{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:BNMKSNP23L3PLPUR4BQULEP5PZ","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":"cdce1c2da0b2ff2c2511056796c016990a4687ea2d465f22cbdfe1c5b3b5a20d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2015-10-20T16:01:05Z","title_canon_sha256":"9b63760b09a40acbb13239a0d50dc640ff652a775632e9d830c4cc60e26fe4e0"},"schema_version":"1.0","source":{"id":"1510.05940","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.05940","created_at":"2026-05-18T01:17:59Z"},{"alias_kind":"arxiv_version","alias_value":"1510.05940v2","created_at":"2026-05-18T01:17:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.05940","created_at":"2026-05-18T01:17:59Z"},{"alias_kind":"pith_short_12","alias_value":"BNMKSNP23L3P","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"BNMKSNP23L3PLPUR","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"BNMKSNP2","created_at":"2026-05-18T12:29:14Z"}],"graph_snapshots":[{"event_id":"sha256:f129249261e54bdd97dead4e0e0438e31dad5539b36da2d47222c63fc22f97bf","target":"graph","created_at":"2026-05-18T01:17:59Z","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":"Probabilistic linear discriminant analysis (PLDA) is a popular normalization approach for the i-vector model, and has delivered state-of-the-art performance in speaker recognition. A potential problem of the PLDA model, however, is that it essentially assumes Gaussian distributions over speaker vectors, which is not always true in practice. Additionally, the objective function is not directly related to the goal of the task, e.g., discriminating true speakers and imposters. In this paper, we propose a max-margin metric learning approach to solve the problems. It learns a linear transform with ","authors_text":"Chao Xing, Dong Wang, Lantian Li, Thomas Fang Zheng","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2015-10-20T16:01:05Z","title":"Max-margin Metric Learning for Speaker Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.05940","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:fc938597fa7bd14e7175b7e09f5dae3e6ff39aab7b15e69a166acecbabf2c82b","target":"record","created_at":"2026-05-18T01:17:59Z","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":"cdce1c2da0b2ff2c2511056796c016990a4687ea2d465f22cbdfe1c5b3b5a20d","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2015-10-20T16:01:05Z","title_canon_sha256":"9b63760b09a40acbb13239a0d50dc640ff652a775632e9d830c4cc60e26fe4e0"},"schema_version":"1.0","source":{"id":"1510.05940","kind":"arxiv","version":2}},"canonical_sha256":"0b58a935fadaf6f5be91e0614591fd7e46254a9602cfd7b3a87a8cf4c6e8ce4a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b58a935fadaf6f5be91e0614591fd7e46254a9602cfd7b3a87a8cf4c6e8ce4a","first_computed_at":"2026-05-18T01:17:59.589580Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:17:59.589580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"V1sZ+ozvr1YrDpLFVrqbzbP4LbVet8LOpVdxIfSng1YFs4lAJp7Yuq8HTyukOtMklncIfZ+d5VBQzoTijT/kBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:17:59.590496Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.05940","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc938597fa7bd14e7175b7e09f5dae3e6ff39aab7b15e69a166acecbabf2c82b","sha256:f129249261e54bdd97dead4e0e0438e31dad5539b36da2d47222c63fc22f97bf"],"state_sha256":"47097813ec8cd494ff30b3d5936b705fee03c90333614846f38f8c736c8c019a"}