{"as_of":"2026-08-09T23:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c9cf869c65ff135e5430e89767142d754a8273ee57369d5fbf2d73bd26a78f8b","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-03T12:01:01.414371Z","state":"measured"},{"denominator":40,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":40,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-03T12:00:57.108795Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.19919","snapshot_observed_at":"2026-08-03T12:00:57.108795Z","title":"However, KD methods that heavily emphasize model compression fail to ensure gen- eralization capacity","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.108795Z"},"links":{"cited_paper":"/paper/2601.19919","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:52fc6374273b366ed787e10d9013ecc850db41dfca65335db628dbaaa46c8a58","observation_id":"06bf35fe-10ce-492b-8187-cccd90efe84b","resolution":{"observed_at":"2026-08-03T12:00:57.108795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2601.19919/citation-record","integrity":"/paper/2601.19919/integrity","json":"/paper/2601.19919/citation-record.json","paper":"/paper/2601.19919"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:56.954340Z","title":"In addition, with the rapid advancement of hardware, deep learning (DL)-based ASR is gaining more attention [3]","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:56.954340Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:dc60b7f546b924160be04f75b737d5c4c2da51bb596beeab493a4a5e86e55796","observation_id":"49609f18-4257-42b3-b0aa-914a023085e7","resolution":{"observed_at":"2026-08-03T12:00:56.954340Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.19919","snapshot_observed_at":"2026-08-03T12:00:57.108795Z","title":"However, KD methods that heavily emphasize model compression fail to ensure gen- eralization capacity","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.108795Z"},"links":{"cited_paper":"/paper/2601.19919","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:52fc6374273b366ed787e10d9013ecc850db41dfca65335db628dbaaa46c8a58","observation_id":"06bf35fe-10ce-492b-8187-cccd90efe84b","resolution":{"observed_at":"2026-08-03T12:00:57.108795Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.410466Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.410466Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:2cb0fb1b2ad00f2ec69964cc052f0a5ccd7fcf7317e86ca05a9ac6c67199d958","observation_id":"4053b3c0-093a-419b-8889-4b7bb6abfe36","resolution":{"observed_at":"2026-08-03T12:00:57.410466Z","resolver_source":null,"status":"malformed_identifier"},"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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.292347Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.292347Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:0b2fabef687e3f6051bf2126a36a177ef08bd1bb1339902e846373b97b5690cf","observation_id":"2e9ff464-e32d-40c1-abca-52cac495f2b8","resolution":{"observed_at":"2026-08-03T12:00:57.292347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.10504","last_updated":"2022-07-20T22:31:00Z","snapshot_observed_at":"2026-07-06T10:06:22.268771Z","submitted_at":"2020-10-20T17:58:13Z","title":"Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.10504","snapshot_observed_at":"2026-08-03T12:00:58.068320Z","title":"Pushing the limits of semi-supervised learning for automatic speech recognitio n,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.068320Z"},"links":{"cited_paper":"/paper/2010.10504","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:fcd3cb61ba62eecfbbf8a2f370d0201544c629ecfaad86bba43c5f5c00d9245f","observation_id":"3b1ee6d2-3d86-4470-8032-10b3e767f42c","resolution":{"observed_at":"2026-08-03T12:00:58.068320Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.214287Z","title":"• In our experiments, FastWhisper achieved lower infer- ence latency than the original Whisper model while ensuring robust ASR performance across evaluation datasets","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.214287Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:d05d0160451baec501d94dc1b5b7f96bbf2d8036003ad5f86dccf9a1116e3be6","observation_id":"af1d5073-10d3-43ff-aa6d-8d1ccf796b6d","resolution":{"observed_at":"2026-08-03T12:00:57.214287Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.478674Z","title":"The use of ASKD and the Whisper encoder for training resulted in a 0.97% lower WER score compared to the teacher model, Whisper, despite the smaller dataset and fewer parameters","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.478674Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:3d4040514b7ba921d3f0f3ba9165131c8ca9c7b074130797753b56b89ed8619b","observation_id":"871f0c08-5ac4-4a6d-8f7c-b2c7b3e0e2b0","resolution":{"observed_at":"2026-08-03T12:00:57.478674Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.15122","last_updated":"2021-04-01T09:12:22Z","snapshot_observed_at":"2026-07-06T10:54:07.917175Z","submitted_at":"2021-03-28T12:52:03Z","title":"Quantifying Bias in Automatic Speech Recognition","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.15122","snapshot_observed_at":"2026-08-03T12:00:57.572649Z","title":"Quantifying bias in automatic speech recogn ition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.572649Z"},"links":{"cited_paper":"/paper/2103.15122","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:b98634da7293194647652ac6dd13cd59e29120052a654498be669ec9c4a01f3e","observation_id":"51a66e0e-b52b-4a4d-b46a-e874da00ab54","resolution":{"observed_at":"2026-08-03T12:00:57.572649Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.595304Z","title":"Far-field au- tomatic speech recognition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.595304Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:93c276d5f5e7e90085bf3def35abb4dbb04fb30f1fc7fc433f7b09defeb0ed1a","observation_id":"a2cf057d-2b0e-46d7-9970-beb852ed85e6","resolution":{"observed_at":"2026-08-03T12:00:57.595304Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.698813Z","title":"Almost unsupervised text to speech and automatic spee ch recognition,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.698813Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:d1955a78293aa7ca045f53037807120cb9557fc1bcb5cdf7828b15a3e47f9cf5","observation_id":"950376e9-ed2e-4d29-aa43-94983fb58b53","resolution":{"observed_at":"2026-08-03T12:00:57.698813Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:57.896775Z","title":"wav2vec 2.0: A framework for self-supervised learni ng of speech representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:57.896775Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:3b7e1228e3e7e93ad8ea307807311a7a093ee52e552b67a714b2ac146a13da96","observation_id":"bc977ebe-56bd-4240-a86f-64c23cae287b","resolution":{"observed_at":"2026-08-03T12:00:57.896775Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:58.235876Z","title":"Robust speech recognit ion via large-scale weak supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.235876Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:054a369e5d4d876917f5d93386bfc1552094e24cea38b8fbbe5d9eeb765d060d","observation_id":"10fea597-1014-44f4-b2f1-3c105dac4fb0","resolution":{"observed_at":"2026-08-03T12:00:58.235876Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:58.366810Z","title":"Beyond neural scaling laws: beating power l aw scaling via data pruning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.366810Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:e9ce6d8e149b49e994346b5adc2a1b3d2cd8b428240f58c31662aed8ba9e47a4","observation_id":"4f8b9de0-01d2-4dca-ad2f-40700603175a","resolution":{"observed_at":"2026-08-03T12:00:58.366810Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:58.478492Z","title":"A better and faster end-to-end mo del for streaming asr,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.478492Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:9bf4d086f4ddc41e33b256f844b850329e0ef593eda755b06f00a6906703b077","observation_id":"7ea56402-9a5a-4bc9-afed-4fad49c86fc8","resolution":{"observed_at":"2026-08-03T12:00:58.478492Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.03540","last_updated":"2019-09-24T10:06:29Z","snapshot_observed_at":"2026-07-06T08:05:48.377851Z","submitted_at":"2019-07-08T12:10:18Z","title":"ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.03540","snapshot_observed_at":"2026-08-03T12:00:58.644990Z","title":"Shrinkml: End-t o- end asr model compression using reinforcement learning,","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.644990Z"},"links":{"cited_paper":"/paper/1907.03540","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:f3979d0551feaeb92973aeace8c3f1571a9bb678732848cfc105668ee4c5acbf","observation_id":"51794e45-7b4c-4bcc-b1e7-5d53f102cbec","resolution":{"observed_at":"2026-08-03T12:00:58.644990Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2008.02897","last_updated":"2020-08-06T22:33:10Z","snapshot_observed_at":"2026-08-04T11:12:23.712734Z","submitted_at":"2020-08-06T22:33:10Z","title":"Iterative Compression of End-to-End ASR Model using AutoML","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2008.02897","snapshot_observed_at":"2026-08-03T12:00:58.773028Z","title":"Iterative compression of end-to-end asr model usin g au- toml,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.773028Z"},"links":{"cited_paper":"/paper/2008.02897","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:7470884fb722524a22857c090e807627cb5040602dba011573023a76f7010ea6","observation_id":"966d4251-6042-48b4-8ff5-bb0441201026","resolution":{"observed_at":"2026-08-03T12:00:58.773028Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:58.870450Z","title":"Multi-stage progressive compression o f con- former transducer for on-device speech recognition.,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.870450Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:94594b83ccc17af9b0c1bfa37866f744d08901cbd41cf80d1ac11eb9b8326044","observation_id":"a6e9eed2-4164-4688-94cc-b0d95806c02c","resolution":{"observed_at":"2026-08-03T12:00:58.870450Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1503.02531","last_updated":"2015-03-09T15:44:49Z","snapshot_observed_at":"2026-07-06T04:11:24.157003Z","submitted_at":"2015-03-09T15:44:49Z","title":"Distilling the Knowledge in a Neural Network","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1503.02531","snapshot_observed_at":"2026-08-03T12:00:58.991437Z","title":"Distilling the knowledge in a neural network,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:58.991437Z"},"links":{"cited_paper":"/paper/1503.02531","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:fa142bb3d7ec4543bdba530d5917609eef533ff41456f57368888d41eebe642a","observation_id":"0ffd4e67-5689-4d37-ac33-149271757589","resolution":{"observed_at":"2026-08-03T12:00:58.991437Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:59.111579Z","title":"Knowledge distillation: A survey,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.111579Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:c5e0236fb3aacefed9015374f876fb71edbe27f73ca29b858c78ca702ca35b56","observation_id":"8d24a199-c8fb-423f-bd53-4e2cf8d05b4a","resolution":{"observed_at":"2026-08-03T12:00:59.111579Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:59.255489Z","title":"Distil hubert: Speech representation learning by layer-wise distillatio n of hidden- unit bert,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.255489Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:df90eaf31b628bccb99f91f33121ca88401a3ea2da476a57af9ae8813de08c6b","observation_id":"a36d44dc-3fa4-4602-80cf-b50ac136407c","resolution":{"observed_at":"2026-08-03T12:00:59.255489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.00430","last_updated":"2023-11-01T10:45:07Z","snapshot_observed_at":"2026-08-08T04:51:03.844048Z","submitted_at":"2023-11-01T10:45:07Z","title":"Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.00430","snapshot_observed_at":"2026-08-03T12:00:59.413512Z","title":"Distil- whisper: Robust knowledge distillation via large-scale ps eudo la- belling,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.413512Z"},"links":{"cited_paper":"/paper/2311.00430","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:d7d6ced71b0c8b73a3cbab576e21ac0632844c4972f18dac516bde1a5f89f610","observation_id":"f5b8dcf7-51c6-401a-af52-af0466e4b2be","resolution":{"observed_at":"2026-08-03T12:00:59.413512Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:59.550083Z","title":"On informatio n and sufficiency,","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.550083Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:dc0613f105eb7a449b99d4dac534747031f7502a55798b7e35918d88e6c24d77","observation_id":"d229d629-353b-42a9-9534-3b6858c8877e","resolution":{"observed_at":"2026-08-03T12:00:59.550083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2207.00555","last_updated":"2022-07-01T17:11:23Z","snapshot_observed_at":"2026-08-08T16:31:25.250330Z","submitted_at":"2022-07-01T17:11:23Z","title":"FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2207.00555","snapshot_observed_at":"2026-08-03T12:00:59.685825Z","title":"Fithubert: Going thinner and deeper for kno wl- edge distillation of speech self-supervised learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.685825Z"},"links":{"cited_paper":"/paper/2207.00555","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:0905f19489b449f672abad5746eb479f8dfb6b58c3359e22ceff2b3344a68a65","observation_id":"3bb91cdc-7a55-46ae-b9da-29129d4833e3","resolution":{"observed_at":"2026-08-03T12:00:59.685825Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:59.851187Z","title":"Masked token similarity transfer for compressing transfo rmer- based asr models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.851187Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:0d5aaa8416f20ab520e172c01323c57cc37bea57569f0f0e34ba15d013aea3dd","observation_id":"cf9bc363-4f75-4f92-a9b2-9afd2b808b53","resolution":{"observed_at":"2026-08-03T12:00:59.851187Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:00:59.963646Z","title":"Does knowledge distillation re ally work?,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-03T12:00:59.963646Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:fe1bcbcdadffab0938428e656fbe76babafb8660328d5c476b23137e33af95c2","observation_id":"708bac43-09ce-4dd7-ba98-6f8c88682d66","resolution":{"observed_at":"2026-08-03T12:00:59.963646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.13621","last_updated":"2025-08-10T10:58:20Z","snapshot_observed_at":"2026-08-08T07:10:37.803289Z","submitted_at":"2023-11-22T08:34:33Z","title":"EA-KD: Entropy-based Adaptive Knowledge Distillation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.13621","snapshot_observed_at":"2026-08-03T12:01:00.084468Z","title":"Know ledge from the dark side: Entropy-reweighted knowledge distilla tion for balanced knowledge transfer,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.084468Z"},"links":{"cited_paper":"/paper/2311.13621","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:88ed2467531ad659ae74ea80e5c11da98b735e8553b87dabaeba7b5c19a09434","observation_id":"147ef1ea-f90e-4e0b-9c7b-d4f5c829d982","resolution":{"observed_at":"2026-08-03T12:01:00.084468Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.08019","last_updated":"2024-05-11T15:06:24Z","snapshot_observed_at":"2026-08-05T13:54:21.688310Z","submitted_at":"2024-05-11T15:06:24Z","title":"AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.08019","snapshot_observed_at":"2026-08-03T12:01:00.179828Z","title":"Adakd: Dynamic knowledge distillation of asr models using adaptive loss weighting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.179828Z"},"links":{"cited_paper":"/paper/2405.08019","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:5b75b0f19eac9a967283dac598bd35fc18fa02793d2a3e6907578ef30e9bb249","observation_id":"808a03a4-c75a-459a-a38c-b98a2aed7b5a","resolution":{"observed_at":"2026-08-03T12:01:00.179828Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:00.256764Z","title":"Self-knowledge distillation with progressive refi nement of targets,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.256764Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:8fabe38d561dbf2e68f96373e1bb6877e14dcfe73a926625ed4b4b6c57f37b26","observation_id":"2fea69e3-ff2c-48d7-8f5a-7cc83ee5a6c7","resolution":{"observed_at":"2026-08-03T12:01:00.256764Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.09579","last_updated":"2024-12-12T18:54:07Z","snapshot_observed_at":"2026-07-06T20:06:09.333397Z","submitted_at":"2024-12-12T18:54:07Z","title":"A Theoretical Analysis of Soft-Label vs Hard-Label Training in Neural Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.09579","snapshot_observed_at":"2026-08-03T12:01:00.357052Z","title":"A theore tical anal- ysis of soft-label vs hard-label training in neural network s,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.357052Z"},"links":{"cited_paper":"/paper/2412.09579","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:9c6d96c0271e00a44f4116fb6481bc20975531e45c42ce7c633907e7eb757f4c","observation_id":"b39c9108-1cc3-4e77-a9fb-df2a3a8e4c83","resolution":{"observed_at":"2026-08-03T12:01:00.357052Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:00.451037Z","title":"Librispeech: an asr corpus based on public domain a udio books,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.451037Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:7beb4277616911999c5b552233a6ff2decd9ed75ee044287a99e3b213196f32e","observation_id":"b6a9947c-2c07-4214-931a-7c67c0d4b0f3","resolution":{"observed_at":"2026-08-03T12:01:00.451037Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:00.519384Z","title":"Ted-lium 3: Twice as much data and corpus repartition for experiments on speaker adap tation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.519384Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:0733a4395ed095a7923ae8caf8cf9be60916470a7517bdc3b20b41e242803caa","observation_id":"71ccd06c-13aa-493a-9deb-048099fd620b","resolution":{"observed_at":"2026-08-03T12:01:00.519384Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:00.617701Z","title":"The lj speech dataset,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.617701Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:9e75582747448c44388b6d1da7b4981097718cc97f77d00e1ba2ec9a05a07c1c","observation_id":"b377352d-ddd0-45ee-970a-ace2feecae05","resolution":{"observed_at":"2026-08-03T12:01:00.617701Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2203.15591","last_updated":"2022-03-29T14:02:57Z","snapshot_observed_at":"2026-07-06T12:54:11.616335Z","submitted_at":"2022-03-29T14:02:57Z","title":"Earnings-22: A Practical Benchmark for Accents in the Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15591","snapshot_observed_at":"2026-08-03T12:01:00.692815Z","title":"Earnings-22: A practical benchmark for ac cents in the wild,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.692815Z"},"links":{"cited_paper":"/paper/2203.15591","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:f24e62b4b841adb953ee97936f9bd3285d4f0e798d26365c85edb07290524959","observation_id":"b2ab651f-c346-4439-a1a5-af915a21c347","resolution":{"observed_at":"2026-08-03T12:01:00.692815Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:00.865611Z","title":"Unleashing the killer corpus: experie nces in creating the multi-everything ami meeting corpus,","venue":null,"work_id":null,"year":2007},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:00.865611Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:f725fc066f0e964edc2063337e1ece4c0806feeddbe9a1fc86dd9854405b3065","observation_id":"c07adfc1-48c4-4fc8-b406-c386f1f1b3e6","resolution":{"observed_at":"2026-08-03T12:01:00.865611Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.06909","last_updated":"2021-06-13T04:09:16Z","snapshot_observed_at":"2026-08-09T08:32:43.969496Z","submitted_at":"2021-06-13T04:09:16Z","title":"GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.06909","snapshot_observed_at":"2026-08-03T12:01:01.032776Z","title":"Gigaspeech: An evolving, multi-domai n asr corpus with 10,000 hours of transcribed audio,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:01.032776Z"},"links":{"cited_paper":"/paper/2106.06909","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:899989930047b660d89052d5c24602d7ef83f3b1a75c20112ecbfc9c7177f0f8","observation_id":"67ef9509-bed9-4fba-a2c6-3335ddaeeb53","resolution":{"observed_at":"2026-08-03T12:01:01.032776Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:01.148341Z","title":"V oxpopuli: A large-scale multilingual spe ech corpus for representation learning, semi-supervised learning and in- terpretation,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:01.148341Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:4bc564550642b16db8db518efc8b27accd6dc2f4b16d5ed5eef4bef1dfb4afdb","observation_id":"65182b4b-4978-45c5-8537-f720ff8a725c","resolution":{"observed_at":"2026-08-03T12:01:01.148341Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2002.05202","last_updated":"2020-02-12T19:57:13Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-02-12T19:57:13Z","title":"GLU Variants Improve Transformer","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2002.05202","snapshot_observed_at":"2026-08-03T12:01:01.242044Z","title":"Glu variants improve transformer,","venue":null,"work_id":null,"year":2002},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:01.242044Z"},"links":{"cited_paper":"/paper/2002.05202","citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:b869378a937817e2f5b24618de54235375fcbc293d5fd13db79759c930261270","observation_id":"ce3cfbe7-0fe9-4c5b-93c8-9beac092a11f","resolution":{"observed_at":"2026-08-03T12:01:01.242044Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:01.315029Z","title":"Nvidia nemo canary model pushes the frontier of speech recognition and translation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:01.315029Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:6d266542bdcf045211a0e73b0100db095a5739bea61f62c1d4d0496eeb8f1e90","observation_id":"0bbdd8a1-6e78-492f-8b5a-fc5aee908dc4","resolution":{"observed_at":"2026-08-03T12:01:01.315029Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-03T12:01:01.414371Z","title":"C risper- whisper: Accurate timestamps on verbatim speech transcrip tions,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-03T12:01:01.414371Z"},"links":{"citing_paper":"/paper/2601.19919"},"observation_digest":"sha256:d501d2875df46e0890d4b4923db905b4beea52e598194879e11aa15e0205b075","observation_id":"4416bbdf-bc15-4697-a8de-ba3f1ed5df83","resolution":{"observed_at":"2026-08-03T12:01:01.414371Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2601.19919","last_updated":"2026-06-01T03:38:43Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-09T23:25:28.906145Z","submitted_at":"2026-01-08T08:05:30Z","title":"ASKD-Whisper: Adaptive Self-knowledge Distillation for Efficient and Low-Latency Automatic Speech Recognition"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":38,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":39},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 9 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 1 inbound Pith citation observation for arXiv:2601.19919."}