{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Z333AQ72WQ6AIWMF5IEDMVDHE5","short_pith_number":"pith:Z333AQ72","canonical_record":{"source":{"id":"2203.14688","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-03-28T12:41:41Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"ef80366d4e30276f3ca2cddc1e6ef39e36cf3096461d178c6ec570542ca413dc","abstract_canon_sha256":"72ec42bf4f727df76433064b8c4433dbacf24a853b78df9c42874daaa8c2747b"},"schema_version":"1.0"},"canonical_sha256":"cef7b043fab43c045985ea0836546727483a509e53fc339afd0103b25e8fddfa","source":{"kind":"arxiv","id":"2203.14688","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14688","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14688v2","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14688","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"pith_short_12","alias_value":"Z333AQ72WQ6A","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"pith_short_16","alias_value":"Z333AQ72WQ6AIWMF","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"pith_short_8","alias_value":"Z333AQ72","created_at":"2026-07-05T05:45:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Z333AQ72WQ6AIWMF5IEDMVDHE5","target":"record","payload":{"canonical_record":{"source":{"id":"2203.14688","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-03-28T12:41:41Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"ef80366d4e30276f3ca2cddc1e6ef39e36cf3096461d178c6ec570542ca413dc","abstract_canon_sha256":"72ec42bf4f727df76433064b8c4433dbacf24a853b78df9c42874daaa8c2747b"},"schema_version":"1.0"},"canonical_sha256":"cef7b043fab43c045985ea0836546727483a509e53fc339afd0103b25e8fddfa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:45:27.351906Z","signature_b64":"AinP5hgKbuhjCom2ncwyQXP//rLvAMLmeFoMGj6KEw8/I73SNFpQxa7Z6LWLe/zmAF0ncszqzJbTL59F/efFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cef7b043fab43c045985ea0836546727483a509e53fc339afd0103b25e8fddfa","last_reissued_at":"2026-07-05T05:45:27.351458Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:45:27.351458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.14688","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:45:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0ZoBjpOYZrqAYwXPkq0nk9fnrFKji9ezXaJcG0XNJkpeMXJhidrzfTLduGoXBX979ZjwhbHW2MIgdFCv9uRZDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:05:50.646485Z"},"content_sha256":"15bcfa10f54ebc16cbccedd30e47f0035ad34d6752fb151136c778eda2e9cf6c","schema_version":"1.0","event_id":"sha256:15bcfa10f54ebc16cbccedd30e47f0035ad34d6752fb151136c778eda2e9cf6c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Z333AQ72WQ6AIWMF5IEDMVDHE5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Training speaker recognition systems with limited data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"David A. van Leeuwen, Nik Vaessen","submitted_at":"2022-03-28T12:41:41Z","abstract_excerpt":"This work considers training neural networks for speaker recognition with a much smaller dataset size compared to contemporary work. We artificially restrict the amount of data by proposing three subsets of the popular VoxCeleb2 dataset. These subsets are restricted to 50\\,k audio files (versus over 1\\,M files available), and vary on the axis of number of speakers and session variability. We train three speaker recognition systems on these subsets; the X-vector, ECAPA-TDNN, and wav2vec2 network architectures. We show that the self-supervised, pre-trained weights of wav2vec2 substantially impro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14688","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/2203.14688/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":"2026-07-05T05:45:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EPX3FjGbfPAbX0WA51kC3HdErkxGjbgxd82NMAo5egbUGoNzBaHL5iTFd0esP8AatiJmt6jvJOKoieNosSkRDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T05:05:50.646917Z"},"content_sha256":"94667466a264cacebba53ff798a608d93cf4310d84d3ff4ffc89a71611eab472","schema_version":"1.0","event_id":"sha256:94667466a264cacebba53ff798a608d93cf4310d84d3ff4ffc89a71611eab472"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5/bundle.json","state_url":"https://pith.science/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5/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":"2026-07-09T05:05:50Z","links":{"resolver":"https://pith.science/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5","bundle":"https://pith.science/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5/bundle.json","state":"https://pith.science/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z333AQ72WQ6AIWMF5IEDMVDHE5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Z333AQ72WQ6AIWMF5IEDMVDHE5","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":"72ec42bf4f727df76433064b8c4433dbacf24a853b78df9c42874daaa8c2747b","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-03-28T12:41:41Z","title_canon_sha256":"ef80366d4e30276f3ca2cddc1e6ef39e36cf3096461d178c6ec570542ca413dc"},"schema_version":"1.0","source":{"id":"2203.14688","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.14688","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"arxiv_version","alias_value":"2203.14688v2","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.14688","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"pith_short_12","alias_value":"Z333AQ72WQ6A","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"pith_short_16","alias_value":"Z333AQ72WQ6AIWMF","created_at":"2026-07-05T05:45:27Z"},{"alias_kind":"pith_short_8","alias_value":"Z333AQ72","created_at":"2026-07-05T05:45:27Z"}],"graph_snapshots":[{"event_id":"sha256:94667466a264cacebba53ff798a608d93cf4310d84d3ff4ffc89a71611eab472","target":"graph","created_at":"2026-07-05T05:45:27Z","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/2203.14688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work considers training neural networks for speaker recognition with a much smaller dataset size compared to contemporary work. We artificially restrict the amount of data by proposing three subsets of the popular VoxCeleb2 dataset. These subsets are restricted to 50\\,k audio files (versus over 1\\,M files available), and vary on the axis of number of speakers and session variability. We train three speaker recognition systems on these subsets; the X-vector, ECAPA-TDNN, and wav2vec2 network architectures. We show that the self-supervised, pre-trained weights of wav2vec2 substantially impro","authors_text":"David A. van Leeuwen, Nik Vaessen","cross_cats":["cs.LG","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-03-28T12:41:41Z","title":"Training speaker recognition systems with limited data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.14688","kind":"arxiv","version":2},"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":"sha256:15bcfa10f54ebc16cbccedd30e47f0035ad34d6752fb151136c778eda2e9cf6c","target":"record","created_at":"2026-07-05T05:45:27Z","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":"72ec42bf4f727df76433064b8c4433dbacf24a853b78df9c42874daaa8c2747b","cross_cats_sorted":["cs.LG","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-03-28T12:41:41Z","title_canon_sha256":"ef80366d4e30276f3ca2cddc1e6ef39e36cf3096461d178c6ec570542ca413dc"},"schema_version":"1.0","source":{"id":"2203.14688","kind":"arxiv","version":2}},"canonical_sha256":"cef7b043fab43c045985ea0836546727483a509e53fc339afd0103b25e8fddfa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cef7b043fab43c045985ea0836546727483a509e53fc339afd0103b25e8fddfa","first_computed_at":"2026-07-05T05:45:27.351458Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:45:27.351458Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AinP5hgKbuhjCom2ncwyQXP//rLvAMLmeFoMGj6KEw8/I73SNFpQxa7Z6LWLe/zmAF0ncszqzJbTL59F/efFAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:45:27.351906Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.14688","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15bcfa10f54ebc16cbccedd30e47f0035ad34d6752fb151136c778eda2e9cf6c","sha256:94667466a264cacebba53ff798a608d93cf4310d84d3ff4ffc89a71611eab472"],"state_sha256":"50b8b5bfeea78090c0b9211bf9dda917e99133ecf2a5baf983414615efd9a31a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fANcnmTkhclICsG9Kk5cXHyrnHukybFQzbTaIys1UOnFo56o3/h5dB4HTK1hOAJUV4I0VDFnAK2PGjsN3ax3Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T05:05:50.649786Z","bundle_sha256":"17afae8864de61c5cc357c73bbed90eeab7d4f8cbcff4eaa9b671c684bc3eef4"}}