{"as_of":"2026-08-17T15:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d34b313b32f04a01dd58183391e1c204f7b747146c71bfb4bcff5f172bc7e4ba","coverage":[{"denominator":63,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":63,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T16:16:37.910207Z","state":"measured"},{"denominator":64,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":64,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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":"2026-08-15T16:16:37.636867Z","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":"2026-08-15T16:16:38.122462Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"cited_work":{"arxiv_id":"2509.07139","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.07139","snapshot_observed_at":"2026-08-15T16:16:38.122462Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","venue":"cs.CL","work_id":"c4c7b221-b259-4d1d-a9b3-8c9bd3cff791","year":2025},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.636867Z"},"links":{"cited_paper":"/paper/2509.07139","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:36853ce34b0a3c251a24b115f547e4ed4682a8413dd8e27c92035fad02c909bd","observation_id":"fc5f675d-e757-4a8f-bc63-55d4076eef8b","resolution":{"observed_at":"2026-08-15T16:16:38.130032Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.07139/citation-record","integrity":"/paper/2509.07139/integrity","json":"/paper/2509.07139/citation-record.json","paper":"/paper/2509.07139"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T16:16:38.892227Z","title":"standard","venue":null,"work_id":"d2c0b58b-ff62-45f4-9cef-2f62cf0c21ec","year":2025},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.616683Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:f7c7c248b00dbb900b19683dd7c46367a6880e6635de7b5e6c61b802791e1f80","observation_id":"de631d99-9ca2-4449-a5a9-ee81d2276630","resolution":{"observed_at":"2026-08-15T16:16:38.896749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.877880Z","title":null,"venue":null,"work_id":"40d25791-2c8c-4e29-aa9d-501da77fac4b","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.621312Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:e2b2aa58c937dca55ef6a378d86e115428600f155f659b1921bbebe2e6de23f5","observation_id":"4c143fba-48f0-4b81-8ac4-699ecb710f7f","resolution":{"observed_at":"2026-08-15T16:16:38.882545Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.863176Z","title":null,"venue":null,"work_id":"00ac59d3-02db-4923-a9b1-efa1848fcc89","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.626226Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:1b1f583499cde485df487c047426c8fb5495fdc71f3704a83d2d20c026c84091","observation_id":"ab223d1e-6abf-4c94-a331-86a6f31dd1c1","resolution":{"observed_at":"2026-08-15T16:16:38.868041Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.848800Z","title":null,"venue":null,"work_id":"479fc8e0-337d-495d-b618-133a0b88ff75","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.631656Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:33d901dafe7ccc7d895a3fb6517827d71d489fa3e3f7a19a543e75e1505fb7f6","observation_id":"484c9fc6-d768-4763-80cc-2a0fc4f251c2","resolution":{"observed_at":"2026-08-15T16:16:38.853702Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"cited_work":{"arxiv_id":"2509.07139","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.07139","snapshot_observed_at":"2026-08-15T16:16:38.122462Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","venue":"cs.CL","work_id":"c4c7b221-b259-4d1d-a9b3-8c9bd3cff791","year":2025},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.636867Z"},"links":{"cited_paper":"/paper/2509.07139","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:36853ce34b0a3c251a24b115f547e4ed4682a8413dd8e27c92035fad02c909bd","observation_id":"fc5f675d-e757-4a8f-bc63-55d4076eef8b","resolution":{"observed_at":"2026-08-15T16:16:38.130032Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.833100Z","title":"The hidden test set con- tains data sourced from the same corpora as the development set along with 4 additional corpora [34–37]","venue":null,"work_id":"b61ece88-3dab-47cf-af0c-fbd1407dccf3","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.642125Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:5b81f55d1e82528d71a9be37a62a8c9f9e904ec993dde1e65fb989582b25f96f","observation_id":"c41d9204-4998-474f-9a91-c92977c7133f","resolution":{"observed_at":"2026-08-15T16:16:38.838987Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.817704Z","title":"\"\" 3Args: 4waveform (np.array): speech waveform 5Returns: 6pred_lid (str): ISO3 code of LID pred 7pred_asr (str): predicted transcript 8","venue":null,"work_id":"803a778a-ee12-4fd3-b0b8-1c665c9b9a88","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.646931Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:abbdc336bcbd5abd2efcaeff6a7629af65afdb8b141263cc733718acee556599","observation_id":"66257566-f0e3-4e73-b8fa-7148c570d7f0","resolution":{"observed_at":"2026-08-15T16:16:38.822556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.802045Z","title":null,"venue":null,"work_id":"9af5e9db-664b-4788-a748-fa1cd2afbd3d","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.651680Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:6db560a1014c8f2cb7f73c32cc31de08c43a0687f5406795281bc26592f34e39","observation_id":"bd911933-4b36-405b-8b7f-d1721a68ee2e","resolution":{"observed_at":"2026-08-15T16:16:38.807152Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.788268Z","title":null,"venue":null,"work_id":"42a4d73d-98ff-4222-b66e-cb5272df61bc","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.656498Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:9157c74f1dc0c8fa899ffc5fd5b43447abb1580c71ba9067f1e49bb6c13bbeeb","observation_id":"16f95097-d7ec-4f5c-80bd-ee5e943a54dd","resolution":{"observed_at":"2026-08-15T16:16:38.792628Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.774751Z","title":null,"venue":null,"work_id":"6d4ae198-1d9b-48f2-8709-84155ab18be2","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.661616Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:6bc9760d107cf668bd6aed2e676ca13a5047b4b4be04109823f9431be18f850a","observation_id":"7774eeea-b64c-469a-aff3-6574f5ab43d6","resolution":{"observed_at":"2026-08-15T16:16:38.779193Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.761202Z","title":"Since all of these models are self-supervised, we develop ASR systems via fine-tuning on the ML-SUPERB 2.0 public set [14]","venue":null,"work_id":"003714b4-d345-4280-b3ef-b18ad1552c32","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.666196Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:631162299ebe3d8f6686d07fa80c8cb64f10119be56b5841ffc388c92e93555c","observation_id":"e663b56b-c5b0-492e-95e3-90e3964986d2","resolution":{"observed_at":"2026-08-15T16:16:38.765738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.583063Z","title":"Superb@ slt 2022: Challenge on general- ization and efficiency of self-supervised speech representation learning,","venue":null,"work_id":"323e4d0b-6df7-4c1f-8797-68e6b2dbb09f","year":2022},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.732017Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:08c7a68ee35c9a5d44a0e234d4fccdcce919f8cdd16cbbe29a3f2bed488a3693","observation_id":"6dc5ea14-434b-4d58-b09b-85a79daee2c4","resolution":{"observed_at":"2026-08-15T16:16:38.587963Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.731918Z","title":"The challenge introduces a novel multilin- gual test suite of accented and dialect speech and uses new metrics to test the robustness of ASR systems","venue":null,"work_id":"7e8f5cff-2d48-45c8-b641-f8d21bd3f47c","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.675676Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:7d0e706a12197d7f3d882bd5d9e990d2e3acd1bf110e2e3240e99161161829d1","observation_id":"040a659d-2eb0-4eed-aa79-2f32009a5521","resolution":{"observed_at":"2026-08-15T16:16:38.737000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.715376Z","title":"Wav2vec 2.0: A framework for self- supervised learning of speech representations,","venue":null,"work_id":"48a6b6a2-f61d-4e64-a7b2-2c1e0d82425b","year":2020},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.680771Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:49702c5283f63ba1f63490336187537e7f4b1bb1e9c9bc397816fce220e6dd8c","observation_id":"b3bdd008-6e1c-4f7d-9e7c-e7cc5c0bc48e","resolution":{"observed_at":"2026-08-15T16:16:38.720761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.700233Z","title":"Hubert: Self-supervised speech representation learning by masked prediction of hidden units,","venue":null,"work_id":"ea202466-97da-4f1a-9c57-d7dfc9d69257","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.685471Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:296bdaf60db23f250e5bfec338d78f2d5a3c7a813906a9aa44b7a1284e27070e","observation_id":"d3032ef4-ea2d-4f59-afb3-6a92517e6de6","resolution":{"observed_at":"2026-08-15T16:16:38.705250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.685242Z","title":"Robust speech recognition via large-scale weak supervision,","venue":null,"work_id":"527fa724-4e7a-4672-89fc-badf501ea00b","year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.690260Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:a49c5af524cd3e1e4ce8f0cd263702deeb0161d9fccc3f8a2179f6f292135f55","observation_id":"d3e44ed5-3409-4bd6-a470-77a2b3bc2150","resolution":{"observed_at":"2026-08-15T16:16:38.690325Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2401.16658","last_updated":"2024-08-27T02:15:49Z","snapshot_observed_at":"2026-08-16T14:23:25.481967Z","submitted_at":"2024-01-30T01:22:18Z","title":"OWSM v3.1: Better and Faster Open Whisper-Style Speech Models based on E-Branchformer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.16658","snapshot_observed_at":"2026-08-15T16:16:37.696161Z","title":"Owsm v3. 1: Better and faster open whisper- style speech models based on e-branchformer,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.696161Z"},"links":{"cited_paper":"/paper/2401.16658","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:b81214f87c9a111a8e8af8f3ba81dcc373eefe5df6f54bec8e20106bfbb208c1","observation_id":"ddcc2970-6156-4895-bb1e-c36b72429415","resolution":{"observed_at":"2026-08-15T16:16:37.696161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.01037","last_updated":"2023-09-25T01:20:23Z","snapshot_observed_at":"2026-08-16T15:51:28.593814Z","submitted_at":"2023-03-02T07:47:18Z","title":"Google USM: Scaling Automatic Speech Recognition Beyond 100 Languages","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.01037","snapshot_observed_at":"2026-08-15T16:16:37.701203Z","title":"Google usm: Scaling automatic speech recogni- tion beyond 100 languages,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.701203Z"},"links":{"cited_paper":"/paper/2303.01037","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:8d246639da940d42cfdf6a06d3bb9abb607a8082968926ed1107acfd2a436540","observation_id":"f3b62685-fee0-4190-a213-962adf70e875","resolution":{"observed_at":"2026-08-15T16:16:37.701203Z","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":"2026-08-15T16:16:38.671079Z","title":"Self-supervised speech representations still struggle with african american vernacular english,","venue":null,"work_id":"85009855-6845-4c63-8688-105463645e09","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.706018Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:65543b5d5faecf0718cefee0b943353e3e341b8dd6f25a03668e8795ae6998ec","observation_id":"1f3ccd39-f221-4734-950c-75ef6d178936","resolution":{"observed_at":"2026-08-15T16:16:38.675457Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.657394Z","title":"Towards inclusive automatic speech recognition,","venue":null,"work_id":"36a83e6e-739d-4747-8509-605a49d8dec4","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.709981Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:c0367dc292ee26654ea25a6b619a57ef7bb426f7db81a4f910e3b1d4cf861943","observation_id":"fab1cb7d-47a0-457f-9813-411e708a92cd","resolution":{"observed_at":"2026-08-15T16:16:38.661747Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.643597Z","title":"Findings of the IWSLT 2023 Evaluation Campaign,","venue":null,"work_id":"1f870674-c16f-47a7-a107-372cddbf812f","year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.714430Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:07bb8c3de019c2069b197134dfc5c2cef15c487dca680e1b20ec9208f612b84b","observation_id":"1bbf7d44-e0a1-479f-8b9c-19062dcd11e6","resolution":{"observed_at":"2026-08-15T16:16:38.648000Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.628435Z","title":"SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Genera- tive Capabilities,","venue":null,"work_id":"06ccddd5-f6f7-403c-88b4-c23675d7af7f","year":2022},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.718565Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:3ec74431e00fc79604946cd26250fd44f0f45cf1d8b6f543902dbc57dc9c31b0","observation_id":"d35adab2-e89b-40e4-8204-38b790dd0916","resolution":{"observed_at":"2026-08-15T16:16:38.633568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.613206Z","title":"A V-SUPERB: A Multi-Task Evaluation Bench- mark for Audio-Visual Representation Models,","venue":null,"work_id":"a7b2d868-b883-42eb-a20c-6308e4f58cf6","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.722745Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:8bb092aec30912ccb47d81e18b2bbedb2897a4028236b5a2050b40d0fe03469f","observation_id":"4d1636ec-4af0-4d45-a3fd-698b3687bef9","resolution":{"observed_at":"2026-08-15T16:16:38.618050Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.598471Z","title":"ML-SUPERB: Multilingual Speech Universal PERformance Benchmark,","venue":null,"work_id":"90b9cc5e-f84e-41c4-87a2-56e48d4ad5e1","year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.727299Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:e9171fc0743ad5a291a8089683f14b32a7ba1f0e61aadc5215bedf0f32a52830","observation_id":"a26f6842-2a32-43a5-9a56-9ad62c1ab07a","resolution":{"observed_at":"2026-08-15T16:16:38.603442Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.433989Z","title":"Sada: Saudi audio dataset for arabic,","venue":null,"work_id":"63174248-2395-4163-a03b-c7fed39a4365","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.794718Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:b82f4c4b8e51b5ea7078e50b4c3e2daeef5d5be6d712bb83080a2d8a1209f735","observation_id":"d7b00ff8-0310-4c95-be99-b99aa9e88013","resolution":{"observed_at":"2026-08-15T16:16:38.439415Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.568185Z","title":"Findings of the 2023 ML-SUPERB Challenge: Pre- Training And Evaluation Over More Languages And Beyond,","venue":null,"work_id":"49131b08-b1fd-425e-ac6a-a0387bace98f","year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.736668Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:94baadbb8cd4faf29e6844e985f086db6e997a6e099b58d41e14567c4c0d3df7","observation_id":"ee291495-6a38-48e1-bfd0-33077e4cd356","resolution":{"observed_at":"2026-08-15T16:16:38.572961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.552714Z","title":"ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets,","venue":null,"work_id":"1dc7bec4-a02b-4f33-9a78-f2181fd9fd34","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.741316Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:c51483afe3e214028d6817b1cddef51303daecb75a335809088bb10a6be398bf","observation_id":"ad19c228-ce3d-45b3-8118-d0a9da88ee82","resolution":{"observed_at":"2026-08-15T16:16:38.557775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.538594Z","title":"SUPERB: Speech Processing Universal PERfor- mance Benchmark,","venue":null,"work_id":"e8c12776-dc82-42c5-8092-e87cfea5bbad","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.746683Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:981d238287221ff70c442ff1a2d6206334bf19e5ccf014525c3ec51b36cd08d2","observation_id":"210af595-383f-4979-971c-89616d36f716","resolution":{"observed_at":"2026-08-15T16:16:38.542824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.525614Z","title":"Dynabench: Rethinking benchmarking in NLP,","venue":null,"work_id":"0d8a1f64-d235-41c4-bc81-55ad382d18d8","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.751802Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:01157a9eb5299991d3654f41a8d0c54ec01cc3d5103b0ffec917f34456413c24","observation_id":"66f6b343-8b93-42dc-8375-ef7d438da6a3","resolution":{"observed_at":"2026-08-15T16:16:38.529533Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.509942Z","title":"Scaling speech technology to 1,000+ lan- guages,","venue":null,"work_id":"a428b00c-1ef6-4b4a-875f-1fe04bd9a089","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.757005Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:3ec80310b31bff7cc4aaaf3152580e0dbe322d95bad64db549ffd3544f127b05","observation_id":"caba7e39-0f54-4651-9fbc-8adef189c600","resolution":{"observed_at":"2026-08-15T16:16:38.515901Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.00837","last_updated":"2024-07-02T17:23:44Z","snapshot_observed_at":"2026-08-16T13:38:19.826616Z","submitted_at":"2024-06-30T21:40:26Z","title":"Towards Robust Speech Representation Learning for Thousands of Languages","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.00837","snapshot_observed_at":"2026-08-15T16:16:37.761527Z","title":"Towards robust speech representation learning for thousands of languages,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.761527Z"},"links":{"cited_paper":"/paper/2407.00837","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:a132195a39f3e1bea7aea4650ad82f33b2ecdbdce5082aa91475f6992ae319d8","observation_id":"4780c053-c0f3-4e61-8c0d-5fb0a35c4a62","resolution":{"observed_at":"2026-08-15T16:16:37.761527Z","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":"2026-08-15T16:16:38.495453Z","title":"Artie bias corpus: An open dataset for detecting demographic bias in speech applications,","venue":null,"work_id":"ba26e820-8989-46f3-a729-d3a29da895af","year":2020},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.766714Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:894fa63cb4b41ce70422d9d977693428e0ffd15bf39894590f58e88c18655583","observation_id":"3ff66918-3ebb-44f4-839b-37366a819b4a","resolution":{"observed_at":"2026-08-15T16:16:38.500154Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.480741Z","title":"Speech Accent Archive,","venue":null,"work_id":"491dadb4-12b8-4a30-9cc7-4add18b59ed1","year":2015},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.772005Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:a092d50c33e3fe28b5b381f5e1ebcf35acd3a2ff09636eebdaec3366fecf689d","observation_id":"141c0f25-709a-41df-b99f-b907eb0ebcb2","resolution":{"observed_at":"2026-08-15T16:16:38.485560Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.12734","last_updated":"2024-08-22T20:55:17Z","snapshot_observed_at":"2026-08-16T13:24:28.104959Z","submitted_at":"2024-08-22T20:55:17Z","title":"Towards measuring fairness in speech recognition: Fair-Speech dataset","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.12734","snapshot_observed_at":"2026-08-15T16:16:37.777219Z","title":"Towards measuring fairness in speech recognition: Fair-speech dataset,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.777219Z"},"links":{"cited_paper":"/paper/2408.12734","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:4b650d8a2209c321882502af81e9eb268a08821322f9e3675f71ae1c362b71f6","observation_id":"82cb4b4c-9e18-424e-ba87-cec770c2e4d2","resolution":{"observed_at":"2026-08-15T16:16:37.777219Z","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":"2026-08-15T16:16:38.465009Z","title":"Fleurs: Few-shot learning evaluation of uni- versal representations of speech,","venue":null,"work_id":"2e8a69bc-3e8e-4d1d-8f47-561a93eb1d1a","year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.782398Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:7c27d63000d6677423206cb3a836922f77b3d3cfb1866beff17f569a143dddab","observation_id":"96c7fe15-902d-44ab-89cc-1078fef94ad6","resolution":{"observed_at":"2026-08-15T16:16:38.470443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.449728Z","title":"Common voice: A massively-multilingual speech corpus,","venue":null,"work_id":"09c687c8-7313-4d91-bafe-5b9dc16f83ff","year":2020},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.786340Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:bd4896b0648348c8a6aa74c77f484c37dc8b15d7a036369e0a92e6518b77232a","observation_id":"5dc9c1b3-466f-4b00-a96f-4c714bef1769","resolution":{"observed_at":"2026-08-15T16:16:38.454565Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2308.11596","last_updated":"2023-10-25T03:52:07Z","snapshot_observed_at":"2026-08-16T15:06:35.991757Z","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":"2026-08-15T16:16:37.790495Z","title":"Seamlessm4t-massively multilingual & mul- timodal machine translation,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.790495Z"},"links":{"cited_paper":"/paper/2308.11596","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:910c04bb72ad8a9735ec4ed9d7d8c16a8fd35cee377fa13364e04e640f301d0d","observation_id":"81eaba92-bba0-4f4e-850b-941f4a837efa","resolution":{"observed_at":"2026-08-15T16:16:37.790495Z","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":"2026-08-15T16:16:38.254631Z","title":"Findings of the WMT 2021 shared task on large-scale multilingual machine translation,","venue":null,"work_id":"220d64be-c306-44ef-bc30-fcff4cf63b70","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.853208Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:1f09fc9ffa6b4f397c7c1083b0f4eb050203361020ea6a0cc9ac2895dee0a109","observation_id":"2d8a4589-7bc8-40df-b684-099efb44ff2a","resolution":{"observed_at":"2026-08-15T16:16:38.258905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.417338Z","title":"V oxpopuli: A large-scale multilingual speech corpus for representation learning, semi-supervised learning and interpretation,","venue":null,"work_id":"ac340cdd-34c0-46e4-8015-177723f1380e","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.798741Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:c45e120f76ae7bbb8bce3f3280586ba7252e62263515c652d92f22c4cc91397a","observation_id":"8df50f6e-fa5f-40e9-814a-4f98634bde35","resolution":{"observed_at":"2026-08-15T16:16:38.422863Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.402231Z","title":"Open-source multi-speaker corpora of the English accents in the British isles,","venue":null,"work_id":"e1926e36-61de-44a3-b88a-54c55b46e515","year":2020},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.802918Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:6bf17e4b90c94710830a299926da1b351f77c3a1994ab0e229ca457b20fd9537","observation_id":"426972c6-5bb2-4161-bcf0-1b8c73964a97","resolution":{"observed_at":"2026-08-15T16:16:38.406998Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.387520Z","title":"Globe: A high-quality english corpus with global accents for zero-shot speaker adaptive text- to-speech,","venue":null,"work_id":"8e80091d-1aae-4729-95a1-8ca34bb4d0ce","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.807584Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:216e6187a45d9a83bdf47b46fd481d0b5a7a080b28a164ed79db743ea60ccfe5","observation_id":"566d2070-7066-4352-9c47-5f782c6e98e2","resolution":{"observed_at":"2026-08-15T16:16:38.392299Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.374425Z","title":"L2-arctic: A non-native english speech corpus,","venue":null,"work_id":"c4548ae5-f6c9-4ace-831b-a9aba4fadb5a","year":2018},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.811782Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:272c3151de49bb69cfba5641c93712928aa097366290407c08d22e952211370c","observation_id":"16d19aa3-28c8-4a9d-9ca1-40d6c1869f00","resolution":{"observed_at":"2026-08-15T16:16:38.378549Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.359947Z","title":"Dogan-Schönberger, J","venue":null,"work_id":"0bc7c370-0b5a-4991-8bea-bd8fb5020e93","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.816263Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:c4c4c4ef7d89af422d3f4537be4e7086d6c27a4ccbdbf5630446b903e9a6c134","observation_id":"fb957ccc-ca98-42e2-8eb7-a723267e2295","resolution":{"observed_at":"2026-08-15T16:16:38.364857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.345030Z","title":"Speech recognition for greek dialects: A challenging benchmark,","venue":null,"work_id":"cbd30b07-dd92-4400-9666-ae8a972965c7","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.820661Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:5fdae715a3d39118df655a9948dd45951f37350153f3f95530d53d8cd8682747","observation_id":"113b992d-d453-4366-810e-0e15f6be9ba4","resolution":{"observed_at":"2026-08-15T16:16:38.349812Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.330645Z","title":"Interspeech 2018 low resource au- tomatic speech recognition challenge for indian languages,","venue":null,"work_id":"c49e412f-bdfd-4819-ac32-47c77144c907","year":2018},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.825328Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:7f1d9f216d406fed0360af6851b4d2cbd9669cf359c7006c82b8323eaeeafd2d","observation_id":"756b7290-361a-4ce1-a857-c735b4013eb7","resolution":{"observed_at":"2026-08-15T16:16:38.335751Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.315649Z","title":"Crowdsourcing Latin American Span- ish for low-resource text-to-speech,","venue":null,"work_id":"eaddfa55-247d-48a5-b561-0a0550e101fd","year":2020},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.830061Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:20726f233cfb95572d5d3abd5e34cc7ca51280466238940a3a3a5c4f7c10c081","observation_id":"3237c8af-a348-487a-9fb7-8542319aa2c6","resolution":{"observed_at":"2026-08-15T16:16:38.320731Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.299554Z","title":"Leveraging data collection and un- supervised learning for code-switched tunisian arabic automatic speech recognition,","venue":null,"work_id":"32271746-409e-44d1-aaae-3b11d762b9a6","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.834932Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:d041a3607dfbce6515c6838b8e7ae05a8e09eb011987ba569743836df448d0b8","observation_id":"444f61a2-fc90-4bf1-8d39-7de5ecae4bec","resolution":{"observed_at":"2026-08-15T16:16:38.304645Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.04527","last_updated":"2024-10-06T15:41:38Z","snapshot_observed_at":"2026-08-16T13:12:09.501001Z","submitted_at":"2024-10-06T15:41:38Z","title":"Casablanca: Data and Models for Multidialectal Arabic Speech Recognition","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.04527","snapshot_observed_at":"2026-08-15T16:16:37.839473Z","title":"Casablanca: Data and models for multidialec- tal arabic speech recognition,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.839473Z"},"links":{"cited_paper":"/paper/2410.04527","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:bb2cba83cc32667b63e92d464576972cfc0c1bfac172f389e0c381ddcfb6a008","observation_id":"bdcd86b3-edeb-4955-be61-50a719fc68b7","resolution":{"observed_at":"2026-08-15T16:16:37.839473Z","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":"2026-08-15T16:16:38.284679Z","title":"Automatic speech recognition datasets in Can- tonese: A survey and new dataset,","venue":null,"work_id":"af401237-fe52-43f7-9d86-c72fda49bd2e","year":2022},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.844234Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:bb615b379cdb3bf95d29d148598cb629e5bb20eff9726173bfa9dd81b8ac828d","observation_id":"c54f854d-93c8-4ae3-a590-b931254fb5f4","resolution":{"observed_at":"2026-08-15T16:16:38.289486Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.746689Z","title":"These are run in azero-shot manner, as they are designed to be used out-of-the-box","venue":null,"work_id":"ccf3f6a7-f244-4d2e-9fce-2fa100e373a8","year":null},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.670697Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:fa084b630dd8cce004ca08a396e33c51e99ac6b7af4deaf2e3593d0a3dd09393","observation_id":"5eb172f4-2759-4fd3-afdb-c32abf1f1807","resolution":{"observed_at":"2026-08-15T16:16:38.752004Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.268689Z","title":"Finnish dialect identification: The effect of audio and text,","venue":null,"work_id":"58744d6c-4de0-427d-8f23-fe43ffeaa688","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.848843Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:e7b5bb3e8d8b41ea22710fcfbb881fef2c101a3cf914da4294d91cdaa403f0f6","observation_id":"ff04132a-89b9-4531-8f7e-76eb4ac7991f","resolution":{"observed_at":"2026-08-15T16:16:38.274479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.238758Z","title":"Findings of the 2021 conference on ma- chine translation (WMT21),","venue":null,"work_id":"adebeebd-a30d-406f-a613-b60931a1cc30","year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.857200Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:aadee602e77e4b64cea21ae79c28c3c570ffd5033b73eb530daf188f4f55358c","observation_id":"73107445-82a0-4021-88b5-2f0b641d661f","resolution":{"observed_at":"2026-08-15T16:16:38.244629Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.222092Z","title":"OWSM v3.1: Better and Faster Open Whisper- Style Speech Models based on E-Branchformer,","venue":null,"work_id":"c5444840-607e-4447-90e7-5213f0f88f7e","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.861520Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:bbdacceb945280a04b8a66b2ef28b8bb381d9e4ef4c302f79629c7a31fb9a8f5","observation_id":"83a6f4bc-46bb-4f7f-952a-4a485d8848b6","resolution":{"observed_at":"2026-08-15T16:16:38.227954Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-15T16:16:37.866927Z","title":"LLaMA: Open and efficient foundation lan- guage models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.866927Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:b6affdd5a243c5fc9acdc9c2800acfa36888741c708e240ddd1261666ed77363","observation_id":"f7495814-cf0d-455d-aca0-b531c1878b76","resolution":{"observed_at":"2026-08-15T16:16:37.866927Z","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":"2026-08-15T16:16:38.205879Z","title":"Language models are few-shot learners,","venue":null,"work_id":"5902a2ac-f18b-4dc0-8f1f-f3d3403635ae","year":2020},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.872358Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:734275167444d6f883f41ffdc0d8bee174bbf5b5080a4528cc0ce3dbd890b8fa","observation_id":"9cf07fb7-661b-4a4d-befb-a63d7adc2869","resolution":{"observed_at":"2026-08-15T16:16:38.211147Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.07725","last_updated":"2024-06-11T21:08:47Z","snapshot_observed_at":"2026-08-16T13:43:59.359252Z","submitted_at":"2024-06-11T21:08:47Z","title":"The Interspeech 2024 Challenge on Speech Processing Using Discrete Units","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.07725","snapshot_observed_at":"2026-08-15T16:16:37.877180Z","title":"The interspeech 2024 challenge on speech pro- cessing using discrete units,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.877180Z"},"links":{"cited_paper":"/paper/2406.07725","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:2cf27d671902a750731474371b4f850ec0374dc9efe500f4df2879e32dd6c35a","observation_id":"48e633f2-cf7b-4a39-80ea-52a96ea96596","resolution":{"observed_at":"2026-08-15T16:16:37.877180Z","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":"2026-08-15T16:16:38.190283Z","title":"NeurIPS 2024 competition proposal: UR- GENT challenge,","venue":null,"work_id":"99b0576e-624f-4412-9348-6684ba14be33","year":2024},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.881856Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:c6ebfe98e947e5d415f85e7f9313f9a620399f5d14637a1c7d436c6092333fa7","observation_id":"ae9fd73c-ce44-41c7-8dda-3b529975b920","resolution":{"observed_at":"2026-08-15T16:16:38.195572Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.174047Z","title":"Wavlm: Large-scale self-supervised pre-training for full stack speech processing,","venue":null,"work_id":"fad831c4-bac2-44b1-b7f1-43b10c44b225","year":2022},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.886495Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:232922a441ca652df50a032d39154df9e7a87d31b80f7fd9484c80c32f79cd36","observation_id":"8c773e1b-01a4-487d-87eb-c35a8b3a8701","resolution":{"observed_at":"2026-08-15T16:16:38.178873Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2006.13979","last_updated":"2020-12-15T23:19:19Z","snapshot_observed_at":"2026-08-13T17:50:33.934751Z","submitted_at":"2020-06-24T18:25:05Z","title":"Unsupervised Cross-lingual Representation Learning for Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.13979","snapshot_observed_at":"2026-08-15T16:16:37.890944Z","title":"Unsupervised cross-lingual represen- tation learning for speech recognition,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.890944Z"},"links":{"cited_paper":"/paper/2006.13979","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:728f2874daf10d3170f002526b565119e4a3c57d20d3e292c7b0eef62f59846c","observation_id":"76d1ea6f-c6b8-4d33-a5c3-d19525a93f52","resolution":{"observed_at":"2026-08-15T16:16:37.890944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.09296","last_updated":"2021-12-16T18:29:22Z","snapshot_observed_at":"2026-08-16T17:41:05.963766Z","submitted_at":"2021-11-17T18:49:42Z","title":"XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.09296","snapshot_observed_at":"2026-08-15T16:16:37.895958Z","title":"Xls-r: Self-supervised cross-lingual speech rep- resentation learning at scale,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.895958Z"},"links":{"cited_paper":"/paper/2111.09296","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:d5a2fc12904feac6ea242860d6795056d7dba0fb3569b84df25bdfacbf85b914","observation_id":"70ccc7c0-ae92-4041-83a6-1d4010e5e8a6","resolution":{"observed_at":"2026-08-15T16:16:37.895958Z","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":"2026-08-15T16:16:38.158393Z","title":"Attention is all you need,","venue":null,"work_id":"27e09447-4224-4f18-b39c-b8ceecbb6cd1","year":2017},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.901015Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:9ff14698ece5e76e4d6610be004fa620da827f3cd482d4bb1565c73255348d8a","observation_id":"05e9e296-7b1d-4692-a060-cc0644f9f303","resolution":{"observed_at":"2026-08-15T16:16:38.163113Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-15T16:16:38.142416Z","title":"Connectionist temporal classification: La- belling unsegmented sequence data with recurrent neural net- works,","venue":null,"work_id":"42e8ce31-35be-4350-9d9a-46e126339301","year":2006},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.905525Z"},"links":{"citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:085af9554ff195da796883f41a7904bc9ff03ba0c28a94c9db67f0f482f04c14","observation_id":"0ab2d292-5149-4ec4-ba40-f5709faf45e4","resolution":{"observed_at":"2026-08-15T16:16:38.147350Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.10373","last_updated":"2025-02-14T18:51:40Z","snapshot_observed_at":"2026-08-14T19:33:12.609183Z","submitted_at":"2025-02-14T18:51:40Z","title":"OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.10373","snapshot_observed_at":"2026-08-15T16:16:37.910207Z","title":"OWLS: Scaling Laws for Multilingual Speech Recognition and Translation Models,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-15T16:16:37.910207Z"},"links":{"cited_paper":"/paper/2502.10373","citing_paper":"/paper/2509.07139"},"observation_digest":"sha256:80b7a8fcffebc055bc085806b15fd1230a9a9190d52698c31097d1afb407549f","observation_id":"d504821c-e3a8-40a7-a572-40123c91faf5","resolution":{"observed_at":"2026-08-15T16:16:37.910207Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2509.07139","last_updated":"2025-09-08T18:42:36Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-15T16:10:59.129081Z","submitted_at":"2025-09-08T18:42:36Z","title":"The ML-SUPERB 2.0 Challenge: Towards Inclusive ASR Benchmarking for All Language Varieties"},"reference_resolution":{"displayed":63,"state_counts":{"malformed_identifier":0,"metadata_mismatch":1,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":45},"total_outbound_references":63},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 17 August 2026, this Paper Citation Record lists 63 of 63 outbound references and 1 inbound Pith citation observation for arXiv:2509.07139."}