{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DXINAD2ER5FZBGSKRERCCEFL5W","short_pith_number":"pith:DXINAD2E","schema_version":"1.0","canonical_sha256":"1dd0d00f448f4b909a4a89222110abedbf8ff7eae1a953306c8605de7a5fa563","source":{"kind":"arxiv","id":"2404.10180","version":2},"attestation_state":"computed","paper":{"title":"Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE","eess.AS"],"primary_cat":"cs.CL","authors_text":"Angad Chandorkar, Christopher Li, Diamantino Caseiro, Gan Song, Golan Pundak, Pat Rondon, Rohit Prabhavalkar, Tsendsuren Munkhdalai, Weiran Wang, Xavier Velez, Zelin Wu, Zhong Meng","submitted_at":"2024-04-15T23:28:13Z","abstract_excerpt":"Contextual biasing enables speech recognizers to transcribe important phrases in the speaker's context, such as contact names, even if they are rare in, or absent from, the training data. Attention-based biasing is a leading approach which allows for full end-to-end cotraining of the recognizer and biasing system and requires no separate inference-time components. Such biasers typically consist of a context encoder; followed by a context filter which narrows down the context to apply, improving per-step inference time; and, finally, context application via cross attention. Though much work has"},"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":"2404.10180","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-04-15T23:28:13Z","cross_cats_sorted":["cs.AI","cs.LG","cs.NE","eess.AS"],"title_canon_sha256":"397098384c0d224807cd7b320440f671daec0d24534ea47358304ceac89914af","abstract_canon_sha256":"5ded33f5b7e156df617ec30b7d5b384ca40176959a95bd17a26d5a346c86f870"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:11:03.778087Z","signature_b64":"bxKPaQGYWCgy6pG58D7pFDND9hPK0egyc3kfNzwHviYixBgG0C2LNeHZR822ukxX4+cdrclPJFdrC8vOivZtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1dd0d00f448f4b909a4a89222110abedbf8ff7eae1a953306c8605de7a5fa563","last_reissued_at":"2026-07-05T08:11:03.777614Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:11:03.777614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.NE","eess.AS"],"primary_cat":"cs.CL","authors_text":"Angad Chandorkar, Christopher Li, Diamantino Caseiro, Gan Song, Golan Pundak, Pat Rondon, Rohit Prabhavalkar, Tsendsuren Munkhdalai, Weiran Wang, Xavier Velez, Zelin Wu, Zhong Meng","submitted_at":"2024-04-15T23:28:13Z","abstract_excerpt":"Contextual biasing enables speech recognizers to transcribe important phrases in the speaker's context, such as contact names, even if they are rare in, or absent from, the training data. Attention-based biasing is a leading approach which allows for full end-to-end cotraining of the recognizer and biasing system and requires no separate inference-time components. Such biasers typically consist of a context encoder; followed by a context filter which narrows down the context to apply, improving per-step inference time; and, finally, context application via cross attention. Though much work has"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.10180","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/2404.10180/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":"2404.10180","created_at":"2026-07-05T08:11:03.777672+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.10180v2","created_at":"2026-07-05T08:11:03.777672+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.10180","created_at":"2026-07-05T08:11:03.777672+00:00"},{"alias_kind":"pith_short_12","alias_value":"DXINAD2ER5FZ","created_at":"2026-07-05T08:11:03.777672+00:00"},{"alias_kind":"pith_short_16","alias_value":"DXINAD2ER5FZBGSK","created_at":"2026-07-05T08:11:03.777672+00:00"},{"alias_kind":"pith_short_8","alias_value":"DXINAD2E","created_at":"2026-07-05T08:11:03.777672+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/DXINAD2ER5FZBGSKRERCCEFL5W","json":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W.json","graph_json":"https://pith.science/api/pith-number/DXINAD2ER5FZBGSKRERCCEFL5W/graph.json","events_json":"https://pith.science/api/pith-number/DXINAD2ER5FZBGSKRERCCEFL5W/events.json","paper":"https://pith.science/paper/DXINAD2E"},"agent_actions":{"view_html":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W","download_json":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W.json","view_paper":"https://pith.science/paper/DXINAD2E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.10180&json=true","fetch_graph":"https://pith.science/api/pith-number/DXINAD2ER5FZBGSKRERCCEFL5W/graph.json","fetch_events":"https://pith.science/api/pith-number/DXINAD2ER5FZBGSKRERCCEFL5W/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W/action/storage_attestation","attest_author":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W/action/author_attestation","sign_citation":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W/action/citation_signature","submit_replication":"https://pith.science/pith/DXINAD2ER5FZBGSKRERCCEFL5W/action/replication_record"}},"created_at":"2026-07-05T08:11:03.777672+00:00","updated_at":"2026-07-05T08:11:03.777672+00:00"}