{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:AIOQJPI37WJJU4XGN6EP2H76U4","short_pith_number":"pith:AIOQJPI3","schema_version":"1.0","canonical_sha256":"021d04bd1bfd929a72e66f88fd1ffea7021b5e1dae711789e8a391c8055d197f","source":{"kind":"arxiv","id":"2112.12522","version":2},"attestation_state":"computed","paper":{"title":"Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang","submitted_at":"2021-12-23T13:23:17Z","abstract_excerpt":"Automatic speech recognition (ASR) has shown rapid advances in recent years but still degrades significantly in far-field and noisy environments. The recent development of self-supervised learning (SSL) technology can improve the ASR performance by pre-training the model with additional unlabeled speech and the SSL pre-trained model has achieved the state-of-the-art result on several speech benchmarks. Nevertheless, most of the previous SSL methods ignore the influence of the background noise or reverberation, which is crucial to deploying ASR systems in real-world speech applications. This st"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2112.12522","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-12-23T13:23:17Z","cross_cats_sorted":["cs.CL","eess.AS"],"title_canon_sha256":"0758899f02639ffe93d10918949a26b8249ff84f9e37aa057ede1a5359183a6e","abstract_canon_sha256":"48f8949bcbf4bd69961abdd2e734ad8fd51117a2ca2f01a286041b39b3a721e3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:20:17.809943Z","signature_b64":"uQKo+ihVARCgU1yaxrCDiQgWbmFmD3KLUmS1f2rVaVveSRq6PO17sOJpS+osm5JUmAGSFY65IJOHgTEaGVhgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"021d04bd1bfd929a72e66f88fd1ffea7021b5e1dae711789e8a391c8055d197f","last_reissued_at":"2026-07-05T04:20:17.809511Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:20:17.809511Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Variant Consistency based Self-supervised Learning for Robust Automatic Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Changfeng Gao, Gaofeng Cheng, Pengyuan Zhang","submitted_at":"2021-12-23T13:23:17Z","abstract_excerpt":"Automatic speech recognition (ASR) has shown rapid advances in recent years but still degrades significantly in far-field and noisy environments. The recent development of self-supervised learning (SSL) technology can improve the ASR performance by pre-training the model with additional unlabeled speech and the SSL pre-trained model has achieved the state-of-the-art result on several speech benchmarks. Nevertheless, most of the previous SSL methods ignore the influence of the background noise or reverberation, which is crucial to deploying ASR systems in real-world speech applications. This st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.12522","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2112.12522/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2112.12522","created_at":"2026-07-05T04:20:17.809569+00:00"},{"alias_kind":"arxiv_version","alias_value":"2112.12522v2","created_at":"2026-07-05T04:20:17.809569+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.12522","created_at":"2026-07-05T04:20:17.809569+00:00"},{"alias_kind":"pith_short_12","alias_value":"AIOQJPI37WJJ","created_at":"2026-07-05T04:20:17.809569+00:00"},{"alias_kind":"pith_short_16","alias_value":"AIOQJPI37WJJU4XG","created_at":"2026-07-05T04:20:17.809569+00:00"},{"alias_kind":"pith_short_8","alias_value":"AIOQJPI3","created_at":"2026-07-05T04:20:17.809569+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4","json":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4.json","graph_json":"https://pith.science/api/pith-number/AIOQJPI37WJJU4XGN6EP2H76U4/graph.json","events_json":"https://pith.science/api/pith-number/AIOQJPI37WJJU4XGN6EP2H76U4/events.json","paper":"https://pith.science/paper/AIOQJPI3"},"agent_actions":{"view_html":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4","download_json":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4.json","view_paper":"https://pith.science/paper/AIOQJPI3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2112.12522&json=true","fetch_graph":"https://pith.science/api/pith-number/AIOQJPI37WJJU4XGN6EP2H76U4/graph.json","fetch_events":"https://pith.science/api/pith-number/AIOQJPI37WJJU4XGN6EP2H76U4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4/action/storage_attestation","attest_author":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4/action/author_attestation","sign_citation":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4/action/citation_signature","submit_replication":"https://pith.science/pith/AIOQJPI37WJJU4XGN6EP2H76U4/action/replication_record"}},"created_at":"2026-07-05T04:20:17.809569+00:00","updated_at":"2026-07-05T04:20:17.809569+00:00"}