{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:KS2ZG6IXMLA2VH2SYJKBXHWPT6","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":"2b603cf3df3979ea5d335aafd65e24fa9d8ca4198cb451b382ddb2b7eb038d5f","cross_cats_sorted":["cs.AI","cs.LG","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-07-14T09:24:55Z","title_canon_sha256":"deb1e882a7956f01a309780baf1b677cb36d9a441443c00afa46a43c0b3a3aa1"},"schema_version":"1.0","source":{"id":"2207.06767","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2207.06767","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"arxiv_version","alias_value":"2207.06767v2","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.06767","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_12","alias_value":"KS2ZG6IXMLA2","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_16","alias_value":"KS2ZG6IXMLA2VH2S","created_at":"2026-07-05T06:50:27Z"},{"alias_kind":"pith_short_8","alias_value":"KS2ZG6IX","created_at":"2026-07-05T06:50:27Z"}],"graph_snapshots":[{"event_id":"sha256:0a7ad3a5b72456a111809f0a102c4db45a1cdb2a37f1ffd16ca2ef97035bcaaf","target":"graph","created_at":"2026-07-05T06:50:27Z","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/2207.06767/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Performance in Speech Emotion Recognition (SER) on a single language has increased greatly in the last few years thanks to the use of deep learning techniques. However, cross-lingual SER remains a challenge in real-world applications due to two main factors: the first is the big gap among the source and the target domain distributions; the second factor is the major availability of unlabeled utterances in contrast to the labeled ones for the new language. Taking into account previous aspects, we propose a Semi-Supervised Learning (SSL) method for cross-lingual emotion recognition when only few","authors_text":"Alexey Petrovsky, Flavio Piccoli, Ivan Shanin, Luigi Celona, Mirko Agarla, Paolo Napoletano, Raimondo Schettini, Simone Bianco","cross_cats":["cs.AI","cs.LG","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-07-14T09:24:55Z","title":"Semi-supervised cross-lingual speech emotion recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.06767","kind":"arxiv","version":2},"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:dab7c01c94a133b86477c7cd5d4d7ab7593b80ce62f52d7772cd6e461e046ecb","target":"record","created_at":"2026-07-05T06:50:27Z","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":"2b603cf3df3979ea5d335aafd65e24fa9d8ca4198cb451b382ddb2b7eb038d5f","cross_cats_sorted":["cs.AI","cs.LG","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-07-14T09:24:55Z","title_canon_sha256":"deb1e882a7956f01a309780baf1b677cb36d9a441443c00afa46a43c0b3a3aa1"},"schema_version":"1.0","source":{"id":"2207.06767","kind":"arxiv","version":2}},"canonical_sha256":"54b593791762c1aa9f52c2541b9ecf9fa7985ec6601bc3d1a8dce3af000e057d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"54b593791762c1aa9f52c2541b9ecf9fa7985ec6601bc3d1a8dce3af000e057d","first_computed_at":"2026-07-05T06:50:27.370151Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:50:27.370151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eqkO30cc1RN4AGX/UVjkPzDV9ut5kmayR6xIj431oLR1hYvA8eGYOEmtaWoweR5E5wZAm1b6dpuUAbzu/x+cDA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:50:27.370692Z","signed_message":"canonical_sha256_bytes"},"source_id":"2207.06767","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:dab7c01c94a133b86477c7cd5d4d7ab7593b80ce62f52d7772cd6e461e046ecb","sha256:0a7ad3a5b72456a111809f0a102c4db45a1cdb2a37f1ffd16ca2ef97035bcaaf"],"state_sha256":"7e51e0aa424bdb2cc8072559d8db8366f02a3895dc77701a9d701b1f4fc2120a"}