{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:NQTFDMJ7SHOGPBXDGDCQORWX3N","short_pith_number":"pith:NQTFDMJ7","schema_version":"1.0","canonical_sha256":"6c2651b13f91dc6786e330c50746d7db736c71a8ab7dc3c3c0face0ffb131364","source":{"kind":"arxiv","id":"1902.02375","version":1},"attestation_state":"computed","paper":{"title":"Centroid-based deep metric learning for speaker recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Frank Rudzicz, Jixuan Wang, Kuan-Chieh Wang, Marc Law, Michael Brudno","submitted_at":"2019-02-06T19:40:33Z","abstract_excerpt":"Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker recognition task. However, there is still a significant performance gap between recognizing speakers in the training set and unseen speakers. The latter case corresponds to the few-shot learning task, where a trained model is evaluated on unseen classes. Here, we optimize a speaker embedding model with prototypical network loss (PNL), a state-of-the-art approach for the few-shot image classification task. The resulting"},"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":"1902.02375","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-02-06T19:40:33Z","cross_cats_sorted":["cs.SD","eess.AS","stat.ML"],"title_canon_sha256":"844bc0bb29a5dc4ac832ef0ba1737ed90d96ce6ec6c3cbb0f8fa5a769b027fde","abstract_canon_sha256":"ec645a552dbc976f8aed4f7a4284b1e8376da042eeabc784eaab157aa84904e0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:33.621192Z","signature_b64":"7IooEXy5EmKsnW5ESgshXCHlF15Foc1NvHuxU2gi9Pcrf5/k3DaUE3SLOxG6qwMB9zZVSOhCzTGFS8gm+Z9hAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6c2651b13f91dc6786e330c50746d7db736c71a8ab7dc3c3c0face0ffb131364","last_reissued_at":"2026-05-17T23:54:33.620518Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:33.620518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Centroid-based deep metric learning for speaker recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS","stat.ML"],"primary_cat":"cs.LG","authors_text":"Frank Rudzicz, Jixuan Wang, Kuan-Chieh Wang, Marc Law, Michael Brudno","submitted_at":"2019-02-06T19:40:33Z","abstract_excerpt":"Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker recognition task. However, there is still a significant performance gap between recognizing speakers in the training set and unseen speakers. The latter case corresponds to the few-shot learning task, where a trained model is evaluated on unseen classes. Here, we optimize a speaker embedding model with prototypical network loss (PNL), a state-of-the-art approach for the few-shot image classification task. The resulting"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.02375","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1902.02375","created_at":"2026-05-17T23:54:33.620636+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.02375v1","created_at":"2026-05-17T23:54:33.620636+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.02375","created_at":"2026-05-17T23:54:33.620636+00:00"},{"alias_kind":"pith_short_12","alias_value":"NQTFDMJ7SHOG","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_16","alias_value":"NQTFDMJ7SHOGPBXD","created_at":"2026-05-18T12:33:24.271573+00:00"},{"alias_kind":"pith_short_8","alias_value":"NQTFDMJ7","created_at":"2026-05-18T12:33:24.271573+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/NQTFDMJ7SHOGPBXDGDCQORWX3N","json":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N.json","graph_json":"https://pith.science/api/pith-number/NQTFDMJ7SHOGPBXDGDCQORWX3N/graph.json","events_json":"https://pith.science/api/pith-number/NQTFDMJ7SHOGPBXDGDCQORWX3N/events.json","paper":"https://pith.science/paper/NQTFDMJ7"},"agent_actions":{"view_html":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N","download_json":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N.json","view_paper":"https://pith.science/paper/NQTFDMJ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.02375&json=true","fetch_graph":"https://pith.science/api/pith-number/NQTFDMJ7SHOGPBXDGDCQORWX3N/graph.json","fetch_events":"https://pith.science/api/pith-number/NQTFDMJ7SHOGPBXDGDCQORWX3N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N/action/storage_attestation","attest_author":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N/action/author_attestation","sign_citation":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N/action/citation_signature","submit_replication":"https://pith.science/pith/NQTFDMJ7SHOGPBXDGDCQORWX3N/action/replication_record"}},"created_at":"2026-05-17T23:54:33.620636+00:00","updated_at":"2026-05-17T23:54:33.620636+00:00"}