{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:Q4QMTXFKILEYCK5AWBCWND3YTX","short_pith_number":"pith:Q4QMTXFK","schema_version":"1.0","canonical_sha256":"8720c9dcaa42c9812ba0b045668f789dd710716ac62c081da2ab7f2e0d8ec221","source":{"kind":"arxiv","id":"2102.02074","version":1},"attestation_state":"computed","paper":{"title":"Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Achintya Kumar Sarkar, Md Sahidullah, Zheng-Hua Tan","submitted_at":"2021-02-03T14:06:29Z","abstract_excerpt":"In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment set with two methods: (i) transfer learning and (ii) training from scratch. Next, feature vectors of a given utterance are fed to the PP-DNNs and the output from each PP-DNN at frame-level is considered one new set of generated data. The generated data from each PP-DNN is then us"},"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":"2102.02074","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2021-02-03T14:06:29Z","cross_cats_sorted":["cs.LG","eess.AS"],"title_canon_sha256":"106d1dabf538c72a42b6f6323ea8f05c25465baa5caf4a9c2f301622fc3cef3a","abstract_canon_sha256":"c241acf0193f3311ce41e954cb0a5c66c3f3259b4885c321d0c37427768ee4f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:12:36.466780Z","signature_b64":"CfVAM+LgbAm3L9zpsI6wgDI7vD9+8+b694wMwQ/rv77RG/J/O0nUMtYrMnjE5X9w2Msw7BV5n9+5qeeRtn34AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8720c9dcaa42c9812ba0b045668f789dd710716ac62c081da2ab7f2e0d8ec221","last_reissued_at":"2026-07-05T02:12:36.466367Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:12:36.466367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data Generation Using Pass-phrase-dependent Deep Auto-encoders for Text-Dependent Speaker Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Achintya Kumar Sarkar, Md Sahidullah, Zheng-Hua Tan","submitted_at":"2021-02-03T14:06:29Z","abstract_excerpt":"In this paper, we propose a novel method that trains pass-phrase specific deep neural network (PP-DNN) based auto-encoders for creating augmented data for text-dependent speaker verification (TD-SV). Each PP-DNN auto-encoder is trained using the utterances of a particular pass-phrase available in the target enrollment set with two methods: (i) transfer learning and (ii) training from scratch. Next, feature vectors of a given utterance are fed to the PP-DNNs and the output from each PP-DNN at frame-level is considered one new set of generated data. The generated data from each PP-DNN is then us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2102.02074","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2102.02074/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2102.02074","created_at":"2026-07-05T02:12:36.466425+00:00"},{"alias_kind":"arxiv_version","alias_value":"2102.02074v1","created_at":"2026-07-05T02:12:36.466425+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2102.02074","created_at":"2026-07-05T02:12:36.466425+00:00"},{"alias_kind":"pith_short_12","alias_value":"Q4QMTXFKILEY","created_at":"2026-07-05T02:12:36.466425+00:00"},{"alias_kind":"pith_short_16","alias_value":"Q4QMTXFKILEYCK5A","created_at":"2026-07-05T02:12:36.466425+00:00"},{"alias_kind":"pith_short_8","alias_value":"Q4QMTXFK","created_at":"2026-07-05T02:12:36.466425+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/Q4QMTXFKILEYCK5AWBCWND3YTX","json":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX.json","graph_json":"https://pith.science/api/pith-number/Q4QMTXFKILEYCK5AWBCWND3YTX/graph.json","events_json":"https://pith.science/api/pith-number/Q4QMTXFKILEYCK5AWBCWND3YTX/events.json","paper":"https://pith.science/paper/Q4QMTXFK"},"agent_actions":{"view_html":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX","download_json":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX.json","view_paper":"https://pith.science/paper/Q4QMTXFK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2102.02074&json=true","fetch_graph":"https://pith.science/api/pith-number/Q4QMTXFKILEYCK5AWBCWND3YTX/graph.json","fetch_events":"https://pith.science/api/pith-number/Q4QMTXFKILEYCK5AWBCWND3YTX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX/action/storage_attestation","attest_author":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX/action/author_attestation","sign_citation":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX/action/citation_signature","submit_replication":"https://pith.science/pith/Q4QMTXFKILEYCK5AWBCWND3YTX/action/replication_record"}},"created_at":"2026-07-05T02:12:36.466425+00:00","updated_at":"2026-07-05T02:12:36.466425+00:00"}