{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:HA542PIAPRNA2ARMVDRS3NQKH7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"308d7f6743ce3660f1dfcca1f6af1c307dcc7f25f15c53a52ed04d3ea7cc7a14","cross_cats_sorted":["cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-09-16T16:04:16Z","title_canon_sha256":"00ec75acdfbbd71c2844ef479470dfd7f5df5ec5f3e1e005a691b5c9ccd5643e"},"schema_version":"1.0","source":{"id":"2409.10429","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.10429","created_at":"2026-07-05T11:21:35Z"},{"alias_kind":"arxiv_version","alias_value":"2409.10429v2","created_at":"2026-07-05T11:21:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.10429","created_at":"2026-07-05T11:21:35Z"},{"alias_kind":"pith_short_12","alias_value":"HA542PIAPRNA","created_at":"2026-07-05T11:21:35Z"},{"alias_kind":"pith_short_16","alias_value":"HA542PIAPRNA2ARM","created_at":"2026-07-05T11:21:35Z"},{"alias_kind":"pith_short_8","alias_value":"HA542PIA","created_at":"2026-07-05T11:21:35Z"}],"graph_snapshots":[{"event_id":"sha256:6c17cfed43b96c4c93bf553f403e23bdc73ce1e8db5c798fc453df18a23eb6a5","target":"graph","created_at":"2026-07-05T11:21:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2409.10429/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Automatic Speech Recognition (ASR) models demonstrate outstanding performance on high-resource languages but face significant challenges when applied to low-resource languages due to limited training data and insufficient cross-lingual generalization. Existing adaptation strategies, such as shallow fusion, data augmentation, and direct fine-tuning, either rely on external resources, suffer computational inefficiencies, or fail in test-time adaptation scenarios. To address these limitations, we introduce Speech Meta In-Context LEarning (SMILE), an innovative framework that combines meta-learnin","authors_text":"Hung-yi Lee, Ming-Hao Hsu","cross_cats":["cs.CL","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-09-16T16:04:16Z","title":"SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.10429","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ee46b06d04c6eab3e299b5b130f70325ab33952ab57b2fdc8bd5543b784c54fc","target":"record","created_at":"2026-07-05T11:21:35Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"308d7f6743ce3660f1dfcca1f6af1c307dcc7f25f15c53a52ed04d3ea7cc7a14","cross_cats_sorted":["cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2024-09-16T16:04:16Z","title_canon_sha256":"00ec75acdfbbd71c2844ef479470dfd7f5df5ec5f3e1e005a691b5c9ccd5643e"},"schema_version":"1.0","source":{"id":"2409.10429","kind":"arxiv","version":2}},"canonical_sha256":"383bcd3d007c5a0d022ca8e32db60a3fce55967ba991ab8b082b6b73d32ecde7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"383bcd3d007c5a0d022ca8e32db60a3fce55967ba991ab8b082b6b73d32ecde7","first_computed_at":"2026-07-05T11:21:35.833238Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:21:35.833238Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gEdkcHuHqf+soUteVhSYVSUsJy4APIhL7iYeaG83wx4/fdJKIFVkMeH1XD2heeX3B8jRPwfy0yx6wJyC7vETBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:21:35.833802Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.10429","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee46b06d04c6eab3e299b5b130f70325ab33952ab57b2fdc8bd5543b784c54fc","sha256:6c17cfed43b96c4c93bf553f403e23bdc73ce1e8db5c798fc453df18a23eb6a5"],"state_sha256":"675282dd2172e3486571a6bcc3c135ca6527f93fb7f81c7e96e7d656115746d4"}