{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:T6I7BTLTMCIQ466DFXUS4K32PI","short_pith_number":"pith:T6I7BTLT","schema_version":"1.0","canonical_sha256":"9f91f0cd7360910e7bc32de92e2b7a7a143ba81c97abc183e53c91458ae4bf9a","source":{"kind":"arxiv","id":"2310.00178","version":1},"attestation_state":"computed","paper":{"title":"Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.CL","authors_text":"Diamantino Caseiro, Ding Zhao, Gan Song, Golan Pundak, Khe Chai Sim, Pat Rondon, Pedro Moreno Mengibar, Rohit Prabhavalkar, Tara Sainath, Tsendsuren Munkhdalai, Weiran Wang, Zelin Wu, Zhong Meng","submitted_at":"2023-09-29T22:50:10Z","abstract_excerpt":"Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios. We propose algorithms for contextual biasing based on the Knuth-Morris-Pratt algorithm for pattern matching. During beam search, we boost the score of a token extension if it extends matching into a set of biasing phrases. Our method simulates the classical approaches often implemented in the weighted finite state transducer (WFST) framework, but avoids the FST language altogether, with careful considerations o"},"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":"2310.00178","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-09-29T22:50:10Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"6219d8be54e24a592b8dc0ad3058e956ae1f991c2f94ba5ff29e326d33397d19","abstract_canon_sha256":"e9eba6a08f2eb7eed60ef98d2e1c1be1cefa3c9989364cee905467b866080ab9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:02.373528Z","signature_b64":"HQx85wNdtEk0r8+h30NS0ErUlEZSLcVN2z+RvarLfDCEFl9rhI3nzMmjt++Pa72pUK2+tqc+NZO3fAm8VRGkCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f91f0cd7360910e7bc32de92e2b7a7a143ba81c97abc183e53c91458ae4bf9a","last_reissued_at":"2026-07-05T06:56:02.373051Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:02.373051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.CL","authors_text":"Diamantino Caseiro, Ding Zhao, Gan Song, Golan Pundak, Khe Chai Sim, Pat Rondon, Pedro Moreno Mengibar, Rohit Prabhavalkar, Tara Sainath, Tsendsuren Munkhdalai, Weiran Wang, Zelin Wu, Zhong Meng","submitted_at":"2023-09-29T22:50:10Z","abstract_excerpt":"Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scenarios. We propose algorithms for contextual biasing based on the Knuth-Morris-Pratt algorithm for pattern matching. During beam search, we boost the score of a token extension if it extends matching into a set of biasing phrases. Our method simulates the classical approaches often implemented in the weighted finite state transducer (WFST) framework, but avoids the FST language altogether, with careful considerations o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00178","kind":"arxiv","version":1},"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/2310.00178/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":"2310.00178","created_at":"2026-07-05T06:56:02.373109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.00178v1","created_at":"2026-07-05T06:56:02.373109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00178","created_at":"2026-07-05T06:56:02.373109+00:00"},{"alias_kind":"pith_short_12","alias_value":"T6I7BTLTMCIQ","created_at":"2026-07-05T06:56:02.373109+00:00"},{"alias_kind":"pith_short_16","alias_value":"T6I7BTLTMCIQ466D","created_at":"2026-07-05T06:56:02.373109+00:00"},{"alias_kind":"pith_short_8","alias_value":"T6I7BTLT","created_at":"2026-07-05T06:56:02.373109+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/T6I7BTLTMCIQ466DFXUS4K32PI","json":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI.json","graph_json":"https://pith.science/api/pith-number/T6I7BTLTMCIQ466DFXUS4K32PI/graph.json","events_json":"https://pith.science/api/pith-number/T6I7BTLTMCIQ466DFXUS4K32PI/events.json","paper":"https://pith.science/paper/T6I7BTLT"},"agent_actions":{"view_html":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI","download_json":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI.json","view_paper":"https://pith.science/paper/T6I7BTLT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.00178&json=true","fetch_graph":"https://pith.science/api/pith-number/T6I7BTLTMCIQ466DFXUS4K32PI/graph.json","fetch_events":"https://pith.science/api/pith-number/T6I7BTLTMCIQ466DFXUS4K32PI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI/action/storage_attestation","attest_author":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI/action/author_attestation","sign_citation":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI/action/citation_signature","submit_replication":"https://pith.science/pith/T6I7BTLTMCIQ466DFXUS4K32PI/action/replication_record"}},"created_at":"2026-07-05T06:56:02.373109+00:00","updated_at":"2026-07-05T06:56:02.373109+00:00"}