{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:HPDO4PJHJHIWWYAXIN2PKJKWYC","short_pith_number":"pith:HPDO4PJH","canonical_record":{"source":{"id":"1905.11173","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-05-27T12:41:36Z","cross_cats_sorted":["cs.CL","eess.AS"],"title_canon_sha256":"68b8f930b29f8746733c66ff50a0c9ce64b4a59d2fe50faa7b286461f8f24ed2","abstract_canon_sha256":"fd9804c7e760c041f663faea789df3c75ad9c70c5505b5b609b9c45c715d9176"},"schema_version":"1.0"},"canonical_sha256":"3bc6ee3d2749d16b60174374f52556c09b91ae7c49fafb2308c69bd0df74d1d7","source":{"kind":"arxiv","id":"1905.11173","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11173","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11173v3","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11173","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"pith_short_12","alias_value":"HPDO4PJHJHIW","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"pith_short_16","alias_value":"HPDO4PJHJHIWWYAX","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"pith_short_8","alias_value":"HPDO4PJH","created_at":"2026-07-05T00:45:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:HPDO4PJHJHIWWYAXIN2PKJKWYC","target":"record","payload":{"canonical_record":{"source":{"id":"1905.11173","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-05-27T12:41:36Z","cross_cats_sorted":["cs.CL","eess.AS"],"title_canon_sha256":"68b8f930b29f8746733c66ff50a0c9ce64b4a59d2fe50faa7b286461f8f24ed2","abstract_canon_sha256":"fd9804c7e760c041f663faea789df3c75ad9c70c5505b5b609b9c45c715d9176"},"schema_version":"1.0"},"canonical_sha256":"3bc6ee3d2749d16b60174374f52556c09b91ae7c49fafb2308c69bd0df74d1d7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:45:51.161692Z","signature_b64":"PWiEAiu67nTmNVfjwAfRhYtIBN9shfSrQqJ91oWeQcxkcommY4/Lp8CTdZeDqbK+bqucBwcZcShT3BGp9RrQDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3bc6ee3d2749d16b60174374f52556c09b91ae7c49fafb2308c69bd0df74d1d7","last_reissued_at":"2026-07-05T00:45:51.161327Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:45:51.161327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1905.11173","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:45:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vEn9jfpQI3J0Spj4mfRSmy4LvqjBrWKISChETyKrp5OiihSFQdUAGMrd/9eptDmWhSN9Jq4nYVChCqS3x4SVAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:23:43.724043Z"},"content_sha256":"9938c7df7473f46f95f3360c7948b89167a6a17331fbb2a57f5cfba2249979f4","schema_version":"1.0","event_id":"sha256:9938c7df7473f46f95f3360c7948b89167a6a17331fbb2a57f5cfba2249979f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:HPDO4PJHJHIWWYAXIN2PKJKWYC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Haichao Du, Hang Zhou, Jianwei Tai, Qingjia Huang, Weijuan Zhang, Xiaoqi Jia, Yakai Li","submitted_at":"2019-05-27T12:41:36Z","abstract_excerpt":"Despite the remarkable progress made in synthesizing emotional speech from text, it is still challenging to provide emotion information to existing speech segments. Previous methods mainly rely on parallel data, and few works have studied the generalization ability for one model to transfer emotion information across different languages. To cope with such problems, we propose an emotion transfer system named ET-GAN, for learning language-independent emotion transfer from one emotion to another without parallel training samples. Based on cycle-consistent generative adversarial network, our meth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11173","kind":"arxiv","version":3},"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/1905.11173/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:45:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Y/8WTLDPeOWzLwvS8OttfiMGtR9NmEht+rt3636SspbnEEb4G94oRCQJf8tMokL6k5qBBavmcM2yNve6dY4cDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T22:23:43.724720Z"},"content_sha256":"7c8bb6bad228e52eb065493b67bfd2b87ab674187bdaa95793a2e471235fd6ac","schema_version":"1.0","event_id":"sha256:7c8bb6bad228e52eb065493b67bfd2b87ab674187bdaa95793a2e471235fd6ac"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC/bundle.json","state_url":"https://pith.science/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T22:23:43Z","links":{"resolver":"https://pith.science/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC","bundle":"https://pith.science/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC/bundle.json","state":"https://pith.science/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HPDO4PJHJHIWWYAXIN2PKJKWYC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:HPDO4PJHJHIWWYAXIN2PKJKWYC","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"fd9804c7e760c041f663faea789df3c75ad9c70c5505b5b609b9c45c715d9176","cross_cats_sorted":["cs.CL","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-05-27T12:41:36Z","title_canon_sha256":"68b8f930b29f8746733c66ff50a0c9ce64b4a59d2fe50faa7b286461f8f24ed2"},"schema_version":"1.0","source":{"id":"1905.11173","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11173","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11173v3","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11173","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"pith_short_12","alias_value":"HPDO4PJHJHIW","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"pith_short_16","alias_value":"HPDO4PJHJHIWWYAX","created_at":"2026-07-05T00:45:51Z"},{"alias_kind":"pith_short_8","alias_value":"HPDO4PJH","created_at":"2026-07-05T00:45:51Z"}],"graph_snapshots":[{"event_id":"sha256:7c8bb6bad228e52eb065493b67bfd2b87ab674187bdaa95793a2e471235fd6ac","target":"graph","created_at":"2026-07-05T00:45:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1905.11173/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the remarkable progress made in synthesizing emotional speech from text, it is still challenging to provide emotion information to existing speech segments. Previous methods mainly rely on parallel data, and few works have studied the generalization ability for one model to transfer emotion information across different languages. To cope with such problems, we propose an emotion transfer system named ET-GAN, for learning language-independent emotion transfer from one emotion to another without parallel training samples. Based on cycle-consistent generative adversarial network, our meth","authors_text":"Haichao Du, Hang Zhou, Jianwei Tai, Qingjia Huang, Weijuan Zhang, Xiaoqi Jia, Yakai Li","cross_cats":["cs.CL","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-05-27T12:41:36Z","title":"ET-GAN: Cross-Language Emotion Transfer Based on Cycle-Consistent Generative Adversarial Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11173","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9938c7df7473f46f95f3360c7948b89167a6a17331fbb2a57f5cfba2249979f4","target":"record","created_at":"2026-07-05T00:45:51Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"fd9804c7e760c041f663faea789df3c75ad9c70c5505b5b609b9c45c715d9176","cross_cats_sorted":["cs.CL","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2019-05-27T12:41:36Z","title_canon_sha256":"68b8f930b29f8746733c66ff50a0c9ce64b4a59d2fe50faa7b286461f8f24ed2"},"schema_version":"1.0","source":{"id":"1905.11173","kind":"arxiv","version":3}},"canonical_sha256":"3bc6ee3d2749d16b60174374f52556c09b91ae7c49fafb2308c69bd0df74d1d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3bc6ee3d2749d16b60174374f52556c09b91ae7c49fafb2308c69bd0df74d1d7","first_computed_at":"2026-07-05T00:45:51.161327Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:45:51.161327Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PWiEAiu67nTmNVfjwAfRhYtIBN9shfSrQqJ91oWeQcxkcommY4/Lp8CTdZeDqbK+bqucBwcZcShT3BGp9RrQDw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:45:51.161692Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11173","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9938c7df7473f46f95f3360c7948b89167a6a17331fbb2a57f5cfba2249979f4","sha256:7c8bb6bad228e52eb065493b67bfd2b87ab674187bdaa95793a2e471235fd6ac"],"state_sha256":"215e1ff7d71835e378ef30a05d1724ce2b2c4fd7889b5e3abe0da81c7278311d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xag9O86io3eZCrX16p8clGrAMZ47n/Ki8V/kPnputFN9kgBZnztLj9gnjlqjU3whItFdnrkmUVutgc9UrKDjBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T22:23:43.729781Z","bundle_sha256":"92c9dddd085943006156b17553284e8874c0e60a08d28e45d09b59745939d68a"}}