{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JMRMPIK6BE5W356RKTIWRNHUSD","short_pith_number":"pith:JMRMPIK6","schema_version":"1.0","canonical_sha256":"4b22c7a15e093b6df7d154d168b4f490fe01c1da56d5ba52f7983e76074bd95e","source":{"kind":"arxiv","id":"2409.10704","version":1},"attestation_state":"computed","paper":{"title":"Self-supervised Speech Models for Word-Level Stuttered Speech Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Alexandros G. Dimakis, David Harwath, Yi-Jen Shih, Zoi Gkalitsiou","submitted_at":"2024-09-16T20:18:20Z","abstract_excerpt":"Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and experience in stuttering and fluency disorders. Unfortunately, only a small percentage of speech-language pathologists report being comfortable working with individuals who stutter, which is inadequate to accommodate for the 80 million individuals who stutter worldwide. Developing machine learning models for detecting stuttered speech would enable universal and automated screening for stuttering, enabling speech pat"},"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":"2409.10704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-09-16T20:18:20Z","cross_cats_sorted":["cs.AI","cs.CL","cs.SD"],"title_canon_sha256":"28763b4ec93cbcfd89a9b8fcc5ee3ad19032ffd3843dc3500ef9a1d663613d18","abstract_canon_sha256":"e35c7ced8dc0f0a381826e7b51e6b18e13010953eebc31809fa86c599b39271c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:54.946806Z","signature_b64":"IVCuH94eU4UDLr/N97rc2GJWTzxXS2MaQ3nDxSTDFIR01cODtYSTt5hpxLA4AoJ7Si3aLykI+ANs5coUs+T5Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4b22c7a15e093b6df7d154d168b4f490fe01c1da56d5ba52f7983e76074bd95e","last_reissued_at":"2026-07-05T09:07:54.946372Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:54.946372Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-supervised Speech Models for Word-Level Stuttered Speech Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Alexandros G. Dimakis, David Harwath, Yi-Jen Shih, Zoi Gkalitsiou","submitted_at":"2024-09-16T20:18:20Z","abstract_excerpt":"Clinical diagnosis of stuttering requires an assessment by a licensed speech-language pathologist. However, this process is time-consuming and requires clinicians with training and experience in stuttering and fluency disorders. Unfortunately, only a small percentage of speech-language pathologists report being comfortable working with individuals who stutter, which is inadequate to accommodate for the 80 million individuals who stutter worldwide. Developing machine learning models for detecting stuttered speech would enable universal and automated screening for stuttering, enabling speech pat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10704","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/2409.10704/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":"2409.10704","created_at":"2026-07-05T09:07:54.946454+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.10704v1","created_at":"2026-07-05T09:07:54.946454+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10704","created_at":"2026-07-05T09:07:54.946454+00:00"},{"alias_kind":"pith_short_12","alias_value":"JMRMPIK6BE5W","created_at":"2026-07-05T09:07:54.946454+00:00"},{"alias_kind":"pith_short_16","alias_value":"JMRMPIK6BE5W356R","created_at":"2026-07-05T09:07:54.946454+00:00"},{"alias_kind":"pith_short_8","alias_value":"JMRMPIK6","created_at":"2026-07-05T09:07:54.946454+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.03559","citing_title":"Comprehensive Layer-wise Analysis of SSL Models for Audio Deepfake Detection","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD","json":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD.json","graph_json":"https://pith.science/api/pith-number/JMRMPIK6BE5W356RKTIWRNHUSD/graph.json","events_json":"https://pith.science/api/pith-number/JMRMPIK6BE5W356RKTIWRNHUSD/events.json","paper":"https://pith.science/paper/JMRMPIK6"},"agent_actions":{"view_html":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD","download_json":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD.json","view_paper":"https://pith.science/paper/JMRMPIK6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.10704&json=true","fetch_graph":"https://pith.science/api/pith-number/JMRMPIK6BE5W356RKTIWRNHUSD/graph.json","fetch_events":"https://pith.science/api/pith-number/JMRMPIK6BE5W356RKTIWRNHUSD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD/action/storage_attestation","attest_author":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD/action/author_attestation","sign_citation":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD/action/citation_signature","submit_replication":"https://pith.science/pith/JMRMPIK6BE5W356RKTIWRNHUSD/action/replication_record"}},"created_at":"2026-07-05T09:07:54.946454+00:00","updated_at":"2026-07-05T09:07:54.946454+00:00"}