{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:KFUAPNN4I7P65Q4NMKQ5HSNXPI","short_pith_number":"pith:KFUAPNN4","schema_version":"1.0","canonical_sha256":"516807b5bc47dfeec38d62a1d3c9b77a0230218e84b90c1976c61e8ebeb9beb1","source":{"kind":"arxiv","id":"2211.07430","version":1},"attestation_state":"computed","paper":{"title":"The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG","q-bio.QM"],"primary_cat":"eess.AS","authors_text":"Changye Li, Serguei Pakhomov, Trevor Cohen","submitted_at":"2022-11-11T17:06:45Z","abstract_excerpt":"Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment. The feasibility of deploying automated tools that can classify language samples obtained from speech in large-scale clinical settings depends on the ability to capture and automatically transcribe the speech for subsequent analysis. However, the impressive performance of self-supervised learning (SSL) automatic speech recognition (ASR) models with curated speech data is not apparent with challenging speech samples f"},"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":"2211.07430","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.AS","submitted_at":"2022-11-11T17:06:45Z","cross_cats_sorted":["cs.AI","cs.CL","cs.LG","q-bio.QM"],"title_canon_sha256":"8d1f0c9fc396ff24f615336c701fc1c7c856f421f2db645d87f99e6313d54751","abstract_canon_sha256":"7426932300f5ec63cc270d6aa2db2a5d0a6e2f17409c4a3d030f5d89938693ae"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:15:53.585627Z","signature_b64":"qVzY3iWdnN9RFgQTJ+sBOpr2xlwy3OSW1iwVxuZcCtp4t2N+035rcB1CjYAt9kZnV32bTvYIQ1cENziErtdtAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"516807b5bc47dfeec38d62a1d3c9b77a0230218e84b90c1976c61e8ebeb9beb1","last_reissued_at":"2026-07-05T05:15:53.585106Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:15:53.585106Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Far Side of Failure: Investigating the Impact of Speech Recognition Errors on Subsequent Dementia Classification","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.LG","q-bio.QM"],"primary_cat":"eess.AS","authors_text":"Changye Li, Serguei Pakhomov, Trevor Cohen","submitted_at":"2022-11-11T17:06:45Z","abstract_excerpt":"Linguistic anomalies detectable in spontaneous speech have shown promise for various clinical applications including screening for dementia and other forms of cognitive impairment. The feasibility of deploying automated tools that can classify language samples obtained from speech in large-scale clinical settings depends on the ability to capture and automatically transcribe the speech for subsequent analysis. However, the impressive performance of self-supervised learning (SSL) automatic speech recognition (ASR) models with curated speech data is not apparent with challenging speech samples f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.07430","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/2211.07430/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":"2211.07430","created_at":"2026-07-05T05:15:53.585176+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.07430v1","created_at":"2026-07-05T05:15:53.585176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.07430","created_at":"2026-07-05T05:15:53.585176+00:00"},{"alias_kind":"pith_short_12","alias_value":"KFUAPNN4I7P6","created_at":"2026-07-05T05:15:53.585176+00:00"},{"alias_kind":"pith_short_16","alias_value":"KFUAPNN4I7P65Q4N","created_at":"2026-07-05T05:15:53.585176+00:00"},{"alias_kind":"pith_short_8","alias_value":"KFUAPNN4","created_at":"2026-07-05T05:15:53.585176+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.06332","citing_title":"Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI","json":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI.json","graph_json":"https://pith.science/api/pith-number/KFUAPNN4I7P65Q4NMKQ5HSNXPI/graph.json","events_json":"https://pith.science/api/pith-number/KFUAPNN4I7P65Q4NMKQ5HSNXPI/events.json","paper":"https://pith.science/paper/KFUAPNN4"},"agent_actions":{"view_html":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI","download_json":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI.json","view_paper":"https://pith.science/paper/KFUAPNN4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.07430&json=true","fetch_graph":"https://pith.science/api/pith-number/KFUAPNN4I7P65Q4NMKQ5HSNXPI/graph.json","fetch_events":"https://pith.science/api/pith-number/KFUAPNN4I7P65Q4NMKQ5HSNXPI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI/action/storage_attestation","attest_author":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI/action/author_attestation","sign_citation":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI/action/citation_signature","submit_replication":"https://pith.science/pith/KFUAPNN4I7P65Q4NMKQ5HSNXPI/action/replication_record"}},"created_at":"2026-07-05T05:15:53.585176+00:00","updated_at":"2026-07-05T05:15:53.585176+00:00"}