{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:A57MPW6UKSJDWOCOLO3E4QA6GZ","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":"6615c28ae9a97245d0a5baeb0396f87bdff99d401c71d019d6014e90123ac87d","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-01-27T22:20:28Z","title_canon_sha256":"d42f538fd20a575de85ae741a8173c0071f88282692bfbf6e4586584245f39bb"},"schema_version":"1.0","source":{"id":"2201.11826","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.11826","created_at":"2026-07-05T03:52:17Z"},{"alias_kind":"arxiv_version","alias_value":"2201.11826v1","created_at":"2026-07-05T03:52:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.11826","created_at":"2026-07-05T03:52:17Z"},{"alias_kind":"pith_short_12","alias_value":"A57MPW6UKSJD","created_at":"2026-07-05T03:52:17Z"},{"alias_kind":"pith_short_16","alias_value":"A57MPW6UKSJDWOCO","created_at":"2026-07-05T03:52:17Z"},{"alias_kind":"pith_short_8","alias_value":"A57MPW6U","created_at":"2026-07-05T03:52:17Z"}],"graph_snapshots":[{"event_id":"sha256:9881da900cd641d323c730ffdf0123dab40ab8053ccb13a857d30f3843fa1298","target":"graph","created_at":"2026-07-05T03:52:17Z","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/2201.11826/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We propose a novel multi-task pre-training method for Speech Emotion Recognition (SER). We pre-train SER model simultaneously on Automatic Speech Recognition (ASR) and sentiment classification tasks to make the acoustic ASR model more ``emotion aware''. We generate targets for the sentiment classification using text-to-sentiment model trained on publicly available data. Finally, we fine-tune the acoustic ASR on emotion annotated speech data. We evaluated the proposed approach on the MSP-Podcast dataset, where we achieved the best reported concordance correlation coefficient (CCC) of 0.41 for v","authors_text":"Ayoub Ghriss, Bo Yang, Chao Wang, Elizabeth Shriberg, Viktor Rozgic","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-01-27T22:20:28Z","title":"Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.11826","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":"sha256:8176209ec48a221a2817eb90dc9178179a8ae7f9b4df4a722559549639b8e35b","target":"record","created_at":"2026-07-05T03:52:17Z","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":"6615c28ae9a97245d0a5baeb0396f87bdff99d401c71d019d6014e90123ac87d","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-01-27T22:20:28Z","title_canon_sha256":"d42f538fd20a575de85ae741a8173c0071f88282692bfbf6e4586584245f39bb"},"schema_version":"1.0","source":{"id":"2201.11826","kind":"arxiv","version":1}},"canonical_sha256":"077ec7dbd454923b384e5bb64e401e3678a77e283f1bb3e843439ac16b59a0ab","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"077ec7dbd454923b384e5bb64e401e3678a77e283f1bb3e843439ac16b59a0ab","first_computed_at":"2026-07-05T03:52:17.153109Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:52:17.153109Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fKvdtiSkI7fVdgdcNcVhF73Cn9aloSPYcDDlWn7nW7t7NkTNQ+0X5RGBtjdqvpNocUPDauCkA0/08itr2hCWDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:52:17.153555Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.11826","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8176209ec48a221a2817eb90dc9178179a8ae7f9b4df4a722559549639b8e35b","sha256:9881da900cd641d323c730ffdf0123dab40ab8053ccb13a857d30f3843fa1298"],"state_sha256":"b2f01c4c85b10b7eaac92a67045ac02bcbee91602308d4551acd7ca9232f448c"}