{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BICU3GPBYM2ZHH74KQNULTSP72","short_pith_number":"pith:BICU3GPB","schema_version":"1.0","canonical_sha256":"0a054d99e1c335939ffc541b45ce4ffea5b615d61421c3c5bfdb977e360dbe80","source":{"kind":"arxiv","id":"2210.06175","version":3},"attestation_state":"computed","paper":{"title":"Exploring Efficient-tuning Methods in Self-supervised Speech Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Chih-Ying Liu, Chin-Lun Fu, Hung-yi Lee, Shang-Wen Li, Zih-Ching Chen","submitted_at":"2022-10-10T11:08:12Z","abstract_excerpt":"In this study, we aim to explore efficient tuning methods for speech self-supervised learning. Recent studies show that self-supervised learning (SSL) can learn powerful representations for different speech tasks. However, fine-tuning pre-trained models for each downstream task is parameter-inefficient since SSL models are notoriously large with millions of parameters. Adapters are lightweight modules commonly used in NLP to solve this problem. In downstream tasks, the parameters of SSL models are frozen, and only the adapters are trained. Given the lack of studies generally exploring the effe"},"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":"2210.06175","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-10-10T11:08:12Z","cross_cats_sorted":["cs.LG","cs.SD"],"title_canon_sha256":"5a3ddc9949bede53a2632f957ca37cf5280a10e5ed6dc3ae588c25d25c0e62f4","abstract_canon_sha256":"c63a942ec480f1e6488deb9d050cf55da61195595695ecf09f3b3954678bcf4b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:36:50.612570Z","signature_b64":"am0buZNvpMFiJHOLNuZPsD+RtJtk4ZjGEpOdatw2yhl3DjWP0vfJJXClAIqF6wEhkeSfyxpf9o8hDjjrH7YjDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0a054d99e1c335939ffc541b45ce4ffea5b615d61421c3c5bfdb977e360dbe80","last_reissued_at":"2026-07-05T05:36:50.612019Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:36:50.612019Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring Efficient-tuning Methods in Self-supervised Speech Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Chih-Ying Liu, Chin-Lun Fu, Hung-yi Lee, Shang-Wen Li, Zih-Ching Chen","submitted_at":"2022-10-10T11:08:12Z","abstract_excerpt":"In this study, we aim to explore efficient tuning methods for speech self-supervised learning. Recent studies show that self-supervised learning (SSL) can learn powerful representations for different speech tasks. However, fine-tuning pre-trained models for each downstream task is parameter-inefficient since SSL models are notoriously large with millions of parameters. Adapters are lightweight modules commonly used in NLP to solve this problem. In downstream tasks, the parameters of SSL models are frozen, and only the adapters are trained. Given the lack of studies generally exploring the effe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.06175","kind":"arxiv","version":3},"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/2210.06175/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":"2210.06175","created_at":"2026-07-05T05:36:50.612076+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.06175v3","created_at":"2026-07-05T05:36:50.612076+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.06175","created_at":"2026-07-05T05:36:50.612076+00:00"},{"alias_kind":"pith_short_12","alias_value":"BICU3GPBYM2Z","created_at":"2026-07-05T05:36:50.612076+00:00"},{"alias_kind":"pith_short_16","alias_value":"BICU3GPBYM2ZHH74","created_at":"2026-07-05T05:36:50.612076+00:00"},{"alias_kind":"pith_short_8","alias_value":"BICU3GPB","created_at":"2026-07-05T05:36:50.612076+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.26618","citing_title":"Closing the Quality Gap in Low-Resource Text-to-Speech: LoRA Fine-Tuning of VoxCPM2 for Khmer and Korean","ref_index":19,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72","json":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72.json","graph_json":"https://pith.science/api/pith-number/BICU3GPBYM2ZHH74KQNULTSP72/graph.json","events_json":"https://pith.science/api/pith-number/BICU3GPBYM2ZHH74KQNULTSP72/events.json","paper":"https://pith.science/paper/BICU3GPB"},"agent_actions":{"view_html":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72","download_json":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72.json","view_paper":"https://pith.science/paper/BICU3GPB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.06175&json=true","fetch_graph":"https://pith.science/api/pith-number/BICU3GPBYM2ZHH74KQNULTSP72/graph.json","fetch_events":"https://pith.science/api/pith-number/BICU3GPBYM2ZHH74KQNULTSP72/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72/action/storage_attestation","attest_author":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72/action/author_attestation","sign_citation":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72/action/citation_signature","submit_replication":"https://pith.science/pith/BICU3GPBYM2ZHH74KQNULTSP72/action/replication_record"}},"created_at":"2026-07-05T05:36:50.612076+00:00","updated_at":"2026-07-05T05:36:50.612076+00:00"}