{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TRXF25KGXYGHH6W4V4KVOBMOXU","short_pith_number":"pith:TRXF25KG","schema_version":"1.0","canonical_sha256":"9c6e5d7546be0c73fadcaf1557058ebd3f4cdd85587ab4700c077dea73d8c164","source":{"kind":"arxiv","id":"2306.06524","version":1},"attestation_state":"computed","paper":{"title":"What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"John H. L. Hansen, Mu Yang, Okim Kang, Ram C. M. C. Shekar","submitted_at":"2023-06-10T21:20:47Z","abstract_excerpt":"This study is focused on understanding and quantifying the change in phoneme and prosody information encoded in the Self-Supervised Learning (SSL) model, brought by an accent identification (AID) fine-tuning task. This problem is addressed based on model probing. Specifically, we conduct a systematic layer-wise analysis of the representations of the Transformer layers on a phoneme correlation task, and a novel word-level prosody prediction task. We compare the probing performance of the pre-trained and fine-tuned SSL models. Results show that the AID fine-tuning task steers the top 2 layers to"},"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":"2306.06524","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-06-10T21:20:47Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"d37a3651041e8f1cd780911400bfa6f4ada3efa66520ada1179d1a8f4a4d2702","abstract_canon_sha256":"bc2f7c265b5cb27be826602bf13d8950a16d13ed62dfae65a08e99f39a678148"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:19:25.883716Z","signature_b64":"gPRNiMPT2D/rjcPwI/iSYXRuLAsYFNZcfSu93MhTrKaw+J1jd0WlnQiwp7cujMo3750mOgp4cziZiVMnxGX6Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c6e5d7546be0c73fadcaf1557058ebd3f4cdd85587ab4700c077dea73d8c164","last_reissued_at":"2026-07-05T06:19:25.883255Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:19:25.883255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"John H. L. Hansen, Mu Yang, Okim Kang, Ram C. M. C. Shekar","submitted_at":"2023-06-10T21:20:47Z","abstract_excerpt":"This study is focused on understanding and quantifying the change in phoneme and prosody information encoded in the Self-Supervised Learning (SSL) model, brought by an accent identification (AID) fine-tuning task. This problem is addressed based on model probing. Specifically, we conduct a systematic layer-wise analysis of the representations of the Transformer layers on a phoneme correlation task, and a novel word-level prosody prediction task. We compare the probing performance of the pre-trained and fine-tuned SSL models. Results show that the AID fine-tuning task steers the top 2 layers to"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.06524","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/2306.06524/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":"2306.06524","created_at":"2026-07-05T06:19:25.883309+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.06524v1","created_at":"2026-07-05T06:19:25.883309+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.06524","created_at":"2026-07-05T06:19:25.883309+00:00"},{"alias_kind":"pith_short_12","alias_value":"TRXF25KGXYGH","created_at":"2026-07-05T06:19:25.883309+00:00"},{"alias_kind":"pith_short_16","alias_value":"TRXF25KGXYGHH6W4","created_at":"2026-07-05T06:19:25.883309+00:00"},{"alias_kind":"pith_short_8","alias_value":"TRXF25KG","created_at":"2026-07-05T06:19:25.883309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01381","citing_title":"A framework for analyzing concept representations in neural models","ref_index":199,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU","json":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU.json","graph_json":"https://pith.science/api/pith-number/TRXF25KGXYGHH6W4V4KVOBMOXU/graph.json","events_json":"https://pith.science/api/pith-number/TRXF25KGXYGHH6W4V4KVOBMOXU/events.json","paper":"https://pith.science/paper/TRXF25KG"},"agent_actions":{"view_html":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU","download_json":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU.json","view_paper":"https://pith.science/paper/TRXF25KG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.06524&json=true","fetch_graph":"https://pith.science/api/pith-number/TRXF25KGXYGHH6W4V4KVOBMOXU/graph.json","fetch_events":"https://pith.science/api/pith-number/TRXF25KGXYGHH6W4V4KVOBMOXU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU/action/storage_attestation","attest_author":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU/action/author_attestation","sign_citation":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU/action/citation_signature","submit_replication":"https://pith.science/pith/TRXF25KGXYGHH6W4V4KVOBMOXU/action/replication_record"}},"created_at":"2026-07-05T06:19:25.883309+00:00","updated_at":"2026-07-05T06:19:25.883309+00:00"}