{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:CZUCPDEO3M3EYWWLQMHO4ZV7H3","short_pith_number":"pith:CZUCPDEO","canonical_record":{"source":{"id":"2203.16776","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-03-31T03:33:50Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"5aee1c4c9500c19749e06e1cc56037d5c5bd6499ccda8c895473b9833f8e81a5","abstract_canon_sha256":"2d8f529bc4811de13520b9e91f904414250ac1ef247b52f7cf1c65779f50e02b"},"schema_version":"1.0"},"canonical_sha256":"1668278c8edb364c5acb830eee66bf3efdf6f9065c8cc2927a12cef9661d6f6d","source":{"kind":"arxiv","id":"2203.16776","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.16776","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"arxiv_version","alias_value":"2203.16776v4","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.16776","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"pith_short_12","alias_value":"CZUCPDEO3M3E","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"pith_short_16","alias_value":"CZUCPDEO3M3EYWWL","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"pith_short_8","alias_value":"CZUCPDEO","created_at":"2026-07-05T04:45:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:CZUCPDEO3M3EYWWLQMHO4ZV7H3","target":"record","payload":{"canonical_record":{"source":{"id":"2203.16776","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-03-31T03:33:50Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"5aee1c4c9500c19749e06e1cc56037d5c5bd6499ccda8c895473b9833f8e81a5","abstract_canon_sha256":"2d8f529bc4811de13520b9e91f904414250ac1ef247b52f7cf1c65779f50e02b"},"schema_version":"1.0"},"canonical_sha256":"1668278c8edb364c5acb830eee66bf3efdf6f9065c8cc2927a12cef9661d6f6d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:45:48.365733Z","signature_b64":"hEtdqJHa+blI8Ba7xulnF+7OwbIR2NBGSXAF/yTnWVPNlzgWUnuqaN57lRU1h1GNTWrye4enLoqr1Uqmk7CaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1668278c8edb364c5acb830eee66bf3efdf6f9065c8cc2927a12cef9661d6f6d","last_reissued_at":"2026-07-05T04:45:48.365259Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:45:48.365259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.16776","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:45:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TtV3QK75g48oX6gf8QnFWTCm83kaeYWEZMLSw3av1Fx2+bfmxdYeMXp/aqsM3uG1CKtS7WsPw4f17q2JXfpwBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T19:58:08.805889Z"},"content_sha256":"d98eef4aa8ebb049f877dfaff94e59d2ae996499671badac6aedd6f4b7435232","schema_version":"1.0","event_id":"sha256:d98eef4aa8ebb049f877dfaff94e59d2ae996499671badac6aedd6f4b7435232"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:CZUCPDEO3M3EYWWLQMHO4ZV7H3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Empirical Study of Language Model Integration for Transducer based Speech Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"eess.AS","authors_text":"Chen Huang, Guanglu Wan, Huahuan Zheng, Ke Ding, Keyu An, Zhijian Ou","submitted_at":"2022-03-31T03:33:50Z","abstract_excerpt":"Utilizing text-only data with an external language model (ELM) in end-to-end RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class of methods such as density ratio (DR) and internal language model estimation (ILME) have been developed, outperforming the classic shallow fusion (SF) method. The basic idea behind these methods is that RNN-T posterior should first subtract the implicitly learned internal language model (ILM) prior, in order to integrate the ELM. While recent studies suggest that RNN-T only learns some low-order language model information, the DR method us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.16776","kind":"arxiv","version":4},"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/2203.16776/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:45:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y0oT0x0OAinzjvdoMa8zYDkk2oUHNMmiIQ8EAmTwDZ+cBLw1bf6envR1//uuifO1yIqP3OwUz5igx+miftCVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-24T19:58:08.806272Z"},"content_sha256":"06568688e51a4b24d74c2d83761643b49d8057f20d2fa9988b9fb2f8a0bd7ada","schema_version":"1.0","event_id":"sha256:06568688e51a4b24d74c2d83761643b49d8057f20d2fa9988b9fb2f8a0bd7ada"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3/bundle.json","state_url":"https://pith.science/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-24T19:58:08Z","links":{"resolver":"https://pith.science/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3","bundle":"https://pith.science/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3/bundle.json","state":"https://pith.science/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CZUCPDEO3M3EYWWLQMHO4ZV7H3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CZUCPDEO3M3EYWWLQMHO4ZV7H3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2d8f529bc4811de13520b9e91f904414250ac1ef247b52f7cf1c65779f50e02b","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-03-31T03:33:50Z","title_canon_sha256":"5aee1c4c9500c19749e06e1cc56037d5c5bd6499ccda8c895473b9833f8e81a5"},"schema_version":"1.0","source":{"id":"2203.16776","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.16776","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"arxiv_version","alias_value":"2203.16776v4","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.16776","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"pith_short_12","alias_value":"CZUCPDEO3M3E","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"pith_short_16","alias_value":"CZUCPDEO3M3EYWWL","created_at":"2026-07-05T04:45:48Z"},{"alias_kind":"pith_short_8","alias_value":"CZUCPDEO","created_at":"2026-07-05T04:45:48Z"}],"graph_snapshots":[{"event_id":"sha256:06568688e51a4b24d74c2d83761643b49d8057f20d2fa9988b9fb2f8a0bd7ada","target":"graph","created_at":"2026-07-05T04:45:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.16776/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Utilizing text-only data with an external language model (ELM) in end-to-end RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class of methods such as density ratio (DR) and internal language model estimation (ILME) have been developed, outperforming the classic shallow fusion (SF) method. The basic idea behind these methods is that RNN-T posterior should first subtract the implicitly learned internal language model (ILM) prior, in order to integrate the ELM. While recent studies suggest that RNN-T only learns some low-order language model information, the DR method us","authors_text":"Chen Huang, Guanglu Wan, Huahuan Zheng, Ke Ding, Keyu An, Zhijian Ou","cross_cats":["cs.CL","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-03-31T03:33:50Z","title":"An Empirical Study of Language Model Integration for Transducer based Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.16776","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d98eef4aa8ebb049f877dfaff94e59d2ae996499671badac6aedd6f4b7435232","target":"record","created_at":"2026-07-05T04:45:48Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2d8f529bc4811de13520b9e91f904414250ac1ef247b52f7cf1c65779f50e02b","cross_cats_sorted":["cs.CL","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-03-31T03:33:50Z","title_canon_sha256":"5aee1c4c9500c19749e06e1cc56037d5c5bd6499ccda8c895473b9833f8e81a5"},"schema_version":"1.0","source":{"id":"2203.16776","kind":"arxiv","version":4}},"canonical_sha256":"1668278c8edb364c5acb830eee66bf3efdf6f9065c8cc2927a12cef9661d6f6d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1668278c8edb364c5acb830eee66bf3efdf6f9065c8cc2927a12cef9661d6f6d","first_computed_at":"2026-07-05T04:45:48.365259Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:45:48.365259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hEtdqJHa+blI8Ba7xulnF+7OwbIR2NBGSXAF/yTnWVPNlzgWUnuqaN57lRU1h1GNTWrye4enLoqr1Uqmk7CaBg==","signature_status":"signed_v1","signed_at":"2026-07-05T04:45:48.365733Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.16776","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d98eef4aa8ebb049f877dfaff94e59d2ae996499671badac6aedd6f4b7435232","sha256:06568688e51a4b24d74c2d83761643b49d8057f20d2fa9988b9fb2f8a0bd7ada"],"state_sha256":"9f7939d5175d55521fdc671a0eb3e30b83018b9fac237d12119b98e7c0b55363"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WzhJKlQ66NMSHzh4C52MyzRpjhvfj5pLivRXNNkNTc/eNix74h7oD8xIru6UZ/B1xiu63huREW6xLBgGk+8sAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-24T19:58:08.808438Z","bundle_sha256":"4d5effda8a14575213deebd0cb7911ce4342cf74b2b76f0a4b04797d67d34db2"}}