{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UEPANL7IOA5Z7IXAKUXXIPDNX3","short_pith_number":"pith:UEPANL7I","schema_version":"1.0","canonical_sha256":"a11e06afe8703b9fa2e0552f743c6dbed5cba84ffdf96b263cae9f901562f7b0","source":{"kind":"arxiv","id":"2307.11584","version":1},"attestation_state":"computed","paper":{"title":"A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Ali Satvaty, Hossein Sameti, Zeinab Sadat Taghavi","submitted_at":"2023-07-21T13:48:11Z","abstract_excerpt":"Speech Emotion Recognition (SER) is a challenging task. In this paper, we introduce a modality conversion concept aimed at enhancing emotion recognition performance on the MELD dataset. We assess our approach through two experiments: first, a method named Modality-Conversion that employs automatic speech recognition (ASR) systems, followed by a text classifier; second, we assume perfect ASR output and investigate the impact of modality conversion on SER, this method is called Modality-Conversion++. Our findings indicate that the first method yields substantial results, while the second method "},"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":"2307.11584","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-07-21T13:48:11Z","cross_cats_sorted":["cs.CL","cs.LG","eess.AS"],"title_canon_sha256":"630d827076879ef056f0e65283daf601a1c6872661c4c5cc3647a5d2ce03efe5","abstract_canon_sha256":"d3015ce299a8fc88977ad8ca1d66266a1bd83e34de20b0824e6de397ebd00f30"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:33:29.688361Z","signature_b64":"jPHefnt+t7A7y9bTLWoRk46YCg+GaR8U+mf0RLlKb3JDijnwVUiBXP0jaKxfcBCyPpi1bC6pyp9x1PChYXSTAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a11e06afe8703b9fa2e0552f743c6dbed5cba84ffdf96b263cae9f901562f7b0","last_reissued_at":"2026-07-05T06:33:29.687957Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:33:29.687957Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality Conversion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","eess.AS"],"primary_cat":"cs.SD","authors_text":"Ali Satvaty, Hossein Sameti, Zeinab Sadat Taghavi","submitted_at":"2023-07-21T13:48:11Z","abstract_excerpt":"Speech Emotion Recognition (SER) is a challenging task. In this paper, we introduce a modality conversion concept aimed at enhancing emotion recognition performance on the MELD dataset. We assess our approach through two experiments: first, a method named Modality-Conversion that employs automatic speech recognition (ASR) systems, followed by a text classifier; second, we assume perfect ASR output and investigate the impact of modality conversion on SER, this method is called Modality-Conversion++. Our findings indicate that the first method yields substantial results, while the second method "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.11584","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/2307.11584/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":"2307.11584","created_at":"2026-07-05T06:33:29.688013+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.11584v1","created_at":"2026-07-05T06:33:29.688013+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.11584","created_at":"2026-07-05T06:33:29.688013+00:00"},{"alias_kind":"pith_short_12","alias_value":"UEPANL7IOA5Z","created_at":"2026-07-05T06:33:29.688013+00:00"},{"alias_kind":"pith_short_16","alias_value":"UEPANL7IOA5Z7IXA","created_at":"2026-07-05T06:33:29.688013+00:00"},{"alias_kind":"pith_short_8","alias_value":"UEPANL7I","created_at":"2026-07-05T06:33:29.688013+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.16971","citing_title":"RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples","ref_index":97,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3","json":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3.json","graph_json":"https://pith.science/api/pith-number/UEPANL7IOA5Z7IXAKUXXIPDNX3/graph.json","events_json":"https://pith.science/api/pith-number/UEPANL7IOA5Z7IXAKUXXIPDNX3/events.json","paper":"https://pith.science/paper/UEPANL7I"},"agent_actions":{"view_html":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3","download_json":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3.json","view_paper":"https://pith.science/paper/UEPANL7I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.11584&json=true","fetch_graph":"https://pith.science/api/pith-number/UEPANL7IOA5Z7IXAKUXXIPDNX3/graph.json","fetch_events":"https://pith.science/api/pith-number/UEPANL7IOA5Z7IXAKUXXIPDNX3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3/action/storage_attestation","attest_author":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3/action/author_attestation","sign_citation":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3/action/citation_signature","submit_replication":"https://pith.science/pith/UEPANL7IOA5Z7IXAKUXXIPDNX3/action/replication_record"}},"created_at":"2026-07-05T06:33:29.688013+00:00","updated_at":"2026-07-05T06:33:29.688013+00:00"}