{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:JFFZIUVESA3AVTSQ6XGFB7RNB7","short_pith_number":"pith:JFFZIUVE","canonical_record":{"source":{"id":"1902.09074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-25T03:32:58Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"11cd9b0e1af1b02d88c82900e28bcc38d62bc43124d88273e62bbca0b6f0b7c7","abstract_canon_sha256":"ef2532a55fc13902fa8ea1713e8d1301c5e6f85082c8a9433190352ca014974a"},"schema_version":"1.0"},"canonical_sha256":"494b9452a490360ace50f5cc50fe2d0fc9254a9d5496acd60f751e4033b757fc","source":{"kind":"arxiv","id":"1902.09074","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09074","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09074v1","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09074","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"pith_short_12","alias_value":"JFFZIUVESA3A","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JFFZIUVESA3AVTSQ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JFFZIUVE","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:JFFZIUVESA3AVTSQ6XGFB7RNB7","target":"record","payload":{"canonical_record":{"source":{"id":"1902.09074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-25T03:32:58Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"11cd9b0e1af1b02d88c82900e28bcc38d62bc43124d88273e62bbca0b6f0b7c7","abstract_canon_sha256":"ef2532a55fc13902fa8ea1713e8d1301c5e6f85082c8a9433190352ca014974a"},"schema_version":"1.0"},"canonical_sha256":"494b9452a490360ace50f5cc50fe2d0fc9254a9d5496acd60f751e4033b757fc","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:46.732128Z","signature_b64":"Krnjt5TQ+TesZ5XPDDicPpIvsrQAfhAgmarYVyuFlznaGsN77aOZ1zSVz7NfFn/XZU1NsKcCP10Sv61BzX/cAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"494b9452a490360ace50f5cc50fe2d0fc9254a9d5496acd60f751e4033b757fc","last_reissued_at":"2026-05-17T23:52:46.731451Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:46.731451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.09074","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:52:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xWJr4SkBa8kvTFy/bKAxpU4AOyw/JFvS0gs7CycL8Hr8scT/ON4QG3T+163rtmi3J8Ae5RUikPNLT/ZjREfPDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T20:32:06.793682Z"},"content_sha256":"f7149573b040cb7cd29a01adf538f93c2e1aeeef1ef564578a883acee05a5aa3","schema_version":"1.0","event_id":"sha256:f7149573b040cb7cd29a01adf538f93c2e1aeeef1ef564578a883acee05a5aa3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:JFFZIUVESA3AVTSQ6XGFB7RNB7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Channel adversarial training for cross-channel text-independent speaker recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Jin Li, Lei Sun, Liang Zou, Xin Fang, Zhen-Hua Ling","submitted_at":"2019-02-25T03:32:58Z","abstract_excerpt":"The conventional speaker recognition frameworks (e.g., the i-vector and CNN-based approach) have been successfully applied to various tasks when the channel of the enrolment dataset is similar to that of the test dataset. However, in real-world applications, mismatch always exists between these two datasets, which may severely deteriorate the recognition performance. Previously, a few channel compensation algorithms have been proposed, such as Linear Discriminant Analysis (LDA) and Probabilistic LDA. However, these methods always require the collections of different channels from a specific sp"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09074","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":""},"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":"2026-05-17T23:52:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ggw+zJviLLWoczo3+/6XRSESqQxAI04PpaUusyN0Ni3wzlsb85+YCoaiQxWflV8wwRkeFR8Eijbc1NIU+nGRDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T20:32:06.794030Z"},"content_sha256":"2ea3e89a93a1cce02ce878420f130e2cb606123b73b82b0c9ed5515b6b09cb5f","schema_version":"1.0","event_id":"sha256:2ea3e89a93a1cce02ce878420f130e2cb606123b73b82b0c9ed5515b6b09cb5f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7/bundle.json","state_url":"https://pith.science/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7/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":"2026-06-10T20:32:06Z","links":{"resolver":"https://pith.science/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7","bundle":"https://pith.science/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7/bundle.json","state":"https://pith.science/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/JFFZIUVESA3AVTSQ6XGFB7RNB7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:JFFZIUVESA3AVTSQ6XGFB7RNB7","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":"ef2532a55fc13902fa8ea1713e8d1301c5e6f85082c8a9433190352ca014974a","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-25T03:32:58Z","title_canon_sha256":"11cd9b0e1af1b02d88c82900e28bcc38d62bc43124d88273e62bbca0b6f0b7c7"},"schema_version":"1.0","source":{"id":"1902.09074","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.09074","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"arxiv_version","alias_value":"1902.09074v1","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.09074","created_at":"2026-05-17T23:52:46Z"},{"alias_kind":"pith_short_12","alias_value":"JFFZIUVESA3A","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"JFFZIUVESA3AVTSQ","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"JFFZIUVE","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:2ea3e89a93a1cce02ce878420f130e2cb606123b73b82b0c9ed5515b6b09cb5f","target":"graph","created_at":"2026-05-17T23:52:46Z","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"},"paper":{"abstract_excerpt":"The conventional speaker recognition frameworks (e.g., the i-vector and CNN-based approach) have been successfully applied to various tasks when the channel of the enrolment dataset is similar to that of the test dataset. However, in real-world applications, mismatch always exists between these two datasets, which may severely deteriorate the recognition performance. Previously, a few channel compensation algorithms have been proposed, such as Linear Discriminant Analysis (LDA) and Probabilistic LDA. However, these methods always require the collections of different channels from a specific sp","authors_text":"Jin Li, Lei Sun, Liang Zou, Xin Fang, Zhen-Hua Ling","cross_cats":["cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-25T03:32:58Z","title":"Channel adversarial training for cross-channel text-independent speaker recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.09074","kind":"arxiv","version":1},"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:f7149573b040cb7cd29a01adf538f93c2e1aeeef1ef564578a883acee05a5aa3","target":"record","created_at":"2026-05-17T23:52:46Z","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":"ef2532a55fc13902fa8ea1713e8d1301c5e6f85082c8a9433190352ca014974a","cross_cats_sorted":["cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-02-25T03:32:58Z","title_canon_sha256":"11cd9b0e1af1b02d88c82900e28bcc38d62bc43124d88273e62bbca0b6f0b7c7"},"schema_version":"1.0","source":{"id":"1902.09074","kind":"arxiv","version":1}},"canonical_sha256":"494b9452a490360ace50f5cc50fe2d0fc9254a9d5496acd60f751e4033b757fc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"494b9452a490360ace50f5cc50fe2d0fc9254a9d5496acd60f751e4033b757fc","first_computed_at":"2026-05-17T23:52:46.731451Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:46.731451Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Krnjt5TQ+TesZ5XPDDicPpIvsrQAfhAgmarYVyuFlznaGsN77aOZ1zSVz7NfFn/XZU1NsKcCP10Sv61BzX/cAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:46.732128Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.09074","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f7149573b040cb7cd29a01adf538f93c2e1aeeef1ef564578a883acee05a5aa3","sha256:2ea3e89a93a1cce02ce878420f130e2cb606123b73b82b0c9ed5515b6b09cb5f"],"state_sha256":"7d31bc1004c1ec09d28d06ca660478c1518781c7ed8936ae352a5e13e8959cf9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z2o701U1PXazOZXETSw9qg4cUyG2qyDxgAmxdqaib5qWJhCqjHcgfW0Edyw0rc+Hoz2hCAK7pZfEZ4GfZ517DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T20:32:06.796012Z","bundle_sha256":"a52b42b7f75d81b6c89103f7209f0addf670291bf8b5eee3c38d87793b5bf93f"}}