{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BFHNTIYROX64YZUTJ4I47QWQTD","short_pith_number":"pith:BFHNTIYR","schema_version":"1.0","canonical_sha256":"094ed9a31175fdcc66934f11cfc2d098e5e0565031e13d534b2c8677e6ccb806","source":{"kind":"arxiv","id":"1812.09484","version":1},"attestation_state":"computed","paper":{"title":"Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Alfonso Ortega, Antonio Miguel, Eduardo Lleida, Victoria Mingote","submitted_at":"2018-12-22T09:25:59Z","abstract_excerpt":"In this paper, we propose a new differentiable neural network alignment mechanism for text-dependent speaker verification which uses alignment models to produce a supervector representation of an utterance. Unlike previous works with similar approaches, we do not extract the embedding of an utterance from the mean reduction of the temporal dimension. Our system replaces the mean by a phrase alignment model to keep the temporal structure of each phrase which is relevant in this application since the phonetic information is part of the identity in the verification task. Moreover, we can apply a "},"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":"1812.09484","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2018-12-22T09:25:59Z","cross_cats_sorted":["cs.LG","eess.AS","stat.ML"],"title_canon_sha256":"cea5555003a148957b53a6c63d05ea7f2744f9bef6c1bb40e1dbb8d601b36376","abstract_canon_sha256":"0924e68fc415ece8b5e5ba2aa522b3a2984702d9155eaa70b8a909cad651dadf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:27.799418Z","signature_b64":"HlZFVw2ewKaupluBHBr7Yvu5F5lBvi1BDpSVhh0OnDsTOlq5vl68WoDifWSz2vYbTSCkBy8zbLe9O+PXOw/JDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"094ed9a31175fdcc66934f11cfc2d098e5e0565031e13d534b2c8677e6ccb806","last_reissued_at":"2026-05-17T23:57:27.798716Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:27.798716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Differentiable Supervector Extraction for Encoding Speaker and Phrase Information in Text Dependent Speaker Verification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.AS","stat.ML"],"primary_cat":"cs.SD","authors_text":"Alfonso Ortega, Antonio Miguel, Eduardo Lleida, Victoria Mingote","submitted_at":"2018-12-22T09:25:59Z","abstract_excerpt":"In this paper, we propose a new differentiable neural network alignment mechanism for text-dependent speaker verification which uses alignment models to produce a supervector representation of an utterance. Unlike previous works with similar approaches, we do not extract the embedding of an utterance from the mean reduction of the temporal dimension. Our system replaces the mean by a phrase alignment model to keep the temporal structure of each phrase which is relevant in this application since the phonetic information is part of the identity in the verification task. Moreover, we can apply a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.09484","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":"1812.09484","created_at":"2026-05-17T23:57:27.798831+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.09484v1","created_at":"2026-05-17T23:57:27.798831+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.09484","created_at":"2026-05-17T23:57:27.798831+00:00"},{"alias_kind":"pith_short_12","alias_value":"BFHNTIYROX64","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BFHNTIYROX64YZUT","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BFHNTIYR","created_at":"2026-05-18T12:32:16.446611+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/BFHNTIYROX64YZUTJ4I47QWQTD","json":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD.json","graph_json":"https://pith.science/api/pith-number/BFHNTIYROX64YZUTJ4I47QWQTD/graph.json","events_json":"https://pith.science/api/pith-number/BFHNTIYROX64YZUTJ4I47QWQTD/events.json","paper":"https://pith.science/paper/BFHNTIYR"},"agent_actions":{"view_html":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD","download_json":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD.json","view_paper":"https://pith.science/paper/BFHNTIYR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.09484&json=true","fetch_graph":"https://pith.science/api/pith-number/BFHNTIYROX64YZUTJ4I47QWQTD/graph.json","fetch_events":"https://pith.science/api/pith-number/BFHNTIYROX64YZUTJ4I47QWQTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD/action/storage_attestation","attest_author":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD/action/author_attestation","sign_citation":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD/action/citation_signature","submit_replication":"https://pith.science/pith/BFHNTIYROX64YZUTJ4I47QWQTD/action/replication_record"}},"created_at":"2026-05-17T23:57:27.798831+00:00","updated_at":"2026-05-17T23:57:27.798831+00:00"}