{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:PHUMJ4IDPDEQTALMLJEAK7BPUO","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":"00c0df630f4fd103442851f69351983911eed073775858e79d56e0ec353ba18a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-09T00:59:43Z","title_canon_sha256":"8df28e36331d51736e662337e0b0d5f405822ea6822eb12470d19bbdc2a90c45"},"schema_version":"1.0","source":{"id":"2606.10278","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.10278","created_at":"2026-06-10T01:09:04Z"},{"alias_kind":"arxiv_version","alias_value":"2606.10278v1","created_at":"2026-06-10T01:09:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.10278","created_at":"2026-06-10T01:09:04Z"},{"alias_kind":"pith_short_12","alias_value":"PHUMJ4IDPDEQ","created_at":"2026-06-10T01:09:04Z"},{"alias_kind":"pith_short_16","alias_value":"PHUMJ4IDPDEQTALM","created_at":"2026-06-10T01:09:04Z"},{"alias_kind":"pith_short_8","alias_value":"PHUMJ4ID","created_at":"2026-06-10T01:09:04Z"}],"graph_snapshots":[{"event_id":"sha256:ef936a2288ee3dc62ae16703eb4045af2e9c1e920aea05af977d7b02b86803d5","target":"graph","created_at":"2026-06-10T01:09:04Z","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/2606.10278/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Speech Emotion Recognition (SER) aims to identify a speaker's emotional state from audio signals. While recent advances in deep learning have significantly improved SER performance in Indo-European languages, Arabic SER remains underexplored and challenging due to dialectal diversity, limited annotated datasets, and the difficulty of modeling both local spectral cues and long-range temporal dependencies.\n  To address these limitations, this study investigates whether hybrid architectures that jointly model spatial and contextual information can improve emotion recognition in Arabic speech. We ","authors_text":"Samiya Silarbi, Youcef Soufiane Gheffari","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-09T00:59:43Z","title":"Towards Robust Arabic Speech Emotion Recognition with Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.10278","kind":"arxiv","version":1},"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":"sha256:0a3363b4925a8b300c018a23a7477bd727e6558c6e8293ecf9360a6b73871802","target":"record","created_at":"2026-06-10T01:09:04Z","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":"00c0df630f4fd103442851f69351983911eed073775858e79d56e0ec353ba18a","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-09T00:59:43Z","title_canon_sha256":"8df28e36331d51736e662337e0b0d5f405822ea6822eb12470d19bbdc2a90c45"},"schema_version":"1.0","source":{"id":"2606.10278","kind":"arxiv","version":1}},"canonical_sha256":"79e8c4f10378c909816c5a48057c2fa3ba3868d753b6dfd2643f7a9b820af0c9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"79e8c4f10378c909816c5a48057c2fa3ba3868d753b6dfd2643f7a9b820af0c9","first_computed_at":"2026-06-10T01:09:04.149063Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-10T01:09:04.149063Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BLo3QZWBJzuhyf2neHKOeRz+HISxqfmzMQa2xvKnTzKxE3zKbqfAYF0/9P8yXvASpXoGHsMHOyjyJ2L4O48VBQ==","signature_status":"signed_v1","signed_at":"2026-06-10T01:09:04.149899Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.10278","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a3363b4925a8b300c018a23a7477bd727e6558c6e8293ecf9360a6b73871802","sha256:ef936a2288ee3dc62ae16703eb4045af2e9c1e920aea05af977d7b02b86803d5"],"state_sha256":"f859924d5599c5ae903ae840d1796180754d926ca437524318ffd66c6a88426a"}