{"as_of":"2026-08-09T14:16:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:0d3445b07c9e4c943421d984bc1089aa7a93d85ba5a7690a239c71a864667d09","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":"2026-08-06T20:50:24.633588Z","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":"2026-08-06T20:50:24.633588Z","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":"sha256:9a5450b57db35085d8b3257713a075ca455fbda35cbe1ff705678152ef3b95e4","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":"2026-08-06T20:50:24.633588Z","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":"sha256:2a2211604f34fe0412b6ca30403ae5a510dd8f3e18272cf44cf87c31b8a5775b","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":"2026-08-06T20:50:24.633588Z","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":"2026-08-06T20:50:24.633588Z","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":"sha256:9a5450b57db35085d8b3257713a075ca455fbda35cbe1ff705678152ef3b95e4","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":"2026-08-06T20:50:24.633588Z","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":"sha256:749bec1cc835a7e59816346d9b6276a99339654b4f6da6161504058d8c027968","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":"2026-08-06T20:50:24.633588Z","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":"sha256:23266f8f21903f874ae2958c3a4350c5115136c0f3e5c426562abcd9d11c783b","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":"2026-08-06T20:50:24.633588Z","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:ce57bb11c1300a3b06ba76e4e5beed83eb4d97e0523d49563ac2479e9ea5a3c1","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":"2026-08-06T20:50:24.633588Z","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":"sha256:2470726298d87caef0b18bcdbff152ab5af1392cdeeeb36d68d5d508a7db6364","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":"2026-08-06T20:50:24.633588Z","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":"sha256:22e442fe827026d3a96f38bd051835408f56385a807a1c58d36b0547bc906afe","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":"2026-08-06T20:50:24.633588Z","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":"sha256:7d3e02831988000fe424cdcace68d02704e8aae14da62e1a40e68775674565c9","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":"2026-08-06T20:50:24.633588Z","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":"sha256:0ab568a9b441847d09a51f186d0373d91decbaecec90fb6d027811fc686cc8c2","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":"2026-08-06T20:50:24.633588Z","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":"sha256:8dd4356502d80c94b3e1aaf2bd834e33bbd1f27499b864740a1cb15594cd3d90","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":"2026-08-06T20:50:24.633588Z","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":"sha256:47d4b0931f5d7ef8bd4dfa583f631ee41cfc30d8a4209484d10d02d2176bb10f","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":"2026-08-06T20:50:24.633588Z","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:e82b860495692645a01e75c7fa5a887633a3af52a36d9c83105919bafef15f7d","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":"2026-08-06T20:50:24.633588Z","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:e2c4683cb9b5088614ffcba828b84f9bb8a168bacd94a371878ff4854cc17f56","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":"2026-08-06T20:50:24.633588Z","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":"sha256:3a238d2db566f202762e5d9279ea4ac0dc29faa4f62386fb8f53d8114a084f8d","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":"2026-08-06T20:50:24.633588Z","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":"sha256:8f8e60723d47ccfe6d6a81958fa0cc5989b614441262077b840770f53aace630","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":"2026-08-06T20:50:24.633588Z","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":"sha256:6bb8bec0c71f4264faa388e9d6b73f80130aa99643812fc198e6f74891df56c0","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":"2026-08-06T20:50:24.633588Z","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":"sha256:48bd6e75723768e02a108ee25178a925d8dba5d3355f26d7ffa2c8338bf4b2bb","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":"2026-08-06T20:50:24.633588Z","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:daa60e89dc7cf2c87437f32c12f8976cf9db9c90a025b23013f215944ca25446","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":"2026-08-06T20:50:24.633588Z","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":"sha256:37ef3f842635b1b04f6a269a8cf1baa7a29aac38be07630c59631584a95418c6","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":"2026-08-06T20:50:24.633588Z","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:c15d43a7149b700bd23f79d319814f1afbffa8bc4a4cbcf28b0bc1c0678bfa22","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":"2026-08-06T20:50:24.633588Z","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":"sha256:7f8c3c1637e05769590e983d74039d92c1652f682a2f9c390c05877fbf1d9568","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":"2026-08-06T20:50:24.633588Z","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":"sha256:4fed85f491c9d89df0b0b4f9c7af46e5e832a209245933c4aa75b93ad1f627e7","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":"2026-08-06T20:50:24.633588Z","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":"sha256:3f09f5fc39dc53407b5a3c8908515c824776eeadafa43e4ee5f0f874e6d3221e","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":"2026-08-06T20:50:24.633588Z","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":"sha256:927a6e2dfeebc64fd3a081aff346533b6374bcbbc37724e50adfd5a916049759","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":"2026-08-06T20:50:24.633588Z","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:d8467cf991236dc385a9c16f51968b0ccf6504d00b150c58504c4f097321b8d5","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":"2026-08-06T20:50:24.633588Z","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":"sha256:87ef0f488d3a29f02112f03e96608cd41a12b7f1246d31b3cb45079df84f103c","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":"2026-08-06T20:50:24.633588Z","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:ea6ebb98768dad7c9e8f0f48bb4ba78cae773a79461bd889dd96914b707f31aa","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":"2026-08-06T20:50:24.633588Z","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":"sha256:8d98bd7ce84c1c02d625142b9c2ee855fa6cb4c0cec68418355f8445fe522878","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":"2026-08-06T20:50:24.633588Z","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":"sha256:5f16ad4b45ef0fc4b6c370c0269b57b8555ba4d97a2976da1218f03bd01edb5c","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":"2026-08-06T20:50:24.633588Z","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:a203b0ff05c9b0404009967965ec2d93eeee81db84a2bf28f7a4c0e02753ac3c","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":"2026-08-06T20:50:24.633588Z","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":"sha256:7e4a35e7f3f89e0f01de7a1538546f752c71c7dfbbeefc7ee4f723fc5ed668a9","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":"2026-08-06T20:50:24.633588Z","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":"sha256:27f9a19182b3d7f9c4cddf9e9e7c2b985ab610e68b602b50a530fe00d0d9df98","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":"2026-08-06T20:50:24.633588Z","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:d7c4c9be5cafd7ecfe30c04c8b1fe80031cd617dc4dcdb6dea6c819c88ebd400","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":"2026-08-06T20:50:24.633588Z","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":"sha256:453a21851987dca639d86ab9ca69f19bae989145a9d8b8d44dfe7127de82c4c7","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":"2026-08-06T20:50:24.633588Z","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":"sha256:2ae7b3db85f2fe40c0bae120e461f52a55ff867ff1ab636c3b6e0763472ee485","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":"2026-08-06T20:50:24.633588Z","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:d934ff04cd16c2c8a95805f804559fa1c30ef09991839d142e87285dd567bf1a","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":"2026-08-06T20:50:24.633588Z","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":"sha256:740bd9c6b273017c023fbc18b135dbcf57024b16674bcadbc93276d01af623ad","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":"2026-08-06T20:50:24.633588Z","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:c5b7322de4ef12f1c9d5f1e642fd0a1763dafeb8e8dc32219692bbeb5e753627","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":"2026-08-06T20:50:24.633588Z","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":"sha256:18c16cfeedd2bd7e6017e6d29930d5b3ba80ad39ea6cfb1d9acbf56429167fe2","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":"2026-08-06T20:50:24.633588Z","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."}