{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YPV4JJ663BINTLCOBAL2B6JDFR","short_pith_number":"pith:YPV4JJ66","schema_version":"1.0","canonical_sha256":"c3ebc4a7ded850d9ac4e0817a0f9232c5a33638287315309c464a6b20ba499f7","source":{"kind":"arxiv","id":"2411.04573","version":1},"attestation_state":"computed","paper":{"title":"Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Basil K Raju, Elizabeth Sherly, Kavya Manohar, Leena G Pillai","submitted_at":"2024-11-07T09:57:57Z","abstract_excerpt":"This paper presents a novel multistage fine-tuning strategy designed to enhance automatic speech recognition (ASR) performance in low-resource languages using OpenAI's Whisper model. In this approach we aim to build ASR model for languages with limited digital resources by sequentially adapting the model across linguistically similar languages. We experimented this on the Malasar language, a Dravidian language spoken by approximately ten thousand people in the Western Ghats of South India. Malasar language faces critical challenges for technological intervention due to its lack of a native scr"},"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":"2411.04573","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T09:57:57Z","cross_cats_sorted":["cs.AI","cs.SD","eess.AS"],"title_canon_sha256":"dd13c1e86bcde7156cd7ff3e87427537efb2559e6fd2e2fcc1318dd919089ef2","abstract_canon_sha256":"4c7c0aa43d27c210cc211f207de88338d8e7591e99a5cddb93b6a44179382d6c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:22.279394Z","signature_b64":"9qfwXHfFexj2nKgweB4aXAMhQW+8LLgnbYHEpg4UXbybPSyaFtxM0UtYhiVUXUp1foBzpwEuSsH1HvQWE0BGBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c3ebc4a7ded850d9ac4e0817a0f9232c5a33638287315309c464a6b20ba499f7","last_reissued_at":"2026-07-05T09:32:22.278903Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:22.278903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Basil K Raju, Elizabeth Sherly, Kavya Manohar, Leena G Pillai","submitted_at":"2024-11-07T09:57:57Z","abstract_excerpt":"This paper presents a novel multistage fine-tuning strategy designed to enhance automatic speech recognition (ASR) performance in low-resource languages using OpenAI's Whisper model. In this approach we aim to build ASR model for languages with limited digital resources by sequentially adapting the model across linguistically similar languages. We experimented this on the Malasar language, a Dravidian language spoken by approximately ten thousand people in the Western Ghats of South India. Malasar language faces critical challenges for technological intervention due to its lack of a native scr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04573","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/2411.04573/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":"2411.04573","created_at":"2026-07-05T09:32:22.278961+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.04573v1","created_at":"2026-07-05T09:32:22.278961+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04573","created_at":"2026-07-05T09:32:22.278961+00:00"},{"alias_kind":"pith_short_12","alias_value":"YPV4JJ663BIN","created_at":"2026-07-05T09:32:22.278961+00:00"},{"alias_kind":"pith_short_16","alias_value":"YPV4JJ663BINTLCO","created_at":"2026-07-05T09:32:22.278961+00:00"},{"alias_kind":"pith_short_8","alias_value":"YPV4JJ66","created_at":"2026-07-05T09:32:22.278961+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.06507","citing_title":"Fine-tuning Whisper for Pashto ASR: strategies and scale","ref_index":24,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR","json":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR.json","graph_json":"https://pith.science/api/pith-number/YPV4JJ663BINTLCOBAL2B6JDFR/graph.json","events_json":"https://pith.science/api/pith-number/YPV4JJ663BINTLCOBAL2B6JDFR/events.json","paper":"https://pith.science/paper/YPV4JJ66"},"agent_actions":{"view_html":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR","download_json":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR.json","view_paper":"https://pith.science/paper/YPV4JJ66","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.04573&json=true","fetch_graph":"https://pith.science/api/pith-number/YPV4JJ663BINTLCOBAL2B6JDFR/graph.json","fetch_events":"https://pith.science/api/pith-number/YPV4JJ663BINTLCOBAL2B6JDFR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR/action/storage_attestation","attest_author":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR/action/author_attestation","sign_citation":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR/action/citation_signature","submit_replication":"https://pith.science/pith/YPV4JJ663BINTLCOBAL2B6JDFR/action/replication_record"}},"created_at":"2026-07-05T09:32:22.278961+00:00","updated_at":"2026-07-05T09:32:22.278961+00:00"}