{"as_of":"2026-08-17T15:34:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:b7260de6c3f8e79f1db0a6c188e8f1384fc72698d1036e65674a3e3a9ae9e101","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T15:06:56.652596Z","state":"measured"},{"denominator":48,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":48,"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":"2026-08-06T15:33:38.783168Z","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":"2026-08-06T15:33:41.619471Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"cited_work":{"arxiv_id":"1906.11521","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.11521","snapshot_observed_at":"2026-08-06T15:33:41.619471Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","venue":"cs.CL","work_id":"f41530da-a4d2-48ca-9f9d-1e0702fbd9e8","year":2019},"citing_paper":{"arxiv_id":"2507.15523","last_updated":"2025-07-21T11:44:24Z","snapshot_observed_at":"2026-08-10T22:20:55.631197Z","submitted_at":"2025-07-21T11:44:24Z","title":"An Investigation of Test-time Adaptation for Audio Classification under Background Noise","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T15:33:38.783168Z"},"links":{"cited_paper":"/paper/1906.11521","citing_paper":"/paper/2507.15523"},"observation_digest":"sha256:4de2c266085a4b69ba2bd3a8e2a2174f74c58f7e54596f627593529864fb6913","observation_id":"88971923-397f-4e26-9754-68d13f0a942b","resolution":{"observed_at":"2026-08-06T15:33:41.653723Z","resolver_source":"local_arxiv","status":"verified_exact"},"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/1906.11521/citation-record","integrity":"/paper/1906.11521/integrity","json":"/paper/1906.11521/citation-record.json","paper":"/paper/1906.11521"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"In feature-space adaptation , trans- formations of acoustic features are estimated to maximise t he log-likelihood of the adaptation data [1, 2]","venue":null,"work_id":"5a3a233e-d95a-46f3-aeb8-23d67e517795","year":null},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:70a85ee6fdc89e7c135b07d439b76f16df4d9a8acae66f7eed2167da6c6bff2d","observation_id":"91b3a1e3-5c79-4ad4-8869-f8875aba4dff","resolution":{"observed_at":"2026-05-25T15:07:02.028549Z","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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7b347b97-6742-436c-9af0-c1e415c704fa","year":null},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:32a25d2cdaf115984cf37690b8e103b515a12d1a38f7af9dd24095187859fc55","observation_id":"091637ce-db82-4261-82e2-f49032198ce0","resolution":{"observed_at":"2026-05-25T15:07:02.038981Z","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":"2026-06-05T21:23:00.469572Z","title":"surprise language","venue":null,"work_id":"ac3d03c1-c317-4647-bf5f-b73e0c7eea1e","year":2012},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:9ef39865a0e0c6440a5cd05451d548a1755cea9d1e6a4414e2d4aee729724b4d","observation_id":"1c80a604-cb8e-4f71-a48c-aacfffb1a8d9","resolution":{"observed_at":"2026-05-25T15:07:02.033234Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f05649ad-ca5f-45a2-861e-74329ba09879","year":null},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:85afe5c4403d0ddc03237ef9b001516cbf3537319a3368e714734e9a9b109c93","observation_id":"9e1b14c0-71d4-4fbf-a7fc-fd8c3e75973a","resolution":{"observed_at":"2026-05-25T15:07:02.036335Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"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":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7f45611b-5f7a-473c-9e6d-ceb18082ebdc","year":null},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:8b6fa7578feca031307318a1bf451b71c776c0885a3e294ad3677b928600e5f5","observation_id":"b1e30633-3cd4-41cf-92a8-c224882b4823","resolution":{"observed_at":"2026-05-25T15:07:02.020176Z","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":"2026-06-05T21:23:00.469572Z","title":"Maximum likelihood l in- ear regression for speaker adaptation of continuous density hidden markov models","venue":null,"work_id":"7bbb0601-1c47-43d1-bb14-bc640babf824","year":1995},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:d27fc69efce81685b82c66471d55f501ba6449f291b0068c123836e055d89831","observation_id":"735238d3-6093-443d-a33d-c68cbdf1189a","resolution":{"observed_at":"2026-05-25T15:07:02.025163Z","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":"2026-06-05T21:23:00.469572Z","title":"Maximum likelihood linear transformations f or HMM-based speech recognition","venue":null,"work_id":"f30ab081-33e0-4dbe-bbf5-0226a844ac3b","year":1998},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:455aceed01609e54827bfe4a8ae3cf779e168f1ca18abe315a0a603b2c929117","observation_id":"e9b4d8cc-80c3-4d3e-8b28-ec8688e37af3","resolution":{"observed_at":"2026-05-25T15:07:02.039971Z","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":"2026-06-05T21:23:00.469572Z","title":"Learning hidden u nit contri- butions for unsupervised acoustic model adaptation","venue":null,"work_id":"6d6161ab-12b9-4d7f-a3c5-aff7a61600c4","year":2016},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:5c572e6e90cfa29a9d7fb20345e67036ca11194d2ea6bb306406cee789e74bd2","observation_id":"131a1e17-37e0-4ea5-8bed-2845351365b1","resolution":{"observed_at":"2026-05-25T15:07:02.044961Z","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":"2026-06-05T21:23:00.469572Z","title":"Singular val ue de- composition based low-footprint speaker adaptation and pe rson- alization for deep neural network","venue":null,"work_id":"25194bb9-65e1-49f1-8b21-867d4e035f2d","year":2014},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:f96221552083e915bc59f5a4bd0190d3f178b2a582768c36d68167aa03bcffba","observation_id":"4f8a960c-e3f8-4863-a051-088116f72d2e","resolution":{"observed_at":"2026-05-25T15:07:02.032392Z","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":"2026-06-05T21:23:00.469572Z","title":"Speaker adaptation of context dependent deep n eural networks","venue":null,"work_id":"c29ff0be-e247-472d-973c-7f01d012da9d","year":2013},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:8a2101ce48b02b59e5cd5a939ef174fbc585d1dd5e69f7c7048a46de889d4f4c","observation_id":"4dcd03e6-a29d-4ce3-b25b-0642510ee9fb","resolution":{"observed_at":"2026-05-25T15:07:02.015569Z","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":"2026-06-05T21:23:00.469572Z","title":"KL-divergence re gu- larized deep neural network adaptation for improved large v ocab- ulary speech recognition","venue":null,"work_id":"95f411bd-5d4f-4f15-8edb-bb35bc657db5","year":2013},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:c5020a494b181e3c69c1a4a3aba11571aaf5e5a0ed17125183223730b37e60e6","observation_id":"243919f0-4b97-4a73-b561-25fce5595714","resolution":{"observed_at":"2026-05-25T15:07:02.003546Z","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":"2026-06-05T21:23:00.469572Z","title":"Front-end factor analysis for speaker veriﬁcation","venue":null,"work_id":"723e7db4-e833-45cc-979c-ee291fcb5144","year":2011},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:8210a3bdfebc0452422456911e599305d0716910126f2f57a383b2f13e9823f4","observation_id":"5d53cc5b-72f1-4e1f-bea8-663fdf8d95f4","resolution":{"observed_at":"2026-05-25T15:07:02.016512Z","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":"2026-06-05T21:23:00.469572Z","title":"Speaker adaptation of neural network acoustic models using i-vecto rs","venue":null,"work_id":"da08cb0f-2cbf-4766-a334-cdaa785e95dc","year":2013},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:608d5c3928eb4c0a0312893ac78d8e69ca9609e4c3fe2a3758f047aa4964ae18","observation_id":"2f95e4ce-63ef-4260-b12b-9f525e7f9309","resolution":{"observed_at":"2026-05-25T15:07:01.900133Z","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":"2026-06-05T21:23:00.469572Z","title":"Fast speaker adaptation of hybrid NN/HMM model for speech recognition based on discriminativ e learning of speaker code","venue":null,"work_id":"70da1ef8-41b9-45a2-abf9-889a913ca333","year":2013},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:611234d740edf17838802e17b46429f9482abcaca6c873b8ceaabea864263fe5","observation_id":"40d2cf38-3efc-4030-a2e8-e210a5b69568","resolution":{"observed_at":"2026-05-25T15:07:01.862042Z","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":"2026-06-05T21:23:00.469572Z","title":"On combining i-vectors and dis- criminative adaptation methods for unsupervised speaker n ormal- ization in DNN acoustic models","venue":null,"work_id":"ff1916a0-512d-4938-a7df-62980a959d33","year":2016},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:c2de710da1a29faf4a3f8372e55474d3cd77dae97df25685dca35a60f281099e","observation_id":"e1f62b59-d787-4cda-a6b6-1498175fb20e","resolution":{"observed_at":"2026-05-25T15:07:01.930677Z","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":"2026-06-05T21:23:00.469572Z","title":"Speaker adaptation for continuous den sity HMMs: A review","venue":null,"work_id":"592d67ca-fd9d-4232-8bf5-3743a44eaf64","year":2001},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:0c5139e6e2295a353e72385a238ad5f9ba54bd46903cca36d14eca8e4cd8a978","observation_id":"1332c976-4c31-4747-a56a-16660c2b7a8c","resolution":{"observed_at":"2026-05-25T15:07:01.972984Z","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":"2026-06-05T21:23:00.469572Z","title":"Discri minative training of acoustic models applied to domains with unrelia ble transcripts [speech recognition applications]","venue":null,"work_id":"c6cd166b-bed0-4423-a4bc-d81f69ef61e6","year":2005},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:4cf82ad97e5325a8055d9a7106d951430e91924441f0c68490bdd40a2c29a44c","observation_id":"9e3c8665-13a6-4a0a-a35a-924642d7b782","resolution":{"observed_at":"2026-05-25T15:07:01.986617Z","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":"2026-06-05T21:23:00.469572Z","title":"Investiga ting data selection for minimum phone error training of acoustic mode ls","venue":null,"work_id":"25c18f63-1390-4ab3-93b6-d9d49b0b72c1","year":2007},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:ded33d92b69c683e5bba94cdec560292e664726d078aadd570d161cd5dedaf13","observation_id":"0af4840d-8731-409f-a2a0-061517dc89c0","resolution":{"observed_at":"2026-05-25T15:07:01.977847Z","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":"1705.09724","last_updated":"2017-05-26T21:10:15Z","snapshot_observed_at":"2026-08-14T20:57:53.240961Z","submitted_at":"2017-05-26T21:10:15Z","title":"Semi-Supervised Model Training for Unbounded Conversational Speech Recognition","version":1},"cited_work":{"arxiv_id":"1705.09724","doi":null,"metadata_source":"pith","pith_arxiv_id":"1705.09724","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Semi-Supervised Model Training for Unbounded Conversational Speech Recognition","venue":"cs.CL","work_id":"7bae9a0e-8913-421d-85c3-ccb64c1726a7","year":2017},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"cited_paper":"/paper/1705.09724","citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:d1f4d9557ed5f75ff37d5a8bb2947298ce2b6af7f577485a86b1b2ccb48017bd","observation_id":"e9b6ee00-d91b-4f91-8e39-cefee050bce3","resolution":{"observed_at":"2026-05-25T15:07:01.771379Z","resolver_source":"local_arxiv","status":"verified_exact"},"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":"2026-06-05T21:23:00.469572Z","title":"Semi-supervised DNN training with word selection for ASR","venue":null,"work_id":"9287d4df-ada5-4309-9f09-5ac2b1d26bb7","year":2017},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:253618a9e0a16ec4dc341475e254dfdb966c8534a1259c9cdf7578c08de14599","observation_id":"928344f0-7230-44e1-96d4-6aef5fc68031","resolution":{"observed_at":"2026-05-25T15:07:01.866000Z","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":"2026-06-05T21:23:00.469572Z","title":"Dnn adaptation by automatic quality estimation of asr hypotheses","venue":null,"work_id":"6a54b437-363c-4cae-9453-3d3fe29eb946","year":2017},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:af1849f2c5f58133ff0fc317e3ea18316c6def4088ff06d744cb63cd81265b49","observation_id":"04a7ca47-a713-4ec4-9a3c-b2acfbf23f3a","resolution":{"observed_at":"2026-05-25T15:07:01.934890Z","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":"2026-06-05T21:23:00.469572Z","title":"Learning hidden unit co ntri- butions for unsupervised speaker adaptation of neural netw ork acoustic models","venue":null,"work_id":"93071c39-7da7-4021-9b03-ba9fdc6ff15b","year":2014},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:7f3cc7ff90d51057ad9fa871023b3cb75c34888122fb9f5996c14a7f07bbe8e2","observation_id":"aa15a6a4-5935-4190-98d2-cff2c227548e","resolution":{"observed_at":"2026-05-25T15:07:01.900945Z","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":"2026-06-05T21:23:00.469572Z","title":"Subspace lhuc for fast adap ta- tion of deep neural network acoustic models","venue":null,"work_id":"e74b548e-da6e-45ba-b38f-6db484459c9e","year":2016},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:8a558999183daba8341c774ad750f465c6f7e7f8e88a79d63524993716e84190","observation_id":"ad8695fd-1cdb-4c21-bd63-3378f4a3ab5b","resolution":{"observed_at":"2026-05-25T15:07:01.986943Z","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":"2026-06-05T21:23:00.469572Z","title":"Extended low-rank plus diagonal adaptation for deep and recurrent neural networks","venue":null,"work_id":"fb2fa879-9df0-4eca-a2f8-debad4fe7222","year":2017},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:1532d8a1f571697e706b43fefb0bf0d7db188863c53c313272529a4f1be222ae","observation_id":"e4a770bb-3c26-4f6f-a5e5-711da8b7e72f","resolution":{"observed_at":"2026-05-25T15:07:01.990299Z","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":"2026-06-05T21:23:00.469572Z","title":"Regularized adaptation of discrim inative classiﬁers","venue":null,"work_id":"30fe867c-bd76-4d94-8b98-f0a58ccbe802","year":2006},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:5caddc39a4778e8c95cf2c0e3d1a848de218ddb2788738bafd5bf05c42cdf93d","observation_id":"48bcb12a-f9ca-4b48-9049-acec73fb68c7","resolution":{"observed_at":"2026-05-25T15:07:01.964700Z","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":"2026-06-05T21:23:00.469572Z","title":"Lattice- based unsu- pervised acoustic model training","venue":null,"work_id":"fd5f115f-47e1-4937-bea9-9944bd41a167","year":2011},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:494a99014a50b19c24322890ddca2012521a40a9af99ea08e70cc1f92114c1d9","observation_id":"f0840aee-46b0-48c7-b0b4-6daa046f4286","resolution":{"observed_at":"2026-05-25T15:07:01.944258Z","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":"2026-06-05T21:23:00.469572Z","title":"Semi - supervised training of acoustic models using lattice-free MMI","venue":null,"work_id":"86c6c3f5-ef35-4819-982e-0a9b12691099","year":2018},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:7d4980e19b589962f7147efae413d5f791c27b8fcb650f14674277bda88879b8","observation_id":"3ad96e1a-f295-4614-bdc4-3bcecf54fcf5","resolution":{"observed_at":"2026-05-25T15:07:01.926645Z","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":"2026-06-05T21:23:00.469572Z","title":"TED-LIUM:an auto- matic speech recognition dedicated corpus","venue":null,"work_id":"0eef3933-9b76-4c1d-9d3c-b710a327063f","year":2012},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:4a069dda994c8b8af75e104938e35b1efca30a9814822cea75bf32cccf5df4d0","observation_id":"c7bbb7cb-d828-4c70-be1b-675cc41bcdfc","resolution":{"observed_at":"2026-05-25T15:07:01.968160Z","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":"2026-06-05T21:23:00.469572Z","title":"Enhancing the TED- LIUM corpus with selected data for language modeling and mor e TED talks","venue":null,"work_id":"d5f83093-c7bc-41c9-8634-016e734bbf14","year":2014},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:5680514ed769fc970eb1a25145436eca75bbe1438a100dd20b2dd6735c1faf68","observation_id":"0f94aa79-e3f6-49f7-aeea-ca544ff35811","resolution":{"observed_at":"2026-05-25T15:07:01.922653Z","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":"2026-06-05T21:23:00.469572Z","title":"The MGB challenge: Evaluating multi-genre broadcast medi a recognition","venue":null,"work_id":"ad6d2511-3427-46c0-afd4-f3c8894c905f","year":2015},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:f23e2792f6d2a8e823c261a9253ef453bc24b541ed61b2f8a24c89e0a2335f11","observation_id":"f39c2557-0bb8-4ab4-8f32-c9b71e2be685","resolution":{"observed_at":"2026-05-25T15:07:01.882900Z","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":"2026-06-05T21:23:00.469572Z","title":"Maximum mu - tual information estimation of hidden markov model paramet ers for speech recognition","venue":null,"work_id":"0b6555ad-2d55-4aeb-82e6-2a89cee0216e","year":1986},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:b9f9be51a6a441b3cce0eec41e3670fa3c7ba93a134924ef261f4cb1da444bb9","observation_id":"7e01710c-13c6-49af-b1f8-4446119d96d1","resolution":{"observed_at":"2026-05-25T15:07:01.982450Z","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":"2026-06-05T21:23:00.469572Z","title":"Unsupervis ed training and directed manual transcription for L VCSR","venue":null,"work_id":"659bc6f1-8738-4d0a-8736-a532c4f516fd","year":2010},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:ff29c3a95d5a93f210721f73f9a7bc4d67d55516a2fcee2b03e54d98b2fa7a16","observation_id":"69e6513c-41b5-418f-833e-64eb34367848","resolution":{"observed_at":"2026-05-25T15:07:01.905183Z","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":"2026-06-05T21:23:00.469572Z","title":"Lattice-based u n- supervised mllr for speaker adaptation","venue":null,"work_id":"f38d7c6f-4523-4c93-bbed-343cab0fcbdb","year":2000},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:427c540429fc9bc109e2b58282d50caa2999a923a7881639e819ea59c2ebb971","observation_id":"ff1ec517-c427-4bb5-8735-8c8b1641dc53","resolution":{"observed_at":"2026-05-25T15:07:01.904956Z","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":"2026-06-05T21:23:00.469572Z","title":"Discriminative training for large vocabula ry speech recognition","venue":null,"work_id":"5be7954e-cf4d-42a6-8280-c9d30093bf1f","year":2005},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:0ffbaffe19fa9895206dd6217ca167bdd622cdc90c810a8ed9b5e1f500208d6b","observation_id":"146cb024-de27-4994-9da2-241afd0370d7","resolution":{"observed_at":"2026-05-25T15:07:01.960684Z","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":"2026-06-05T21:23:00.469572Z","title":"Purely sequence-trained neu- ral networks for ASR based on lattice-free MMI","venue":null,"work_id":"e887ff9a-1096-4d61-832d-526312e73224","year":2016},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:407b12734c8c0923cffe96e6e6601cce57443e0c6eda6007fb48ad16258b2ea1","observation_id":"b8c54355-2b4a-49e6-84e9-798c1105abcc","resolution":{"observed_at":"2026-05-25T15:07:01.972680Z","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":"2026-06-05T21:23:00.469572Z","title":"Sequen ce- discriminative training of deep neural networks","venue":null,"work_id":"2af0e01c-9104-4af8-9d91-cdfe586c0afd","year":2013},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:0eff0d1d225f8931c9d76f9f7604f4cf8c05837b913566124671975c7728f249","observation_id":"e8558ed8-cc85-4a2a-ae8a-7330236a5b15","resolution":{"observed_at":"2026-05-25T15:07:01.953332Z","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":"2026-06-05T21:23:00.469572Z","title":"A novel loss functio n for the overall risk criterion based discriminative training of HM M mod- els","venue":null,"work_id":"90c41104-8cab-43f9-a389-f4b039485124","year":2000},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:5d7bccfb57bdcaa0d1436e1ec1e7d48836f7853c702ef82e7954c7f8d8541444","observation_id":"a78b4ca4-9b1e-47f2-9624-ff509301d419","resolution":{"observed_at":"2026-05-25T15:07:01.968632Z","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":"2026-06-05T21:23:00.469572Z","title":"End-t o-end speech recognition using lattice-free MMI","venue":null,"work_id":"aed8609b-1c21-40a8-afd5-f63457b37efc","year":2018},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:25b16fdad79f5e9e463f7334bf17b820594ff6ca2f177a56a4054ff5aafb2e4e","observation_id":"7e95c0c6-a343-49e5-bc5f-717fd7914fc9","resolution":{"observed_at":"2026-05-25T15:07:01.978280Z","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":"2026-06-05T21:23:00.469572Z","title":"A compact model for speaker-adaptive training","venue":null,"work_id":"af077e13-29e9-4c06-a797-ad274b2d3b18","year":1996},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:f0d18dd0c92393278b06a2eeb4ee3d6e07ddfdf82a2e52c111929037bca387b7","observation_id":"b38af7bc-e48f-4d42-820a-b7bcea15403d","resolution":{"observed_at":"2026-05-25T15:07:01.960273Z","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":"2026-06-05T21:23:00.469572Z","title":"SA T-LHUC: Speaker adap tive training for learning hidden unit contributions","venue":null,"work_id":"e84cb55e-0250-43df-9797-eeffbc3f89cc","year":2016},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:ac613b6b99f558772a9a2b3d2705d8db846cd27ee0dfea3b5c5ecab6ce288ac9","observation_id":"9e51478d-32ea-4ac0-9f65-3c6cd46c6fcd","resolution":{"observed_at":"2026-05-25T15:07:01.952897Z","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":"2026-06-05T21:23:00.469572Z","title":"The Kaldi speech recog- nition toolkit","venue":null,"work_id":"9c331998-3607-4992-833e-8c39d6deeeda","year":2011},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:a06afb57b31b51f7ac1b120dfc680504a8c84da3d46064e6b4d02c6a1a4c3e45","observation_id":"a9d3d1b4-1708-4731-a024-4366706edc6a","resolution":{"observed_at":"2026-05-25T15:07:01.891670Z","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":"2026-06-05T21:23:00.469572Z","title":"A time delay ne ural network architecture for efﬁcient modeling of long tempora l con- texts","venue":null,"work_id":"69cd98a4-c231-48a2-804a-c5450a88621b","year":2015},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:f6bb40ff00bc4dae5a35a6a450fbbcf6c0d59c92aeaf8a9ea40dde2a077ff06b","observation_id":"643f629e-87fd-4510-85ea-09b0cb3d7d4f","resolution":{"observed_at":"2026-05-25T15:07:01.982748Z","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":"2026-06-05T21:23:00.469572Z","title":"Overview of the IWSLT 2012 evaluation campaign","venue":null,"work_id":"01967a5d-1566-4249-9288-325bd16682a5","year":2012},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:b45a03ca99ee1b06e2986b3a156ff364cd4d624ce717e74fc50e09d550b45f46","observation_id":"63f70346-cb8d-4c41-991a-8d951508b450","resolution":{"observed_at":"2026-05-25T15:07:01.914392Z","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":"2026-06-05T21:23:00.469572Z","title":"Semi-orthogonal low-rank matrix factor iza- tion for deep neural networks","venue":null,"work_id":"5bb506a0-ac53-414a-a6fb-ff7c57193e8a","year":2018},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:e377d377804ab5c4bb347ba49f24caf0d800a12b1ea3e7cce6b2a8d800476d01","observation_id":"eabaa3d7-94ba-44cf-8c1e-e184c63fb940","resolution":{"observed_at":"2026-05-25T15:07:01.940183Z","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":"2026-06-05T21:23:00.469572Z","title":"A pitch extraction algorithm tuned for au to- matic speech recognition","venue":null,"work_id":"ea9783d5-629e-43e6-a64e-5a0717d80ecd","year":2014},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:d9d77cd2acf33c9449c639f283f93094f6d0bc5083c7144877f520bd66798bc3","observation_id":"8639ea4b-d097-4efa-bb3c-9758ed4f6162","resolution":{"observed_at":"2026-05-25T15:07:01.964223Z","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":"2026-06-05T21:23:00.469572Z","title":"Multilingual representations for low resource speech recognition and ke yword search","venue":null,"work_id":"584fe0c1-e693-4d99-9fab-ff58b573d595","year":2016},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:94c8595ead23002d79beed1427afa55c9a27e32b9efa975a5a11fd8d7aa5aff3","observation_id":"969892ab-a441-4864-9a8e-e4515e0be6bb","resolution":{"observed_at":"2026-05-25T15:07:01.990593Z","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":"2026-06-05T21:23:00.469572Z","title":"Low-resource s peech recognition and keyword-spotting","venue":null,"work_id":"5dd8eef1-848f-4eed-80e2-6a0d559d0bc8","year":2017},"citing_paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-05-25T15:06:56.652596Z"},"links":{"citing_paper":"/paper/1906.11521"},"observation_digest":"sha256:44ac465a629fac65cece65423ee8cc7d4a4557da0b012493ccd74175914cd171","observation_id":"2c520375-6334-41a1-93b2-5d8b5fc9ac9c","resolution":{"observed_at":"2026-05-25T15:07:01.943896Z","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"}}],"paper":{"arxiv_id":"1906.11521","last_updated":"2019-06-27T09:47:47Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T22:21:58.015166Z","submitted_at":"2019-06-27T09:47:47Z","title":"Lattice-Based Unsupervised Test-Time Adaptation of Neural Network Acoustic Models"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":2,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":2,"verified_exact":1,"verified_fuzzy":42},"total_outbound_references":47},"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 47 of 47 outbound references and 1 inbound Pith citation observation for arXiv:1906.11521."}