{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:K6Q4WAGADSBK755PFYHJJVMD5D","short_pith_number":"pith:K6Q4WAGA","schema_version":"1.0","canonical_sha256":"57a1cb00c01c82aff7af2e0e94d583e8c47288e8532b9fdfb0fa0eec4bb414bd","source":{"kind":"arxiv","id":"1907.05584","version":1},"attestation_state":"computed","paper":{"title":"Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Abhijeet Sangwan, Harishchandra Dubey, John Hansen","submitted_at":"2019-07-12T05:54:33Z","abstract_excerpt":"Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio st"},"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":"1907.05584","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-07-12T05:54:33Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"754f4efd8971a3e6822cf02e20db6fe47b4c6106ea7a1e70a18ec9e740fdf2ed","abstract_canon_sha256":"3ff924681ca1c89b1a90090273cb56553b5da1b4c4896f336ee6612d0178c09d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:47.803455Z","signature_b64":"5WbfO7lOPVywiDKr8hNQNMZwn1WaLVrYZk/TLiRsPxE2iliCC/WvMYhORLPPquN4E3+HFiehaH3ev2ehyvkfBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57a1cb00c01c82aff7af2e0e94d583e8c47288e8532b9fdfb0fa0eec4bb414bd","last_reissued_at":"2026-05-17T23:40:47.802639Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:47.802639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Abhijeet Sangwan, Harishchandra Dubey, John Hansen","submitted_at":"2019-07-12T05:54:33Z","abstract_excerpt":"Speaker diarization determines who spoke and when? in an audio stream. In this study, we propose a model-based approach for robust speaker clustering using i-vectors. The ivectors extracted from different segments of same speaker are correlated. We model this correlation with a Markov Random Field (MRF) network. Leveraging the advancements in MRF modeling, we used Toeplitz Inverse Covariance (TIC) matrix to represent the MRF correlation network for each speaker. This approaches captures the sequential structure of i-vectors (or equivalent speaker turns) belonging to same speaker in an audio st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05584","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":"1907.05584","created_at":"2026-05-17T23:40:47.802780+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.05584v1","created_at":"2026-05-17T23:40:47.802780+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05584","created_at":"2026-05-17T23:40:47.802780+00:00"},{"alias_kind":"pith_short_12","alias_value":"K6Q4WAGADSBK","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_16","alias_value":"K6Q4WAGADSBK755P","created_at":"2026-05-18T12:33:21.387695+00:00"},{"alias_kind":"pith_short_8","alias_value":"K6Q4WAGA","created_at":"2026-05-18T12:33:21.387695+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.05584","citing_title":"Toeplitz Inverse Covariance based Robust Speaker Clustering for Naturalistic Audio Streams","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D","json":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D.json","graph_json":"https://pith.science/api/pith-number/K6Q4WAGADSBK755PFYHJJVMD5D/graph.json","events_json":"https://pith.science/api/pith-number/K6Q4WAGADSBK755PFYHJJVMD5D/events.json","paper":"https://pith.science/paper/K6Q4WAGA"},"agent_actions":{"view_html":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D","download_json":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D.json","view_paper":"https://pith.science/paper/K6Q4WAGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.05584&json=true","fetch_graph":"https://pith.science/api/pith-number/K6Q4WAGADSBK755PFYHJJVMD5D/graph.json","fetch_events":"https://pith.science/api/pith-number/K6Q4WAGADSBK755PFYHJJVMD5D/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D/action/timestamp_anchor","attest_storage":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D/action/storage_attestation","attest_author":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D/action/author_attestation","sign_citation":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D/action/citation_signature","submit_replication":"https://pith.science/pith/K6Q4WAGADSBK755PFYHJJVMD5D/action/replication_record"}},"created_at":"2026-05-17T23:40:47.802780+00:00","updated_at":"2026-05-17T23:40:47.802780+00:00"}