{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:MIPSKECID75IQYJ6M3RZTZQR4A","short_pith_number":"pith:MIPSKECI","schema_version":"1.0","canonical_sha256":"621f2510481ffa88613e66e399e611e01d3ed03217e45dab41e16d7f60f1972f","source":{"kind":"arxiv","id":"2008.06665","version":1},"attestation_state":"computed","paper":{"title":"EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"P. C. Ching, Shuiyang Mao, Tan Lee","submitted_at":"2020-08-15T07:00:11Z","abstract_excerpt":"Human emotional speech is, by its very nature, a variant signal. This results in dynamics intrinsic to automatic emotion classification based on speech. In this work, we explore a spectral decomposition method stemming from fluid-dynamics, known as Dynamic Mode Decomposition (DMD), to computationally represent and analyze the global utterance-level dynamics of emotional speech. Specifically, segment-level emotion-specific representations are first learned through an Emotion Distillation process. This forms a multi-dimensional signal of emotion flow for each utterance, called Emotion Profiles ("},"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":"2008.06665","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2020-08-15T07:00:11Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"736858d152bddb083f796a6a20de7a1a404673e57317ad6abe45cf19efa30c3b","abstract_canon_sha256":"d11916038ec9623055a06f38d96beb5ad2adc494c62ba555a7fc6a202e1e7365"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:27:30.732683Z","signature_b64":"9BtOApH2p10FN28bx4UKyjXqxSaah2/AYgKMzKnVEsJ2yBL71UwCSFJrPi4lnWEcupYy39YAH4dLKhx5C7EzDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"621f2510481ffa88613e66e399e611e01d3ed03217e45dab41e16d7f60f1972f","last_reissued_at":"2026-07-05T01:27:30.732161Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:27:30.732161Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"P. C. Ching, Shuiyang Mao, Tan Lee","submitted_at":"2020-08-15T07:00:11Z","abstract_excerpt":"Human emotional speech is, by its very nature, a variant signal. This results in dynamics intrinsic to automatic emotion classification based on speech. In this work, we explore a spectral decomposition method stemming from fluid-dynamics, known as Dynamic Mode Decomposition (DMD), to computationally represent and analyze the global utterance-level dynamics of emotional speech. Specifically, segment-level emotion-specific representations are first learned through an Emotion Distillation process. This forms a multi-dimensional signal of emotion flow for each utterance, called Emotion Profiles ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.06665","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/2008.06665/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":"2008.06665","created_at":"2026-07-05T01:27:30.732230+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.06665v1","created_at":"2026-07-05T01:27:30.732230+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.06665","created_at":"2026-07-05T01:27:30.732230+00:00"},{"alias_kind":"pith_short_12","alias_value":"MIPSKECID75I","created_at":"2026-07-05T01:27:30.732230+00:00"},{"alias_kind":"pith_short_16","alias_value":"MIPSKECID75IQYJ6","created_at":"2026-07-05T01:27:30.732230+00:00"},{"alias_kind":"pith_short_8","alias_value":"MIPSKECI","created_at":"2026-07-05T01:27:30.732230+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.13048","citing_title":"DMDIntel: Interpreting Large Language Models via Dynamic Mode Decomposition","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A","json":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A.json","graph_json":"https://pith.science/api/pith-number/MIPSKECID75IQYJ6M3RZTZQR4A/graph.json","events_json":"https://pith.science/api/pith-number/MIPSKECID75IQYJ6M3RZTZQR4A/events.json","paper":"https://pith.science/paper/MIPSKECI"},"agent_actions":{"view_html":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A","download_json":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A.json","view_paper":"https://pith.science/paper/MIPSKECI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.06665&json=true","fetch_graph":"https://pith.science/api/pith-number/MIPSKECID75IQYJ6M3RZTZQR4A/graph.json","fetch_events":"https://pith.science/api/pith-number/MIPSKECID75IQYJ6M3RZTZQR4A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A/action/storage_attestation","attest_author":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A/action/author_attestation","sign_citation":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A/action/citation_signature","submit_replication":"https://pith.science/pith/MIPSKECID75IQYJ6M3RZTZQR4A/action/replication_record"}},"created_at":"2026-07-05T01:27:30.732230+00:00","updated_at":"2026-07-05T01:27:30.732230+00:00"}