49Z","title":"Mamba for Streaming ASR Combined with Unimodal Aggregation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.00070","snapshot_observed_at":"2026-08-12T13:48:38.295707Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.295707Z"},"links":{"cited_paper":"/paper/2410.00070","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:57ef473bbe3f54566485bed61e198c9a9390e4083e5cddd276a763be83bfaf6b","observation_id":"89362d7d-7579-4c1a-aa87-fb9bd6a99ed0","resolution":{"observed_at":"2026-08-12T13:48:38.295707Z","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":"2026-08-12T13:48:40.914589Z","title":"Lindsay, J","venue":null,"work_id":"94781779-606d-4c92-8027-aef847a8cc7c","year":2022},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.347560Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:7767af22e6a1f52473e4d7c77deda272ef8f709193a6ff4a19c84fccdc01afd2","observation_id":"b332acd8-fb36-401b-928c-990ae94df139","resolution":{"observed_at":"2026-08-12T13:48:40.919368Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"document/9053008","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:39.180930Z","title":"Rossenbach, A","venue":null,"work_id":"fffacb4e-f1a7-4779-bc32-89027b6630ba","year":2020},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.351930Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:297d438132e9dfb51239130bdee1bf14200910a78fcd478145599043d220d969","observation_id":"c7e7d28d-8757-4b18-a953-1c0342de5b0b","resolution":{"observed_at":"2026-08-12T13:48:39.187952Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2306.07691","last_updated":"2023-11-20T04:23:08Z","snapshot_observed_at":"2026-08-13T11:18:17.359420Z","submitted_at":"2023-06-13T11:04:43Z","title":"StyleTTS 2: Towards Human-Level Text-to-Speech through Style Diffusion and Adversarial Training with Large Speech Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.07691","snapshot_observed_at":"2026-08-12T13:48:38.355724Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.355724Z"},"links":{"cited_paper":"/paper/2306.07691","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:2ca839c596ffb8e41ecc9a34b32224e1408c9e41b62dc7ec9b6e89dfa45282bc","observation_id":"2ed63fa2-7dcd-4bf0-9e5b-ff260e294d0b","resolution":{"observed_at":"2026-08-12T13:48:38.355724Z","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":"2026-08-12T13:48:40.900513Z","title":"Gambs, M.-O","venue":null,"work_id":"5ca05938-31d2-40c5-80f5-0884510310cd","year":2014},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.360328Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:208d34a0e7c8823d2e76f619bf67267d50c1ea948946e95aeb8c53322353b92a","observation_id":"bb39bbaf-d374-4d7e-8faa-34918830064e","resolution":{"observed_at":"2026-08-12T13:48:40.904988Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:38.364009Z","title":"Patki, R","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.364009Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:631283b25407605b7c4f7ea023f69c39eccd044680fc45ecb43f688c4798935f","observation_id":"54607ab7-f905-4218-8e22-4eb78d77c5a7","resolution":{"observed_at":"2026-08-12T13:48:38.364009Z","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":"2026-08-12T13:48:40.886715Z","title":"https://www.mims.com/india","venue":null,"work_id":"f47b0f8f-ab09-41a3-bd54-0787a303d262","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.369635Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:808de893a3f0f050e72b72747332d17bcf3c80e1c6041c09b746224e56248e6d","observation_id":"37c39de4-9f5b-4000-b159-263de2bfefb6","resolution":{"observed_at":"2026-08-12T13:48:40.891196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.651481Z","title":"https://www.accessdata.fda.gov/scripts/cder/daf/index.cfm","venue":null,"work_id":"f2e92b93-f63c-4393-a913-df91bfebcfa4","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.373984Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:f07c73a525e0ef98726f8a7a51752169a931a5872cc8acd92ee822f67f5ad25c","observation_id":"97e1ef4e-6f7d-4925-b5ad-628fef9ce465","resolution":{"observed_at":"2026-08-12T13:48:40.777187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.572769Z","title":"https://www.icd10data.com/ICD10CM/Codes","venue":null,"work_id":"04ea7ccc-1fee-41ce-9561-cd355183979e","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.377922Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:b53eb7916a2efde720f19a02f927ee69b2ab4f7adbe9f085ada895456a320188","observation_id":"11744c6a-1c5b-4cec-8881-ecbea251e611","resolution":{"observed_at":"2026-08-12T13:48:40.577334Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.558613Z","title":"https://cloud.google.com/","venue":null,"work_id":"dd66e93e-9eb6-4f79-b17a-96c8630836a5","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.383111Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:8f10d28dd3b72a00014094d91e2226efdf077a2254a76f6bf23b8ce31c709d4c","observation_id":"aeed3bed-5503-4248-af24-5810b9b752cb","resolution":{"observed_at":"2026-08-12T13:48:40.563302Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.543196Z","title":"Scikit-learn: Machine Learning in Python","venue":null,"work_id":"866cd182-f8fa-4133-9ebf-376c23abbc44","year":2011},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.387790Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:73cefd374b7e733f0f8b53d454673f68d439ec12cf410e176ce83065a2eeb185","observation_id":"574e2ff1-8666-4cd6-ab7a-942253677e80","resolution":{"observed_at":"2026-08-12T13:48:40.548944Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.527988Z","title":"Transformers: State-of-the-Art Natural Language Processing","venue":null,"work_id":"f651490d-e6a1-464b-a24b-0725173fa751","year":2020},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.392313Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:da17fd1f9e2343bc2d9ac319cc93a4fb32dcc4a3130629f426adf7e5830b6c89","observation_id":"95eb7f3a-cbd8-4377-a0b3-eaef96db335e","resolution":{"observed_at":"2026-08-12T13:48:40.533169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.426669Z","title":"Faster Whisper","venue":null,"work_id":"632d308f-a514-46e1-804a-245c6e6d1541","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.396182Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:ba368ea4d8b1ba40f0c380ba3f8821d142bd199d0566aca3a0e9c2504d74605f","observation_id":"8df99668-e298-4d52-b816-41f120e210ef","resolution":{"observed_at":"2026-08-12T13:48:40.517763Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1910.13461","last_updated":"2019-10-29T18:01:00Z","snapshot_observed_at":"2026-07-06T08:33:12.534026Z","submitted_at":"2019-10-29T18:01:00Z","title":"BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.13461","snapshot_observed_at":"2026-08-12T13:48:38.400659Z","title":"Lewis, Y","venue":null,"work_id":null,"year":1910},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.400659Z"},"links":{"cited_paper":"/paper/1910.13461","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:9ba60a39bd2eb0dcd6a5ab6381aaf83d1d10eab7148fa1dd15017dedfbf09dd9","observation_id":"b7d2916b-7cac-44d1-828f-02a997daa595","resolution":{"observed_at":"2026-08-12T13:48:38.400659Z","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":"2026-08-12T13:48:40.261545Z","title":"Panayotov, G","venue":null,"work_id":"4fa05622-d93c-4fc9-bdf6-c7eb84fcbc42","year":2015},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.432531Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:635ecd0a90a70f706811faa3bfabcf7fdfca0844fb4b700e7e164e2181b036a9","observation_id":"8c819e56-bdc3-46a3-8a4f-ac887e6e3396","resolution":{"observed_at":"2026-08-12T13:48:40.311412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.247661Z","title":null,"venue":null,"work_id":"d378f83a-69c8-42de-baf7-f3c68d621d33","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.489398Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:04251fd7d7bbebde15288274035f50951b5cb263887da268eee4e2e0b0b035aa","observation_id":"937fd7dc-25bb-4170-baa8-2ddbda9d014f","resolution":{"observed_at":"2026-08-12T13:48:40.252010Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.232848Z","title":null,"venue":null,"work_id":"b6f78381-b6e6-451a-a72c-18371f3090af","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.533440Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:c3b25b4c198fe69468d447a8a32f99e31eabd20630ccb7aeac3041d7b98c3c87","observation_id":"22781178-8018-4a94-a56a-13ad9d5f3237","resolution":{"observed_at":"2026-08-12T13:48:40.237670Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2205.12446","last_updated":"2022-05-25T02:29:03Z","snapshot_observed_at":"2026-08-13T21:07:39.743665Z","submitted_at":"2022-05-25T02:29:03Z","title":"FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.12446","snapshot_observed_at":"2026-08-12T13:48:38.563634Z","title":"Conneau, M","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.563634Z"},"links":{"cited_paper":"/paper/2205.12446","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:7e9b2d31d85fb8bd488b27725ab8f165559e14c7eef99a369e1a2e7a57555ac6","observation_id":"91bdb8e0-65c8-41b7-9f00-2cf0a3ed07e7","resolution":{"observed_at":"2026-08-12T13:48:38.563634Z","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":"10.1371/journal.pcbi.1008228","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:38.864638Z","title":"Sainburg, M","venue":null,"work_id":"8e68b423-163a-403d-bec2-db28beb371b2","year":2020},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.568456Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:0756cd7ff52e9b373709e1ef03a89cd3bd3118492c3cac53c8702dfc5f6aace7","observation_id":"cfc4ecf2-05d9-4da2-a739-3d85f05ba3aa","resolution":{"observed_at":"2026-08-12T13:48:38.870072Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:48:38.572878Z","title":"Sainburg","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.572878Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:677dc498b9d723e0de44ce72024efb51f57d9802517bce871abf17159ce31be3","observation_id":"dd532297-1ccd-4972-9416-eb3cf8ca3de4","resolution":{"observed_at":"2026-08-12T13:48:38.572878Z","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":"2026-08-12T13:48:40.147621Z","title":"Dragon Medical One - The #1 Clinical Speech Recognition Solution","venue":null,"work_id":"9993cd33-3e23-4755-af87-b801654b38fc","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.577395Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:a42bd8f6ce463f698f596f89948e14ccae4292317de114e83819737417df82e4","observation_id":"b1b8f862-b3da-4b28-81d0-21615656b7de","resolution":{"observed_at":"2026-08-12T13:48:40.205310Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.034072Z","title":"M*Modal Fluency for Transcription - Next-generation Clinical Documentation","venue":null,"work_id":"97363cc4-fb80-4ee9-a3ea-fdd48576c9b8","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.581924Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:c37be379d2a50ec45a82765b70fdd3ed424ef80198b1ffd39ec64a8d7c1de817","observation_id":"b1c73621-1251-4c5d-8288-73ec59490922","resolution":{"observed_at":"2026-08-12T13:48:40.062342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:40.019140Z","title":"Google Speech-to-Text","venue":null,"work_id":"af7fd88a-e378-4d3f-ae15-4fe9b9252f7c","year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.586657Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:a90ee6f8dd330a30733836c29f45303b336d2bb6f7dca080ddda9dd75ae7b9d5","observation_id":"f9b42efa-a464-41ed-ac02-d7240ea63609","resolution":{"observed_at":"2026-08-12T13:48:40.023584Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2108.06209","last_updated":"2021-09-13T18:29:12Z","snapshot_observed_at":"2026-08-13T18:32:43.081420Z","submitted_at":"2021-08-07T06:29:36Z","title":"W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Training","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.06209","snapshot_observed_at":"2026-08-12T13:48:38.591195Z","title":"Chung, Y","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.591195Z"},"links":{"cited_paper":"/paper/2108.06209","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:0971f205faa52f236be9af58564f53457ee9fd46c77a53f0327f5bcdd4f69800","observation_id":"4550cdcd-8998-4326-93e6-ab62a28eb23c","resolution":{"observed_at":"2026-08-12T13:48:38.591195Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.10504","last_updated":"2022-07-20T22:31:00Z","snapshot_observed_at":"2026-08-14T17:39:54.532775Z","submitted_at":"2020-10-20T17:58:13Z","title":"Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.10504","snapshot_observed_at":"2026-08-12T13:48:38.595709Z","title":"Zhang, J","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.595709Z"},"links":{"cited_paper":"/paper/2010.10504","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:190658f44a65f58ca4fa9e3e7400d1b3c5939378b196f038f1846cbbe194bf5f","observation_id":"114253d7-daee-40f8-a393-35f1e5084e55","resolution":{"observed_at":"2026-08-12T13:48:38.595709Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.15701","last_updated":"2023-10-16T05:47:42Z","snapshot_observed_at":"2026-08-14T13:03:23.636050Z","submitted_at":"2023-09-27T14:44:10Z","title":"HyPoradise: An Open Baseline for Generative Speech Recognition with Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.15701","snapshot_observed_at":"2026-08-12T13:48:38.652142Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.652142Z"},"links":{"cited_paper":"/paper/2309.15701","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:8989ee79ddf627bdfc0672492a6c450ee7d1775529b752e5403f60dd9a600f5d","observation_id":"74fb919a-4a38-4fb7-ae9e-5e86390f3aa9","resolution":{"observed_at":"2026-08-12T13:48:38.652142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02133","last_updated":"2021-04-27T13:23:27Z","snapshot_observed_at":"2026-08-14T06:46:28.505083Z","submitted_at":"2021-04-05T20:13:36Z","title":"SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2104.02133","snapshot_observed_at":"2026-08-12T13:48:38.785927Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.785927Z"},"links":{"cited_paper":"/paper/2104.02133","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:6cb60db22351def5cf275839139e2da3dac0c1f13415703e61e9f7caa056f8b7","observation_id":"318d479a-1e71-4f16-bf20-8941f98c3830","resolution":{"observed_at":"2026-08-12T13:48:38.785927Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11596","last_updated":"2023-10-25T03:52:07Z","snapshot_observed_at":"2026-08-14T20:49:45.201003Z","submitted_at":"2023-08-22T17:44:18Z","title":"SeamlessM4T: Massively Multilingual & Multimodal Machine Translation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.11596","snapshot_observed_at":"2026-08-12T13:48:38.814413Z","title":"Barrault, Y .-A","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.814413Z"},"links":{"cited_paper":"/paper/2308.11596","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:9d60152efda4259c3f45f4e870dd281a6e9275875593210c4baed8e8245106af","observation_id":"f721e6fb-853c-4dce-b1c3-6a251ca32e41","resolution":{"observed_at":"2026-08-12T13:48:38.814413Z","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":"2026-08-12T13:48:40.004170Z","title":"Rousseau, P","venue":null,"work_id":"41c0bfc2-b087-40c5-ad10-36b167ae0d8f","year":2012},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.818848Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:596511cdcce4cdfd8615ce69f957244b6db76af57946572e260204e317aba0a9","observation_id":"826bad6b-73a0-4c8a-a424-d1370488124c","resolution":{"observed_at":"2026-08-12T13:48:40.008426Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-12T13:48:39.988872Z","title":"United-MedSyn: Medical Speech Dataset for ASR","venue":null,"work_id":"3352ca39-4cc0-4263-9e9f-7f09b9cf4021","year":2024},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.823233Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:e21b509e4d8a7cea57deada6fc346fc88c083fb786d4a12d8f0aaefb9e697125","observation_id":"008ea277-43b9-40c5-b36b-38a3ec395271","resolution":{"observed_at":"2026-08-12T13:48:39.994107Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2010.11430","last_updated":"2020-10-22T04:15:37Z","snapshot_observed_at":"2026-07-06T10:07:05.139304Z","submitted_at":"2020-10-22T04:15:37Z","title":"Self-training and Pre-training are Complementary for Speech Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.11430","snapshot_observed_at":"2026-08-12T13:48:38.628572Z","title":null,"venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":2020,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.628572Z"},"links":{"cited_paper":"/paper/2010.11430","citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:4c4b859f972b1c1cc41c0ad5ff256a5b7985e613acb7ff5a59064c5b67244586","observation_id":"fed61ab0-fb20-4c67-a083-caa27010f843","resolution":{"observed_at":"2026-08-12T13:48:38.628572Z","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":"2026-08-12T13:48:40.217436Z","title":null,"venue":null,"work_id":"48b80605-1832-4813-b6a2-788d101a8d85","year":null},"citing_paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-12T13:48:38.558011Z"},"links":{"citing_paper":"/paper/2412.00055"},"observation_digest":"sha256:9936afae9e7f4362eccc09cac2e81f79cc5884793831dfb2137f4f75a11b4514","observation_id":"e1f22c87-b3b4-4ba6-be45-99fcfa300704","resolution":{"observed_at":"2026-08-12T13:48:40.222468Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2412.00055","last_updated":"2024-11-24T17:02:48Z","latest_version":1,"primary_category":"eess.AS","snapshot_observed_at":"2026-08-14T09:26:53.575275Z","submitted_at":"2024-11-24T17:02:48Z","title":"High-precision medical speech recognition through synthetic data and semantic correction: UNITED-MEDASR"},"reference_resolution":{"displayed":50,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":8,"verified_fuzzy":17},"total_outbound_references":50},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 2 inbound Pith citation observations for arXiv:2412.00055."}