{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GZJ2YBCPZUFVYCF2FOAEEFQLW2","short_pith_number":"pith:GZJ2YBCP","schema_version":"1.0","canonical_sha256":"3653ac044fcd0b5c08ba2b8042160bb6a7f62c1f26863880fe96a610ccdc92b1","source":{"kind":"arxiv","id":"2506.11091","version":2},"attestation_state":"computed","paper":{"title":"Customizing Speech Recognition Model with Large Language Model Feedback","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Guoli Ye, Shaoshi Ling","submitted_at":"2025-06-05T18:42:57Z","abstract_excerpt":"Automatic speech recognition (ASR) systems have achieved strong performance on general transcription tasks. However, they continue to struggle with recognizing rare named entities and adapting to domain mismatches. In contrast, large language models (LLMs), trained on massive internet-scale datasets, are often more effective across a wide range of domains. In this work, we propose a reinforcement learning based approach for unsupervised domain adaptation, leveraging unlabeled data to enhance transcription quality, particularly the named entities affected by domain mismatch, through feedback fr"},"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":"2506.11091","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-05T18:42:57Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"7102398900c57a7506953eb564bbc0ad271ec6fec842f67ec2f370853cf17e73","abstract_canon_sha256":"7af88265ad5090770897957659c86d5dd7f7f47a3953601bc009b1843782ffaa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:56:20.541738Z","signature_b64":"/B77kyaFpork31OogJdfiH7Q7lRevadIoMpYj9O9ssnxpsGsKX8gCDFclDDxVkytbGehARfbCYgIsrUzTV96AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3653ac044fcd0b5c08ba2b8042160bb6a7f62c1f26863880fe96a610ccdc92b1","last_reissued_at":"2026-07-05T11:56:20.541305Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:56:20.541305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Customizing Speech Recognition Model with Large Language Model Feedback","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Guoli Ye, Shaoshi Ling","submitted_at":"2025-06-05T18:42:57Z","abstract_excerpt":"Automatic speech recognition (ASR) systems have achieved strong performance on general transcription tasks. However, they continue to struggle with recognizing rare named entities and adapting to domain mismatches. In contrast, large language models (LLMs), trained on massive internet-scale datasets, are often more effective across a wide range of domains. In this work, we propose a reinforcement learning based approach for unsupervised domain adaptation, leveraging unlabeled data to enhance transcription quality, particularly the named entities affected by domain mismatch, through feedback fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.11091","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/2506.11091/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":"2506.11091","created_at":"2026-07-05T11:56:20.541370+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.11091v2","created_at":"2026-07-05T11:56:20.541370+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.11091","created_at":"2026-07-05T11:56:20.541370+00:00"},{"alias_kind":"pith_short_12","alias_value":"GZJ2YBCPZUFV","created_at":"2026-07-05T11:56:20.541370+00:00"},{"alias_kind":"pith_short_16","alias_value":"GZJ2YBCPZUFVYCF2","created_at":"2026-07-05T11:56:20.541370+00:00"},{"alias_kind":"pith_short_8","alias_value":"GZJ2YBCP","created_at":"2026-07-05T11:56:20.541370+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/GZJ2YBCPZUFVYCF2FOAEEFQLW2","json":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2.json","graph_json":"https://pith.science/api/pith-number/GZJ2YBCPZUFVYCF2FOAEEFQLW2/graph.json","events_json":"https://pith.science/api/pith-number/GZJ2YBCPZUFVYCF2FOAEEFQLW2/events.json","paper":"https://pith.science/paper/GZJ2YBCP"},"agent_actions":{"view_html":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2","download_json":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2.json","view_paper":"https://pith.science/paper/GZJ2YBCP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.11091&json=true","fetch_graph":"https://pith.science/api/pith-number/GZJ2YBCPZUFVYCF2FOAEEFQLW2/graph.json","fetch_events":"https://pith.science/api/pith-number/GZJ2YBCPZUFVYCF2FOAEEFQLW2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2/action/storage_attestation","attest_author":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2/action/author_attestation","sign_citation":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2/action/citation_signature","submit_replication":"https://pith.science/pith/GZJ2YBCPZUFVYCF2FOAEEFQLW2/action/replication_record"}},"created_at":"2026-07-05T11:56:20.541370+00:00","updated_at":"2026-07-05T11:56:20.541370+00:00"}