{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MDXB4II54IRXGRFOIFXOSZRLJV","short_pith_number":"pith:MDXB4II5","schema_version":"1.0","canonical_sha256":"60ee1e211de2237344ae416ee9662b4d7c7b1257c95563af6cb772a2cd1565d7","source":{"kind":"arxiv","id":"2008.12527","version":1},"attestation_state":"computed","paper":{"title":"Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Tomoki Toda, Wen-Chin Huang, Xiaohai Tian, Yi Zhao, Zhenhua Ling","submitted_at":"2020-08-28T07:48:17Z","abstract_excerpt":"The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset. In 2020, we organized the third edition of the challenge and constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. After a two-month challenge period, we received 33 submissions, including 3 baselines built on the database. From the results of crowd-sourced listening tests, we observed that VC methods have progressed rapidly thanks to advanced deep learning methods. In particular, s"},"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":"2008.12527","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-08-28T07:48:17Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"9975a07329922f4e3ac268f4ace137c8efb65158ca89f16e022f2e54facc5564","abstract_canon_sha256":"123c36f3cba440b2cc303e9c33b7ac31760911872df7ba5228bb790ceee773f0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:31:16.155650Z","signature_b64":"CRuqkiVsxrP8b4HFD+MXHfxAKmbxpVkp3bMMBTFHJByHRis0MuSYga2LWc9SLf1nqPtA5kE6bGhysq2Op6X2CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60ee1e211de2237344ae416ee9662b4d7c7b1257c95563af6cb772a2cd1565d7","last_reissued_at":"2026-07-05T01:31:16.155210Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:31:16.155210Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Voice Conversion Challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Tomoki Toda, Wen-Chin Huang, Xiaohai Tian, Yi Zhao, Zhenhua Ling","submitted_at":"2020-08-28T07:48:17Z","abstract_excerpt":"The voice conversion challenge is a bi-annual scientific event held to compare and understand different voice conversion (VC) systems built on a common dataset. In 2020, we organized the third edition of the challenge and constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. After a two-month challenge period, we received 33 submissions, including 3 baselines built on the database. From the results of crowd-sourced listening tests, we observed that VC methods have progressed rapidly thanks to advanced deep learning methods. In particular, s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.12527","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/2008.12527/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":"2008.12527","created_at":"2026-07-05T01:31:16.155267+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.12527v1","created_at":"2026-07-05T01:31:16.155267+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.12527","created_at":"2026-07-05T01:31:16.155267+00:00"},{"alias_kind":"pith_short_12","alias_value":"MDXB4II54IRX","created_at":"2026-07-05T01:31:16.155267+00:00"},{"alias_kind":"pith_short_16","alias_value":"MDXB4II54IRXGRFO","created_at":"2026-07-05T01:31:16.155267+00:00"},{"alias_kind":"pith_short_8","alias_value":"MDXB4II5","created_at":"2026-07-05T01:31:16.155267+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.11283","citing_title":"Multimodal Large Language Model-Enabled Video Translation: A Role-Oriented Survey","ref_index":199,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV","json":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV.json","graph_json":"https://pith.science/api/pith-number/MDXB4II54IRXGRFOIFXOSZRLJV/graph.json","events_json":"https://pith.science/api/pith-number/MDXB4II54IRXGRFOIFXOSZRLJV/events.json","paper":"https://pith.science/paper/MDXB4II5"},"agent_actions":{"view_html":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV","download_json":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV.json","view_paper":"https://pith.science/paper/MDXB4II5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.12527&json=true","fetch_graph":"https://pith.science/api/pith-number/MDXB4II54IRXGRFOIFXOSZRLJV/graph.json","fetch_events":"https://pith.science/api/pith-number/MDXB4II54IRXGRFOIFXOSZRLJV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV/action/storage_attestation","attest_author":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV/action/author_attestation","sign_citation":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV/action/citation_signature","submit_replication":"https://pith.science/pith/MDXB4II54IRXGRFOIFXOSZRLJV/action/replication_record"}},"created_at":"2026-07-05T01:31:16.155267+00:00","updated_at":"2026-07-05T01:31:16.155267+00:00"}