{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:6UWVQN7MCEYFMSTNFJRJNFNLBY","short_pith_number":"pith:6UWVQN7M","schema_version":"1.0","canonical_sha256":"f52d5837ec1130564a6d2a629695ab0e393716f548de6ac5c2d6a54697c2ced0","source":{"kind":"arxiv","id":"2405.03952","version":1},"attestation_state":"computed","paper":{"title":"HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bj\\\"orn W. Schuller, Jianjun Ou, Jing Han, Weixiang Xu, Zhongren Dong, Zixing Zhang","submitted_at":"2024-05-07T02:19:16Z","abstract_excerpt":"Automatically detecting Alzheimer's Disease (AD) from spontaneous speech plays an important role in its early diagnosis. Recent approaches highly rely on the Transformer architectures due to its efficiency in modelling long-range context dependencies. However, the quadratic increase in computational complexity associated with self-attention and the length of audio poses a challenge when deploying such models on edge devices. In this context, we construct a novel framework, namely Hierarchical Attention-Free Transformer (HAFFormer), to better deal with long speech for AD detection. Specifically"},"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":"2405.03952","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2024-05-07T02:19:16Z","cross_cats_sorted":["cs.CL","eess.AS"],"title_canon_sha256":"361a7f9de3d28281619fd7a5c162af9743314d5963e78b0adec4002e2115a8b1","abstract_canon_sha256":"32b5ce92cb9e81865582240bccf538d92bf6415a2a9412c11a01d1e31bc59278"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:16:29.582411Z","signature_b64":"M1Ygzoc2y/Wrc23TrDrhrtQQ0CoA8NNouIxnsH4G+e7QecByiLUmSDOdYvaZO1/gfmbRlyHpI3ViVTvpYPabBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f52d5837ec1130564a6d2a629695ab0e393716f548de6ac5c2d6a54697c2ced0","last_reissued_at":"2026-07-05T08:16:29.581875Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:16:29.581875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HAFFormer: A Hierarchical Attention-Free Framework for Alzheimer's Disease Detection From Spontaneous Speech","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","eess.AS"],"primary_cat":"cs.SD","authors_text":"Bj\\\"orn W. Schuller, Jianjun Ou, Jing Han, Weixiang Xu, Zhongren Dong, Zixing Zhang","submitted_at":"2024-05-07T02:19:16Z","abstract_excerpt":"Automatically detecting Alzheimer's Disease (AD) from spontaneous speech plays an important role in its early diagnosis. Recent approaches highly rely on the Transformer architectures due to its efficiency in modelling long-range context dependencies. However, the quadratic increase in computational complexity associated with self-attention and the length of audio poses a challenge when deploying such models on edge devices. In this context, we construct a novel framework, namely Hierarchical Attention-Free Transformer (HAFFormer), to better deal with long speech for AD detection. Specifically"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.03952","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/2405.03952/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":"2405.03952","created_at":"2026-07-05T08:16:29.581955+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.03952v1","created_at":"2026-07-05T08:16:29.581955+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.03952","created_at":"2026-07-05T08:16:29.581955+00:00"},{"alias_kind":"pith_short_12","alias_value":"6UWVQN7MCEYF","created_at":"2026-07-05T08:16:29.581955+00:00"},{"alias_kind":"pith_short_16","alias_value":"6UWVQN7MCEYFMSTN","created_at":"2026-07-05T08:16:29.581955+00:00"},{"alias_kind":"pith_short_8","alias_value":"6UWVQN7M","created_at":"2026-07-05T08:16:29.581955+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/6UWVQN7MCEYFMSTNFJRJNFNLBY","json":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY.json","graph_json":"https://pith.science/api/pith-number/6UWVQN7MCEYFMSTNFJRJNFNLBY/graph.json","events_json":"https://pith.science/api/pith-number/6UWVQN7MCEYFMSTNFJRJNFNLBY/events.json","paper":"https://pith.science/paper/6UWVQN7M"},"agent_actions":{"view_html":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY","download_json":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY.json","view_paper":"https://pith.science/paper/6UWVQN7M","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.03952&json=true","fetch_graph":"https://pith.science/api/pith-number/6UWVQN7MCEYFMSTNFJRJNFNLBY/graph.json","fetch_events":"https://pith.science/api/pith-number/6UWVQN7MCEYFMSTNFJRJNFNLBY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY/action/storage_attestation","attest_author":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY/action/author_attestation","sign_citation":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY/action/citation_signature","submit_replication":"https://pith.science/pith/6UWVQN7MCEYFMSTNFJRJNFNLBY/action/replication_record"}},"created_at":"2026-07-05T08:16:29.581955+00:00","updated_at":"2026-07-05T08:16:29.581955+00:00"}