{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:ZXPBZ5DELQKXM56ZZKHKKIIIKW","short_pith_number":"pith:ZXPBZ5DE","schema_version":"1.0","canonical_sha256":"cdde1cf4645c157677d9ca8ea5210855a0266c7d996c8f9bfd62243cb38bf84b","source":{"kind":"arxiv","id":"2606.11429","version":1},"attestation_state":"computed","paper":{"title":"Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Abeer Alwan, Kaiyuan Zhang, Mohan Shi, Natarajan Balaji Shankar, Zilai Wang","submitted_at":"2026-06-09T20:27:59Z","abstract_excerpt":"Speech foundation models often struggle in low-resource domains due to domain mismatch and data scarcity. We propose Gumbel-BEARD, a domain adaptation framework that automates Whisper encoder layer selection via an end-to-end trainable hard Gumbel-Softmax selector. It enables self-supervised adaptation with a BEST-RQ objective that dynamically adapts to target acoustic characteristics without manual tuning. Experiments on the MyST child speech corpus demonstrate efficiency and scalability: with 10 h of labeled data for fine-tuning, our method matches a fully supervised baseline trained on the "},"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":"2606.11429","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2026-06-09T20:27:59Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"669e677f803122a61be55e98fcf9b16239a0a52c2be0d7a549a00d7b5163ff51","abstract_canon_sha256":"fc448ed7c939d8c786044df2e6b9d6faeb595a1120e77e58e1b47a69aad8af2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:09:48.195025Z","signature_b64":"fsSYU9lIjHUER8p5qKzkQrIQN+7QcjqNhThtGDO6H2pgGU1wXCo+NmIIBxI6MYSSQNqHtRICoxdutlq9+DgZCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cdde1cf4645c157677d9ca8ea5210855a0266c7d996c8f9bfd62243cb38bf84b","last_reissued_at":"2026-06-11T01:09:48.194134Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:09:48.194134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gumbel-BEARD: Automatic Layer Selection for Self-Supervised Adaptation of Whisper in Low-Resource Domains","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Abeer Alwan, Kaiyuan Zhang, Mohan Shi, Natarajan Balaji Shankar, Zilai Wang","submitted_at":"2026-06-09T20:27:59Z","abstract_excerpt":"Speech foundation models often struggle in low-resource domains due to domain mismatch and data scarcity. We propose Gumbel-BEARD, a domain adaptation framework that automates Whisper encoder layer selection via an end-to-end trainable hard Gumbel-Softmax selector. It enables self-supervised adaptation with a BEST-RQ objective that dynamically adapts to target acoustic characteristics without manual tuning. Experiments on the MyST child speech corpus demonstrate efficiency and scalability: with 10 h of labeled data for fine-tuning, our method matches a fully supervised baseline trained on the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11429","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/2606.11429/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":"2606.11429","created_at":"2026-06-11T01:09:48.194264+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11429v1","created_at":"2026-06-11T01:09:48.194264+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11429","created_at":"2026-06-11T01:09:48.194264+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXPBZ5DELQKX","created_at":"2026-06-11T01:09:48.194264+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXPBZ5DELQKXM56Z","created_at":"2026-06-11T01:09:48.194264+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXPBZ5DE","created_at":"2026-06-11T01:09:48.194264+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/ZXPBZ5DELQKXM56ZZKHKKIIIKW","json":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW.json","graph_json":"https://pith.science/api/pith-number/ZXPBZ5DELQKXM56ZZKHKKIIIKW/graph.json","events_json":"https://pith.science/api/pith-number/ZXPBZ5DELQKXM56ZZKHKKIIIKW/events.json","paper":"https://pith.science/paper/ZXPBZ5DE"},"agent_actions":{"view_html":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW","download_json":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW.json","view_paper":"https://pith.science/paper/ZXPBZ5DE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11429&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXPBZ5DELQKXM56ZZKHKKIIIKW/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXPBZ5DELQKXM56ZZKHKKIIIKW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW/action/storage_attestation","attest_author":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW/action/author_attestation","sign_citation":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW/action/citation_signature","submit_replication":"https://pith.science/pith/ZXPBZ5DELQKXM56ZZKHKKIIIKW/action/replication_record"}},"created_at":"2026-06-11T01:09:48.194264+00:00","updated_at":"2026-06-11T01:09:48.194264+00:00"}