{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:SB6GPRJZAUSNFL4ERLA3W3RDHD","short_pith_number":"pith:SB6GPRJZ","schema_version":"1.0","canonical_sha256":"907c67c5390524d2af848ac1bb6e2338c4657a847703743c4ad26d1a87b4ee43","source":{"kind":"arxiv","id":"1907.06111","version":1},"attestation_state":"computed","paper":{"title":"Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Hossein Sameti, Hossein Zeinali, Nooshin Maghsoodi, Themos~Stafylakis","submitted_at":"2019-07-13T17:52:17Z","abstract_excerpt":"In this paper, we combine Hidden Markov Models (HMMs) with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform cha"},"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":"1907.06111","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-07-13T17:52:17Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"fff6707992cc0164f701a92951186daf9f2103d31c15fafc557f6c18d1806b90","abstract_canon_sha256":"c4a5b12783196f6a2144de17a51ecf68d2a12fac5dacdaae8082ff6ac057d86e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:42.396763Z","signature_b64":"FJVkkF9sAIOrPkITCC/EFkXIwoB4Iv2vmlkzX0g1VG+flj7eQmN3WnpHVWUkfv6gCW6i+c3IbZuCikdt3WhPCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"907c67c5390524d2af848ac1bb6e2338c4657a847703743c4ad26d1a87b4ee43","last_reissued_at":"2026-05-17T23:40:42.396236Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:42.396236Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Speaker Recognition with Random Digit Strings Using Uncertainty Normalized HMM-based i-vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Hossein Sameti, Hossein Zeinali, Nooshin Maghsoodi, Themos~Stafylakis","submitted_at":"2019-07-13T17:52:17Z","abstract_excerpt":"In this paper, we combine Hidden Markov Models (HMMs) with i-vector extractors to address the problem of text-dependent speaker recognition with random digit strings. We employ digit-specific HMMs to segment the utterances into digits, to perform frame alignment to HMM states and to extract Baum-Welch statistics. By making use of the natural partition of input features into digits, we train digit-specific i-vector extractors on top of each HMM and we extract well-localized i-vectors, each modelling merely the phonetic content corresponding to a single digit. We then examine ways to perform cha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.06111","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":"1907.06111","created_at":"2026-05-17T23:40:42.396336+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.06111v1","created_at":"2026-05-17T23:40:42.396336+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.06111","created_at":"2026-05-17T23:40:42.396336+00:00"},{"alias_kind":"pith_short_12","alias_value":"SB6GPRJZAUSN","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_16","alias_value":"SB6GPRJZAUSNFL4E","created_at":"2026-05-18T12:33:27.125529+00:00"},{"alias_kind":"pith_short_8","alias_value":"SB6GPRJZ","created_at":"2026-05-18T12:33:27.125529+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/SB6GPRJZAUSNFL4ERLA3W3RDHD","json":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD.json","graph_json":"https://pith.science/api/pith-number/SB6GPRJZAUSNFL4ERLA3W3RDHD/graph.json","events_json":"https://pith.science/api/pith-number/SB6GPRJZAUSNFL4ERLA3W3RDHD/events.json","paper":"https://pith.science/paper/SB6GPRJZ"},"agent_actions":{"view_html":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD","download_json":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD.json","view_paper":"https://pith.science/paper/SB6GPRJZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.06111&json=true","fetch_graph":"https://pith.science/api/pith-number/SB6GPRJZAUSNFL4ERLA3W3RDHD/graph.json","fetch_events":"https://pith.science/api/pith-number/SB6GPRJZAUSNFL4ERLA3W3RDHD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD/action/storage_attestation","attest_author":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD/action/author_attestation","sign_citation":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD/action/citation_signature","submit_replication":"https://pith.science/pith/SB6GPRJZAUSNFL4ERLA3W3RDHD/action/replication_record"}},"created_at":"2026-05-17T23:40:42.396336+00:00","updated_at":"2026-05-17T23:40:42.396336+00:00"}