{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:OSY6V3TPKDHSGKTNPZAM32SBPY","short_pith_number":"pith:OSY6V3TP","schema_version":"1.0","canonical_sha256":"74b1eaee6f50cf232a6d7e40cdea417e2d165c69ed2f4bccb3d0b7f1c0d98157","source":{"kind":"arxiv","id":"2310.16931","version":1},"attestation_state":"computed","paper":{"title":"CL-MASR: A Continual Learning Benchmark for Multilingual ASR","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cem Subakan, Luca Della Libera, Mirco Ravanelli, Pooneh Mousavi, Salah Zaiem","submitted_at":"2023-10-25T18:55:40Z","abstract_excerpt":"Modern multilingual automatic speech recognition (ASR) systems like Whisper have made it possible to transcribe audio in multiple languages with a single model. However, current state-of-the-art ASR models are typically evaluated on individual languages or in a multi-task setting, overlooking the challenge of continually learning new languages. There is insufficient research on how to add new languages without losing valuable information from previous data. Furthermore, existing continual learning benchmarks focus mostly on vision and language tasks, leaving continual learning for multilingual"},"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":"2310.16931","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-25T18:55:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"61a357c267d697845e39ea84767a7d0483931f1c81a8042e561cb5c46867f0f1","abstract_canon_sha256":"5ebf74607b63f102c2f89bab17d83b575787782a0891de30cdfe6112fe3a24de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:05:16.168544Z","signature_b64":"Hos0/CVe77M5OSCWF31dvQxY/z0t0T97Ag6nOv+2BlpamPZkjBG+ma2fyurNozHXI1VLnltpSbq2ozMKkt6dCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"74b1eaee6f50cf232a6d7e40cdea417e2d165c69ed2f4bccb3d0b7f1c0d98157","last_reissued_at":"2026-07-05T07:05:16.168048Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:05:16.168048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CL-MASR: A Continual Learning Benchmark for Multilingual ASR","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Cem Subakan, Luca Della Libera, Mirco Ravanelli, Pooneh Mousavi, Salah Zaiem","submitted_at":"2023-10-25T18:55:40Z","abstract_excerpt":"Modern multilingual automatic speech recognition (ASR) systems like Whisper have made it possible to transcribe audio in multiple languages with a single model. However, current state-of-the-art ASR models are typically evaluated on individual languages or in a multi-task setting, overlooking the challenge of continually learning new languages. There is insufficient research on how to add new languages without losing valuable information from previous data. Furthermore, existing continual learning benchmarks focus mostly on vision and language tasks, leaving continual learning for multilingual"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.16931","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/2310.16931/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":"2310.16931","created_at":"2026-07-05T07:05:16.168102+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.16931v1","created_at":"2026-07-05T07:05:16.168102+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.16931","created_at":"2026-07-05T07:05:16.168102+00:00"},{"alias_kind":"pith_short_12","alias_value":"OSY6V3TPKDHS","created_at":"2026-07-05T07:05:16.168102+00:00"},{"alias_kind":"pith_short_16","alias_value":"OSY6V3TPKDHSGKTN","created_at":"2026-07-05T07:05:16.168102+00:00"},{"alias_kind":"pith_short_8","alias_value":"OSY6V3TP","created_at":"2026-07-05T07:05:16.168102+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/OSY6V3TPKDHSGKTNPZAM32SBPY","json":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY.json","graph_json":"https://pith.science/api/pith-number/OSY6V3TPKDHSGKTNPZAM32SBPY/graph.json","events_json":"https://pith.science/api/pith-number/OSY6V3TPKDHSGKTNPZAM32SBPY/events.json","paper":"https://pith.science/paper/OSY6V3TP"},"agent_actions":{"view_html":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY","download_json":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY.json","view_paper":"https://pith.science/paper/OSY6V3TP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.16931&json=true","fetch_graph":"https://pith.science/api/pith-number/OSY6V3TPKDHSGKTNPZAM32SBPY/graph.json","fetch_events":"https://pith.science/api/pith-number/OSY6V3TPKDHSGKTNPZAM32SBPY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY/action/storage_attestation","attest_author":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY/action/author_attestation","sign_citation":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY/action/citation_signature","submit_replication":"https://pith.science/pith/OSY6V3TPKDHSGKTNPZAM32SBPY/action/replication_record"}},"created_at":"2026-07-05T07:05:16.168102+00:00","updated_at":"2026-07-05T07:05:16.168102+00:00"}