{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:AUAFGSBMVGL3BI5TIL7W4XW5OT","short_pith_number":"pith:AUAFGSBM","schema_version":"1.0","canonical_sha256":"050053482ca997b0a3b342ff6e5edd74f0ea11eae2473f9226ecc02a25463371","source":{"kind":"arxiv","id":"2506.06888","version":2},"attestation_state":"computed","paper":{"title":"Automatic Speech Recognition of African American English: Lexical and Contextual Effects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Hamid Mojarad, Kevin Tang","submitted_at":"2025-06-07T18:30:59Z","abstract_excerpt":"Automatic Speech Recognition (ASR) models often struggle with the phonetic, phonological, and morphosyntactic features found in African American English (AAE). This study focuses on two key AAE variables: Consonant Cluster Reduction (CCR) and ING-reduction. It examines whether the presence of CCR and ING-reduction increases ASR misrecognition. Subsequently, it investigates whether end-to-end ASR systems without an external Language Model (LM) are more influenced by lexical neighborhood effect and less by contextual predictability compared to systems with an LM. The Corpus of Regional African A"},"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":"2506.06888","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-07T18:30:59Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"21f2fa453e23168615a3b255fc2346c1d7739f402fa4da9ca708ccb4144c17aa","abstract_canon_sha256":"6776cfff62118882050e370e0a99de9f7ccf65029360e33a1c7f8b63998e3193"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:57:56.997547Z","signature_b64":"M8O+BKA0KUGV7cZIzbWbtlt3r2ooRicgatllJ4wsaoJYWPw4t0s8BNTD8087p5ovO7TS9F1Q7XRVT/JL9mCmAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"050053482ca997b0a3b342ff6e5edd74f0ea11eae2473f9226ecc02a25463371","last_reissued_at":"2026-07-05T11:57:56.997072Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:57:56.997072Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Speech Recognition of African American English: Lexical and Contextual Effects","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Hamid Mojarad, Kevin Tang","submitted_at":"2025-06-07T18:30:59Z","abstract_excerpt":"Automatic Speech Recognition (ASR) models often struggle with the phonetic, phonological, and morphosyntactic features found in African American English (AAE). This study focuses on two key AAE variables: Consonant Cluster Reduction (CCR) and ING-reduction. It examines whether the presence of CCR and ING-reduction increases ASR misrecognition. Subsequently, it investigates whether end-to-end ASR systems without an external Language Model (LM) are more influenced by lexical neighborhood effect and less by contextual predictability compared to systems with an LM. The Corpus of Regional African A"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.06888","kind":"arxiv","version":2},"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/2506.06888/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":"2506.06888","created_at":"2026-07-05T11:57:56.997132+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.06888v2","created_at":"2026-07-05T11:57:56.997132+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.06888","created_at":"2026-07-05T11:57:56.997132+00:00"},{"alias_kind":"pith_short_12","alias_value":"AUAFGSBMVGL3","created_at":"2026-07-05T11:57:56.997132+00:00"},{"alias_kind":"pith_short_16","alias_value":"AUAFGSBMVGL3BI5T","created_at":"2026-07-05T11:57:56.997132+00:00"},{"alias_kind":"pith_short_8","alias_value":"AUAFGSBM","created_at":"2026-07-05T11:57:56.997132+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06888","citing_title":"Automatic Speech Recognition of African American English: Lexical and Contextual Effects","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT","json":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT.json","graph_json":"https://pith.science/api/pith-number/AUAFGSBMVGL3BI5TIL7W4XW5OT/graph.json","events_json":"https://pith.science/api/pith-number/AUAFGSBMVGL3BI5TIL7W4XW5OT/events.json","paper":"https://pith.science/paper/AUAFGSBM"},"agent_actions":{"view_html":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT","download_json":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT.json","view_paper":"https://pith.science/paper/AUAFGSBM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.06888&json=true","fetch_graph":"https://pith.science/api/pith-number/AUAFGSBMVGL3BI5TIL7W4XW5OT/graph.json","fetch_events":"https://pith.science/api/pith-number/AUAFGSBMVGL3BI5TIL7W4XW5OT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT/action/storage_attestation","attest_author":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT/action/author_attestation","sign_citation":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT/action/citation_signature","submit_replication":"https://pith.science/pith/AUAFGSBMVGL3BI5TIL7W4XW5OT/action/replication_record"}},"created_at":"2026-07-05T11:57:56.997132+00:00","updated_at":"2026-07-05T11:57:56.997132+00:00"}