{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:AMZ2FIRRDIE3S2ARQJUWZE4CW6","short_pith_number":"pith:AMZ2FIRR","schema_version":"1.0","canonical_sha256":"0333a2a2311a09b9681182696c9382b7b5d152d6a65ae6352fbc1c40f0119640","source":{"kind":"arxiv","id":"1907.05905","version":1},"attestation_state":"computed","paper":{"title":"Voice Pathology Detection Using Deep Learning: a Preliminary Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Jesus B. Alonso-Hernandez, Jiri Mekyska, Pavol Harar, Radim Burget, Zdenek Smekal, Zoltan Galaz","submitted_at":"2019-07-12T18:06:02Z","abstract_excerpt":"This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each recording was split into 64 ms Hamming windowed segments with 30 ms overlap. Our trained model achieved"},"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":"1907.05905","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-07-12T18:06:02Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"3be000bee4ed536a5c45e012197cc5e460a149db2f1f696a39c89c5805bc383e","abstract_canon_sha256":"4fe28487406b5d485dbea62a668a1fba93ca907e057f4c80fcccb9acc7c79c65"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:43.043670Z","signature_b64":"vyvMVAGnrWnrwHOX3FoMFDnxd/6SffCGR6rPADXVbvMnXCJjUyG0udTtFoTrRFtxwPfY3SRUxnX/Lj+MtZifAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0333a2a2311a09b9681182696c9382b7b5d152d6a65ae6352fbc1c40f0119640","last_reissued_at":"2026-05-17T23:40:43.043057Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:43.043057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Voice Pathology Detection Using Deep Learning: a Preliminary Study","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Jesus B. Alonso-Hernandez, Jiri Mekyska, Pavol Harar, Radim Burget, Zdenek Smekal, Zoltan Galaz","submitted_at":"2019-07-12T18:06:02Z","abstract_excerpt":"This paper describes a preliminary investigation of Voice Pathology Detection using Deep Neural Networks (DNN). We used voice recordings of sustained vowel /a/ produced at normal pitch from German corpus Saarbruecken Voice Database (SVD). This corpus contains voice recordings and electroglottograph signals of more than 2 000 speakers. The idea behind this experiment is the use of convolutional layers in combination with recurrent Long-Short-Term-Memory (LSTM) layers on raw audio signal. Each recording was split into 64 ms Hamming windowed segments with 30 ms overlap. Our trained model achieved"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05905","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":"1907.05905","created_at":"2026-05-17T23:40:43.043145+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.05905v1","created_at":"2026-05-17T23:40:43.043145+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05905","created_at":"2026-05-17T23:40:43.043145+00:00"},{"alias_kind":"pith_short_12","alias_value":"AMZ2FIRRDIE3","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"AMZ2FIRRDIE3S2AR","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"AMZ2FIRR","created_at":"2026-05-18T12:33:12.712433+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/AMZ2FIRRDIE3S2ARQJUWZE4CW6","json":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6.json","graph_json":"https://pith.science/api/pith-number/AMZ2FIRRDIE3S2ARQJUWZE4CW6/graph.json","events_json":"https://pith.science/api/pith-number/AMZ2FIRRDIE3S2ARQJUWZE4CW6/events.json","paper":"https://pith.science/paper/AMZ2FIRR"},"agent_actions":{"view_html":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6","download_json":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6.json","view_paper":"https://pith.science/paper/AMZ2FIRR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.05905&json=true","fetch_graph":"https://pith.science/api/pith-number/AMZ2FIRRDIE3S2ARQJUWZE4CW6/graph.json","fetch_events":"https://pith.science/api/pith-number/AMZ2FIRRDIE3S2ARQJUWZE4CW6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6/action/storage_attestation","attest_author":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6/action/author_attestation","sign_citation":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6/action/citation_signature","submit_replication":"https://pith.science/pith/AMZ2FIRRDIE3S2ARQJUWZE4CW6/action/replication_record"}},"created_at":"2026-05-17T23:40:43.043145+00:00","updated_at":"2026-05-17T23:40:43.043145+00:00"}