{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FADKXDQO7ZKQYFP47XQNVCFAZZ","short_pith_number":"pith:FADKXDQO","schema_version":"1.0","canonical_sha256":"2806ab8e0efe550c15fcfde0da88a0ce73a69e8e26a7b793f636a42acbe71edf","source":{"kind":"arxiv","id":"2607.25284","version":1},"attestation_state":"computed","paper":{"title":"Multi-Phonation Graph Learning with Self-Supervised Speech Embeddings for ALS Detection and Progression Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Behrad TaghiBeyglou, Ervin Sejdic, Fatemeh Bagheri","submitted_at":"2026-07-28T04:42:05Z","abstract_excerpt":"Amyotrophic lateral sclerosis (ALS) progressively impairs speech motor control, making acoustic analysis a promising biomarker for severity and progression estimation. We propose a subject-level graph framework that aggregates multiple phonation recordings into a unique k-nearest-neighbor graph built from pretrained SSL embeddings of 2s segments. We compare four SSL front-ends (wav2vec 2.0, HuBERT, data2vec-audio, and UniSpeech-SAT) and five graph neural networks (GCN, residual GCN, GAT, GraphSAGE, and GIN) on the SAND dataset tasks (339 participants: 205 ALS, 134 control): 5-class dysarthria "},"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":"2607.25284","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-07-28T04:42:05Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"f59c2d624db1aa28f2b1ecd75371908fe478bd2507a2810cca6f1860faed6719","abstract_canon_sha256":"819c2eafa6f045a562007b1208e29cf1c72b209018d8dde3bb6f60b2b24cf986"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:25:05.022020Z","signature_b64":"yQ8Whtg4kz5CjXQjaCU3NLG/1ZP/O+/4ihGkDT2aNXG8b2OM8b1ooRlbqDXFcHt9OeQje6ir+PPP9Zzp2852CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2806ab8e0efe550c15fcfde0da88a0ce73a69e8e26a7b793f636a42acbe71edf","last_reissued_at":"2026-07-29T01:25:05.021065Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:25:05.021065Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Phonation Graph Learning with Self-Supervised Speech Embeddings for ALS Detection and Progression Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Behrad TaghiBeyglou, Ervin Sejdic, Fatemeh Bagheri","submitted_at":"2026-07-28T04:42:05Z","abstract_excerpt":"Amyotrophic lateral sclerosis (ALS) progressively impairs speech motor control, making acoustic analysis a promising biomarker for severity and progression estimation. We propose a subject-level graph framework that aggregates multiple phonation recordings into a unique k-nearest-neighbor graph built from pretrained SSL embeddings of 2s segments. We compare four SSL front-ends (wav2vec 2.0, HuBERT, data2vec-audio, and UniSpeech-SAT) and five graph neural networks (GCN, residual GCN, GAT, GraphSAGE, and GIN) on the SAND dataset tasks (339 participants: 205 ALS, 134 control): 5-class dysarthria "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25284","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/2607.25284/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":"2607.25284","created_at":"2026-07-29T01:25:05.021513+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.25284v1","created_at":"2026-07-29T01:25:05.021513+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25284","created_at":"2026-07-29T01:25:05.021513+00:00"},{"alias_kind":"pith_short_12","alias_value":"FADKXDQO7ZKQ","created_at":"2026-07-29T01:25:05.021513+00:00"},{"alias_kind":"pith_short_16","alias_value":"FADKXDQO7ZKQYFP4","created_at":"2026-07-29T01:25:05.021513+00:00"},{"alias_kind":"pith_short_8","alias_value":"FADKXDQO","created_at":"2026-07-29T01:25:05.021513+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ","json":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ.json","graph_json":"https://pith.science/api/pith-number/FADKXDQO7ZKQYFP47XQNVCFAZZ/graph.json","events_json":"https://pith.science/api/pith-number/FADKXDQO7ZKQYFP47XQNVCFAZZ/events.json","paper":"https://pith.science/paper/FADKXDQO"},"agent_actions":{"view_html":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ","download_json":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ.json","view_paper":"https://pith.science/paper/FADKXDQO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.25284&json=true","fetch_graph":"https://pith.science/api/pith-number/FADKXDQO7ZKQYFP47XQNVCFAZZ/graph.json","fetch_events":"https://pith.science/api/pith-number/FADKXDQO7ZKQYFP47XQNVCFAZZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ/action/storage_attestation","attest_author":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ/action/author_attestation","sign_citation":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ/action/citation_signature","submit_replication":"https://pith.science/pith/FADKXDQO7ZKQYFP47XQNVCFAZZ/action/replication_record"}},"created_at":"2026-07-29T01:25:05.021513+00:00","updated_at":"2026-07-29T01:25:05.021513+00:00"}