{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VFDYG2F3PVCYIKI2PFFAIRFAMT","short_pith_number":"pith:VFDYG2F3","schema_version":"1.0","canonical_sha256":"a9478368bb7d4584291a794a0444a064ec1969f9b91b3fe3eccdb73c7c1b300e","source":{"kind":"arxiv","id":"2406.02562","version":1},"attestation_state":"computed","paper":{"title":"Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"eess.AS","authors_text":"Bokyeung Lee, Donghyeon Kim, Gwantae Kim, Hanseok Ko","submitted_at":"2024-04-24T01:31:39Z","abstract_excerpt":"In recent times, there has been a growing interest in utilizing personalized large models on low-spec devices, such as mobile and CPU-only devices. However, utilizing a personalized large model in the on-device is inefficient, and sometimes limited due to computational cost. To tackle the problem, this paper presents the weights separation method to minimize on-device model weights using parameter-efficient fine-tuning methods. Moreover, some people speak multiple languages in an utterance, as known as code-switching, the personalized ASR model is necessary to address such cases. However, curr"},"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":"2406.02562","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.AS","submitted_at":"2024-04-24T01:31:39Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"15ac9594524a3e0aff3d653fdd1a34c221a801014cf80721f2d5f882773c8610","abstract_canon_sha256":"7a95cf572555056a7ac26efbb81ef157ed6e69a35f7ec3edc01acf3e1682f3be"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:27:26.999515Z","signature_b64":"iGL4Il0dIwtrRjHuB4YV4aHyJ23IIIq/c+HvitDj3ubeKYCAt0VAHurFfXU+fmaXw4DKFGZDEmPS0PJPYLbZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a9478368bb7d4584291a794a0444a064ec1969f9b91b3fe3eccdb73c7c1b300e","last_reissued_at":"2026-07-05T08:27:26.998983Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:27:26.998983Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Gated Low-rank Adaptation for personalized Code-Switching Automatic Speech Recognition on the low-spec devices","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"eess.AS","authors_text":"Bokyeung Lee, Donghyeon Kim, Gwantae Kim, Hanseok Ko","submitted_at":"2024-04-24T01:31:39Z","abstract_excerpt":"In recent times, there has been a growing interest in utilizing personalized large models on low-spec devices, such as mobile and CPU-only devices. However, utilizing a personalized large model in the on-device is inefficient, and sometimes limited due to computational cost. To tackle the problem, this paper presents the weights separation method to minimize on-device model weights using parameter-efficient fine-tuning methods. Moreover, some people speak multiple languages in an utterance, as known as code-switching, the personalized ASR model is necessary to address such cases. However, curr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.02562","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/2406.02562/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":"2406.02562","created_at":"2026-07-05T08:27:26.999037+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.02562v1","created_at":"2026-07-05T08:27:26.999037+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.02562","created_at":"2026-07-05T08:27:26.999037+00:00"},{"alias_kind":"pith_short_12","alias_value":"VFDYG2F3PVCY","created_at":"2026-07-05T08:27:26.999037+00:00"},{"alias_kind":"pith_short_16","alias_value":"VFDYG2F3PVCYIKI2","created_at":"2026-07-05T08:27:26.999037+00:00"},{"alias_kind":"pith_short_8","alias_value":"VFDYG2F3","created_at":"2026-07-05T08:27:26.999037+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/VFDYG2F3PVCYIKI2PFFAIRFAMT","json":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT.json","graph_json":"https://pith.science/api/pith-number/VFDYG2F3PVCYIKI2PFFAIRFAMT/graph.json","events_json":"https://pith.science/api/pith-number/VFDYG2F3PVCYIKI2PFFAIRFAMT/events.json","paper":"https://pith.science/paper/VFDYG2F3"},"agent_actions":{"view_html":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT","download_json":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT.json","view_paper":"https://pith.science/paper/VFDYG2F3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.02562&json=true","fetch_graph":"https://pith.science/api/pith-number/VFDYG2F3PVCYIKI2PFFAIRFAMT/graph.json","fetch_events":"https://pith.science/api/pith-number/VFDYG2F3PVCYIKI2PFFAIRFAMT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT/action/storage_attestation","attest_author":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT/action/author_attestation","sign_citation":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT/action/citation_signature","submit_replication":"https://pith.science/pith/VFDYG2F3PVCYIKI2PFFAIRFAMT/action/replication_record"}},"created_at":"2026-07-05T08:27:26.999037+00:00","updated_at":"2026-07-05T08:27:26.999037+00:00"}