{"as_of":"2026-08-19T23:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:929906e1c3ce35f9305b245eaa3207ad98470893d11bf9f035a7074393e5ef69","coverage":[{"denominator":34,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":34,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:34:36.388230Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-10T20:25:33.127942Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T20:27:36.589504Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"cited_work":{"arxiv_id":"2501.04521","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.04521","snapshot_observed_at":"2026-07-10T20:27:36.589504Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","venue":"cs.SD","work_id":"7782d72d-8752-48e0-b4eb-1ddb9f6776c6","year":2025},"citing_paper":{"arxiv_id":"2607.06831","last_updated":"2026-07-07T21:57:54Z","snapshot_observed_at":"2026-08-18T21:57:27.056849Z","submitted_at":"2026-07-07T21:57:54Z","title":"Gradient-Based Speech-to-Text Alignment for Any ASR Model: From CTC to Speech LLMs","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-10T20:25:33.127942Z"},"links":{"cited_paper":"/paper/2501.04521","citing_paper":"/paper/2607.06831"},"observation_digest":"sha256:298082440a1c118241cbfb9dff5adf4cf48d8adbe5e19d20798ce03a69f1aa45","observation_id":"b5413ae3-61ac-42fb-8423-5534e301fb5b","resolution":{"observed_at":"2026-07-10T20:27:36.590845Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.04521/citation-record","integrity":"/paper/2501.04521/integrity","json":"/paper/2501.04521/citation-record.json","paper":"/paper/2501.04521"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T21:34:36.857582Z","title":"Connection- ist temporal classification: labelling unsegmented sequen ce data with recurrent neural networks,","venue":null,"work_id":"8b41bcda-5226-4423-9750-2c1e2c5bb0ba","year":2006},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.249026Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:8778a6d4da9fdaa3e32e9db579135a6f7975e10e0b6edf89bc91a559c8212a8e","observation_id":"4b5b5192-4e8d-47e7-9acc-4999b40153fe","resolution":{"observed_at":"2026-08-10T21:34:36.861993Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1211.3711","last_updated":"2012-11-14T19:25:21Z","snapshot_observed_at":"2026-08-15T00:39:21.296689Z","submitted_at":"2012-11-14T19:25:21Z","title":"Sequence Transduction with Recurrent Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1211.3711","snapshot_observed_at":"2026-08-10T21:34:36.253848Z","title":"Sequence transduction with recurrent neura l networks,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.253848Z"},"links":{"cited_paper":"/paper/1211.3711","citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:7b166ff4422ad6d1a4119b53d83dcf5ba76402614ff967d983a6dccc351405eb","observation_id":"6e2dddb1-adbe-4656-af0e-3417f5d16f98","resolution":{"observed_at":"2026-08-10T21:34:36.253848Z","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":"2026-08-10T21:34:36.844631Z","title":"Monotonic recu rrent neural network transducer and decoding strategies,","venue":null,"work_id":"caed1f4e-35d7-4571-b98c-ae9362a44620","year":2019},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.258940Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:bcbb9f3aa04738e6bd22dad862490e80fa4d2026d32704e85a564ab65b5c1354","observation_id":"42052eb8-8c27-468a-a0a0-e1e2daecf71d","resolution":{"observed_at":"2026-08-10T21:34:36.848808Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.832646Z","title":"H MM vs. CTC for Automatic Speech Recognition: Comparison Based on F ull- Sum Training from Scratch,","venue":null,"work_id":"7e54d27e-89b8-457d-a649-de6b0de5cd46","year":2023},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.263392Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:b224462b364ea093592c8f91b4cb5616404f846bf4b50760e598a644aeb484ea","observation_id":"4a34e7cf-0eae-4cf8-9a01-0488113baf35","resolution":{"observed_at":"2026-08-10T21:34:36.836828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.820384Z","title":null,"venue":null,"work_id":"fa6e3941-14ef-491a-a859-da99519001ab","year":1993},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.267986Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:95e593f3ba0dfb3e4119a3cccb40836f131c0db7093d935de0c72b98279f2284","observation_id":"ecfef1e2-e29d-471b-92bc-bd04606be8bb","resolution":{"observed_at":"2026-08-10T21:34:36.824873Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.807927Z","title":"Neural machine tra nslation of rare words with subword units,","venue":null,"work_id":"4d3abd41-196f-4aa2-8378-31ac674f42e4","year":2016},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.271878Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:9c5b0ab75dc8ab1c7490145a2907c1e2edcb40c11a11f8a1bac2441bedc0768d","observation_id":"6b1c0039-a83b-4129-8cc3-1a1cee437a4e","resolution":{"observed_at":"2026-08-10T21:34:36.811909Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.795643Z","title":"Multitask lear ning of context- dependent targets in deep neural network acoustic models,","venue":null,"work_id":"ede5f5f4-5882-41da-b75d-6a3c65af55a5","year":2016},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.276495Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:ac6f07f675e44b27d39fca8f5c8212d6fcb469f1683629ed520988e2c07b0ae3","observation_id":"13a891c8-b96c-4cd8-a1e5-f88ac4b28655","resolution":{"observed_at":"2026-08-10T21:34:36.800261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.778948Z","title":"Context-de pendent acoustic modeling without explicit phone clustering,","venue":null,"work_id":"8adfa25e-b85f-4920-9503-5bfff918b021","year":2020},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.280368Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:e44f45c0f37bca74446f364e874c2e818a3379b477b090da087cf656f869b608","observation_id":"21f54064-2dd3-4acf-842b-cc7d124d6dea","resolution":{"observed_at":"2026-08-10T21:34:36.785201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.765692Z","title":"RNN- transducer with stateless prediction network,","venue":null,"work_id":"a183e0ef-e11e-4592-9c08-2a23ac3ade99","year":2020},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.284360Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:53531cd9ca20a4d6d79984e16313cd20c68e387773b20fbf40c6cb378e388d7e","observation_id":"3426bffa-f777-42d8-98f4-7172672b56e4","resolution":{"observed_at":"2026-08-10T21:34:36.770588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.751647Z","title":"Zipformer: A faster and better encoder for automatic speech recognition,","venue":null,"work_id":"e8f54862-baad-4cc2-a3c3-ff7aeffccf71","year":2023},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.288960Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:de002943bcd03308ad68042cd323162bd91a0760f64d1da2130f8a21438f3e84","observation_id":"2ff99f79-9641-439c-b0fa-02f034008fa9","resolution":{"observed_at":"2026-08-10T21:34:36.755823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.737963Z","title":"Phoneme ba sed neural transducer for large vocabulary speech recognition,","venue":null,"work_id":"69776c93-c76a-4276-b687-d515a98c8775","year":2021},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.292907Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:70d3e932903f39224ba410e928f1504adbce2bae305b7a28ff865e8ef2e9e3fd","observation_id":"748969ed-ce33-4507-b6a5-8b635c7b8417","resolution":{"observed_at":"2026-08-10T21:34:36.743163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.725374Z","title":"Global normalization for streaming speech recogn ition in a modular framework,","venue":null,"work_id":"65dbff26-2448-4f00-a773-04aa1677b5e5","year":2022},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.296796Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:3a668f004a96f809008cdf4ea404ad89202cdc6608e38c2b0a749d63734d12ef","observation_id":"514a7b2a-6069-468d-b639-bf13b7a7bc31","resolution":{"observed_at":"2026-08-10T21:34:36.729462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2104.02387","last_updated":"2021-10-12T11:52:41Z","snapshot_observed_at":"2026-08-19T08:00:50.151580Z","submitted_at":"2021-04-06T09:24:01Z","title":"Towards Consistent Hybrid HMM Acoustic Modeling","version":3},"cited_work":{"arxiv_id":"2104.02387","doi":null,"metadata_source":"pith","pith_arxiv_id":"2104.02387","snapshot_observed_at":"2026-08-10T21:34:36.440781Z","title":"Towards Consistent Hybrid HMM Acoustic Modeling","venue":"cs.SD","work_id":"9ea84963-6835-4be7-a36e-8539a986f178","year":2021},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.301409Z"},"links":{"cited_paper":"/paper/2104.02387","citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:042f10a28b19ae07f18426697712905385af39bb35411d5e2006493ea0fe658f","observation_id":"2453f992-d550-4548-985b-cb448a79cbb0","resolution":{"observed_at":"2026-08-10T21:34:36.445733Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.713718Z","title":"Conformer: Convolutio n- Augmented Transformer for Speech Recognition,","venue":null,"work_id":"83dd76a9-a970-446c-917a-7237064099d0","year":2020},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.305727Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:3af0580152ad6a8018ebe057a98ffc5f1cfc70d26441005d239e6d0ea07bf0be","observation_id":"ea190225-4ee1-4a2e-a463-8ee75ca508c8","resolution":{"observed_at":"2026-08-10T21:34:36.717861Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.700920Z","title":"Competitive and resource efficient factored hybrid HMM systems are simpl er than you think,","venue":null,"work_id":"8cd13f06-a4c9-4061-845f-f07bf01fab63","year":2023},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.309693Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:79d89194c9cbca6b22715f41b0871af4e5ae1ef42942dcf2f97e8819e9bddcd9","observation_id":"c7eafc15-13db-4318-9231-11ea4ec66244","resolution":{"observed_at":"2026-08-10T21:34:36.705177Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.688826Z","title":"CDNN : A context dependent neural network for continuous speech recognitio n,","venue":null,"work_id":"35f3d76e-6c3c-4a31-a4e9-5a9d5a63fcf8","year":1992},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.313329Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:cd349e62ba0e86eadb5f60246bc0af71fbfaed673a7df4213757394544b1f575","observation_id":"d4ea37fd-2223-4e07-8d50-eed73406601a","resolution":{"observed_at":"2026-08-10T21:34:36.692842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.676752Z","title":"HMM vs. CTC for automatic speech recognition: Comparison based on f ull-sum training from scratch,","venue":null,"work_id":"49e09ccf-4ae2-4760-8711-07ca6c5c2a6f","year":2022},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.316943Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:23f4490da5dbef3040bf78613d49ce4443e9a23670888194823a644a4aeb4aac","observation_id":"e032a9c1-97c3-4458-9eb7-3598027c6ef1","resolution":{"observed_at":"2026-08-10T21:34:36.680793Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.665033Z","title":"Weighted finite-sta te transducers in speech recognition,","venue":null,"work_id":"1beecd67-ddc1-447e-b1a0-269fb993bdd1","year":2002},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.320606Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:61c11c2f3ed20bb06a913bc583d7a68dad2225ab5bde336bbf8b97e2a06433e9","observation_id":"1934f727-8935-4eb3-aab0-2cbd28153504","resolution":{"observed_at":"2026-08-10T21:34:36.669010Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.653267Z","title":"Regarding topology and variant fra me rates for differentiable WFST-based end-to-end asr,","venue":null,"work_id":"8981dade-c0ff-4193-a251-557c9b4d9377","year":2023},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.324481Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:d779e21fa02b834cc110d973c6cfea8bca99b9953f4bb5bf6b22b495f65c7ccc","observation_id":"3d6b370c-8953-4ad9-9fab-5812eb3fda1b","resolution":{"observed_at":"2026-08-10T21:34:36.657432Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.640797Z","title":"CTC in the co ntext of generalized full-sum HMM training,","venue":null,"work_id":"d2d31c14-4e71-48e1-85e7-568fe53e11ac","year":2017},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.328163Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:d64ca96c73cdd1275856e7c7dfeab4ed266f8c35336fd7e7047f3c827d407a18","observation_id":"84b6171d-bc8e-46d9-8550-ca60e300fc05","resolution":{"observed_at":"2026-08-10T21:34:36.645121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.627067Z","title":"Investigating the effect of label topology and training criterion on asr pe rformance and alignment quality,","venue":null,"work_id":"ff633424-d379-42ea-9630-25daa29c7d50","year":2024},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.332607Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:45326484f059bddac7cdf02bf7a417724a2113279acbd0eba545b052a7d1e839","observation_id":"f5fa6b54-4b9d-4fb7-b3f8-017fd5cffb20","resolution":{"observed_at":"2026-08-10T21:34:36.631536Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.614602Z","title":"Tree-based state tying for high accuracy acoustic modelling,","venue":null,"work_id":"75e8c03e-b275-4f18-b684-e18561ba9ad2","year":1994},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.336368Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:3cb2554c5df2e2db284cb4e6376a355832c86f9b8af6064cdb49764b2b0477c7","observation_id":"d4818f61-8556-49d5-87a5-7fce9a915400","resolution":{"observed_at":"2026-08-10T21:34:36.619003Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.602636Z","title":"Switchboard : Telephone Speech Corpus for Research and Development,","venue":null,"work_id":"424c6547-db08-4033-9d25-448015d89408","year":1992},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.340196Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:ebe8b15c99241d947053c744f0c1da80c4ec805adfd3050d66bcc4234e613284","observation_id":"f20a9d58-6eae-4e4e-aeec-2808c4281c15","resolution":{"observed_at":"2026-08-10T21:34:36.606897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.589899Z","title":"Libr iSpeech: An ASR Corpus Based on Public Domain Audio Books,","venue":null,"work_id":"cd35178c-3242-4164-a482-7bdb771576bf","year":2015},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.344341Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:44612bac46c9c9f83f1c509916482d2628b902508d511267ccad09ccf2ddf13d","observation_id":"f77601fc-8ca0-47ef-89cf-f8e53b32d924","resolution":{"observed_at":"2026-08-10T21:34:36.594196Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.576362Z","title":"RETURNN: The RWTH extensible training framework for unive rsal recurrent neural networks,","venue":null,"work_id":"1202a2a2-d05a-495e-955d-9cd4a137eadf","year":2017},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.348294Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:ba7c02e27a1926cf68788d94b2488a01c5c7e33e21b512e86dbb463515b61824","observation_id":"64ed2ed2-c9bc-4e5d-a81a-665e8c7380fe","resolution":{"observed_at":"2026-08-10T21:34:36.581250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.562358Z","title":"RASR-the RWTH Aachen u ni- versity open source speech recognition toolkit,","venue":null,"work_id":"bd3d0827-2e4b-4702-90ee-ca41fbc33c1e","year":2011},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.352241Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:45f383916c43f202a389ce805e7058919c0e194f0ac3410016c714103d665dbd","observation_id":"2461d4c6-c100-4e94-a477-c7623696ae20","resolution":{"observed_at":"2026-08-10T21:34:36.567717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.549761Z","title":"RA SR2: The RWTH ASR toolkit for generic sequence-to-sequence speech r ecogni- tion,","venue":null,"work_id":"f54335f9-fa56-4ccf-9e87-4b2e4b86e0cd","year":2023},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.356990Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:a0ca462cf24f9cb2ab0f6e4d5942f761ca826eb383e131252a95c6b3fd081895","observation_id":"25179f24-2f88-4c14-9086-280c83c2aaaa","resolution":{"observed_at":"2026-08-10T21:34:36.554140Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.536677Z","title":"Sisyphus, a workflow ma nager designed for machine translation and automatic speech reco gnition,","venue":null,"work_id":"5e2b6d08-ced2-4d94-9708-4975aa2747e2","year":2018},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.361616Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:2d0562e7bb03cf58f7a27de682d561a1a867a3df8f15b403148f2a80e91f84a6","observation_id":"7d34f993-c345-490b-a0ef-de982e80eadc","resolution":{"observed_at":"2026-08-10T21:34:36.541374Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.522914Z","title":"Gamma tone features and feature combination for large vocabulary speech recogn ition,","venue":null,"work_id":"e3c453e5-3cd7-4faa-983d-d53ecdcc7028","year":2007},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.366564Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:50edad46e6c0c0b50ac7490eeca4bb3cba045a36e2401360c8f50baf93a90141","observation_id":"a3176f0b-b1f7-4799-9804-17656ecd0747","resolution":{"observed_at":"2026-08-10T21:34:36.527462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.509740Z","title":"SpecAugment on Large Scale Datasets,","venue":null,"work_id":"c73c0657-9d7c-4133-9bdd-f4f11f8c1ab7","year":2019},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.371644Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:fe036c33bb3e7ded425c51fcd2eac05ddf3aeb4222272490335a621aeab3c99e","observation_id":"88db6780-e84f-43c3-bc25-5c77f0046b79","resolution":{"observed_at":"2026-08-10T21:34:36.514550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.10586","last_updated":"2022-08-08T09:47:40Z","snapshot_observed_at":"2026-08-19T17:48:57.759212Z","submitted_at":"2022-04-22T09:16:51Z","title":"Efficient Training of Neural Transducer for Speech Recognition","version":4},"cited_work":{"arxiv_id":"2204.10586","doi":null,"metadata_source":"pith","pith_arxiv_id":"2204.10586","snapshot_observed_at":"2026-08-10T21:34:36.420831Z","title":"Efficient Training of Neural Transducer for Speech Recognition","venue":"cs.CL","work_id":"1c945fc5-15a6-4f28-a3cf-b962556a00e5","year":2022},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.376117Z"},"links":{"cited_paper":"/paper/2204.10586","citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:e44ab14ba515a278f8ef96b2b850f79f966a6f97a917ecd17763fc770af2c801","observation_id":"7d3c61b0-964d-45ea-9563-d9f44c082871","resolution":{"observed_at":"2026-08-10T21:34:36.427499Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.495684Z","title":"Conformer: Convolution-augme nted Transformer for Speech Recognition,","venue":null,"work_id":"8877db87-9e13-4103-8963-5e7d5d446b78","year":2020},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.380045Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:5166cded320b59b61ede85f5fb905de043d462c8d18f02f6303c9c2768c409aa","observation_id":"ba0b36b3-e216-49f2-87f5-cfe87455457f","resolution":{"observed_at":"2026-08-10T21:34:36.500055Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.481449Z","title":"Super-convergence: V ery f ast training of neural networks using large learning rates,","venue":null,"work_id":"f87d0293-196c-44d4-b705-8c4d286c068d","year":2019},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.383970Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:66507215883fedaacb4d044dcbc5d769ee185c42f25b0a6db734a2198ef1525f","observation_id":"402b8d24-fa3e-45cf-b0a1-95ce71a27605","resolution":{"observed_at":"2026-08-10T21:34:36.485998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+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":"2026-08-10T21:34:36.468186Z","title":"Incorporating Nesterov Momentum into Adam,","venue":null,"work_id":"5eaa9177-20c8-4888-9546-79b23cae0af7","year":2016},"citing_paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T21:34:36.388230Z"},"links":{"citing_paper":"/paper/2501.04521"},"observation_digest":"sha256:228300b6437cd594638ead4b8d995d46d3425c70bf9cb4838e30b6cdc2431296","observation_id":"4281a5f4-a019-48a2-8cb2-068414736169","resolution":{"observed_at":"2026-08-10T21:34:36.472700Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2501.04521","last_updated":"2025-01-09T09:28:37Z","latest_version":2,"primary_category":"cs.SD","snapshot_observed_at":"2026-08-19T03:05:24.440114Z","submitted_at":"2025-01-08T14:14:19Z","title":"Right Label Context in End-to-End Training of Time-Synchronous ASR Models"},"reference_resolution":{"displayed":34,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":2,"verified_fuzzy":30},"total_outbound_references":34},"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":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2501.04521."}