{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:DU2SASNM2UJREX23CHGYDZNZAR","short_pith_number":"pith:DU2SASNM","canonical_record":{"source":{"id":"2506.00981","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T12:25:13Z","cross_cats_sorted":["cs.AI","cs.SD","eess.AS"],"title_canon_sha256":"62ce8cf8d9d101cfef6d28a45934eda1e235ec968fc14e2f4d1a7a188f570016","abstract_canon_sha256":"416f5a9b5b8d36430f12ba2de1d0876a9206f681c7f820dbce266254fe5883c8"},"schema_version":"1.0"},"canonical_sha256":"1d352049acd513125f5b11cd81e5b9045450f57bbf21bf44684ef35f6cea8c02","source":{"kind":"arxiv","id":"2506.00981","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00981","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00981v2","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00981","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"pith_short_12","alias_value":"DU2SASNM2UJR","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"pith_short_16","alias_value":"DU2SASNM2UJREX23","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"pith_short_8","alias_value":"DU2SASNM","created_at":"2026-07-05T11:34:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:DU2SASNM2UJREX23CHGYDZNZAR","target":"record","payload":{"canonical_record":{"source":{"id":"2506.00981","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T12:25:13Z","cross_cats_sorted":["cs.AI","cs.SD","eess.AS"],"title_canon_sha256":"62ce8cf8d9d101cfef6d28a45934eda1e235ec968fc14e2f4d1a7a188f570016","abstract_canon_sha256":"416f5a9b5b8d36430f12ba2de1d0876a9206f681c7f820dbce266254fe5883c8"},"schema_version":"1.0"},"canonical_sha256":"1d352049acd513125f5b11cd81e5b9045450f57bbf21bf44684ef35f6cea8c02","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:51.781701Z","signature_b64":"r7568Zubi2OsnAcOfnuqm1e3P1GrEjivl+Bo0InKyDB4W3KGlVvzFYc3NoeNKMJKeL7Gjd9wGZbPqqu9eNfFBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1d352049acd513125f5b11cd81e5b9045450f57bbf21bf44684ef35f6cea8c02","last_reissued_at":"2026-07-05T11:34:51.781226Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:51.781226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.00981","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:34:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MbP1RRAjIeWUOZBcl/PA2Mz/801C5bPwH/XVXOtNGyW/ZwFh1ujoXgEQawJbSTJVDXfJgZ9tc6v7jAUJADt+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:31:41.258423Z"},"content_sha256":"f8bf35a889b74ba40ed9e6b96b8a8815fb79402ba98170a7031e75febef23d85","schema_version":"1.0","event_id":"sha256:f8bf35a889b74ba40ed9e6b96b8a8815fb79402ba98170a7031e75febef23d85"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:DU2SASNM2UJREX23CHGYDZNZAR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Charlotte Pouw, Gaofei Shen, Hosein Mohebbi, Marianne de Heer Kloots, Martijn Bentum, Willem Zuidema","submitted_at":"2025-06-01T12:25:13Z","abstract_excerpt":"How language-specific are speech representations learned by self-supervised models? Existing work has shown that a range of linguistic features can be successfully decoded from end-to-end models trained only on speech recordings. However, it's less clear to what extent pre-training on specific languages improves language-specific linguistic information. Here we test the encoding of Dutch phonetic and lexical information in internal representations of self-supervised Wav2Vec2 models. Pre-training exclusively on Dutch improves the representation of Dutch linguistic features as compared to pre-tr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00981","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/2506.00981/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:34:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dOqkbTeRlPXu50Cz67ZuEUs2fzAJzNulnfYTbPKVCisFUKomdHvNHwMDCuvfo9AJoauScyJgk5/796ZzCxD2BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:31:41.259341Z"},"content_sha256":"c5996c9ebca91869f546fa240756fb5d4477bfce2cbebd9c973084cf1fda8338","schema_version":"1.0","event_id":"sha256:c5996c9ebca91869f546fa240756fb5d4477bfce2cbebd9c973084cf1fda8338"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DU2SASNM2UJREX23CHGYDZNZAR/bundle.json","state_url":"https://pith.science/pith/DU2SASNM2UJREX23CHGYDZNZAR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DU2SASNM2UJREX23CHGYDZNZAR/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T05:31:41Z","links":{"resolver":"https://pith.science/pith/DU2SASNM2UJREX23CHGYDZNZAR","bundle":"https://pith.science/pith/DU2SASNM2UJREX23CHGYDZNZAR/bundle.json","state":"https://pith.science/pith/DU2SASNM2UJREX23CHGYDZNZAR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DU2SASNM2UJREX23CHGYDZNZAR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:DU2SASNM2UJREX23CHGYDZNZAR","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":"416f5a9b5b8d36430f12ba2de1d0876a9206f681c7f820dbce266254fe5883c8","cross_cats_sorted":["cs.AI","cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T12:25:13Z","title_canon_sha256":"62ce8cf8d9d101cfef6d28a45934eda1e235ec968fc14e2f4d1a7a188f570016"},"schema_version":"1.0","source":{"id":"2506.00981","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00981","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00981v2","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00981","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"pith_short_12","alias_value":"DU2SASNM2UJR","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"pith_short_16","alias_value":"DU2SASNM2UJREX23","created_at":"2026-07-05T11:34:51Z"},{"alias_kind":"pith_short_8","alias_value":"DU2SASNM","created_at":"2026-07-05T11:34:51Z"}],"graph_snapshots":[{"event_id":"sha256:c5996c9ebca91869f546fa240756fb5d4477bfce2cbebd9c973084cf1fda8338","target":"graph","created_at":"2026-07-05T11:34:51Z","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/2506.00981/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"How language-specific are speech representations learned by self-supervised models? Existing work has shown that a range of linguistic features can be successfully decoded from end-to-end models trained only on speech recordings. However, it's less clear to what extent pre-training on specific languages improves language-specific linguistic information. Here we test the encoding of Dutch phonetic and lexical information in internal representations of self-supervised Wav2Vec2 models. Pre-training exclusively on Dutch improves the representation of Dutch linguistic features as compared to pre-tr","authors_text":"Charlotte Pouw, Gaofei Shen, Hosein Mohebbi, Marianne de Heer Kloots, Martijn Bentum, Willem Zuidema","cross_cats":["cs.AI","cs.SD","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T12:25:13Z","title":"What do self-supervised speech models know about Dutch? Analyzing advantages of language-specific pre-training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00981","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:f8bf35a889b74ba40ed9e6b96b8a8815fb79402ba98170a7031e75febef23d85","target":"record","created_at":"2026-07-05T11:34:51Z","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":"416f5a9b5b8d36430f12ba2de1d0876a9206f681c7f820dbce266254fe5883c8","cross_cats_sorted":["cs.AI","cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-01T12:25:13Z","title_canon_sha256":"62ce8cf8d9d101cfef6d28a45934eda1e235ec968fc14e2f4d1a7a188f570016"},"schema_version":"1.0","source":{"id":"2506.00981","kind":"arxiv","version":2}},"canonical_sha256":"1d352049acd513125f5b11cd81e5b9045450f57bbf21bf44684ef35f6cea8c02","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1d352049acd513125f5b11cd81e5b9045450f57bbf21bf44684ef35f6cea8c02","first_computed_at":"2026-07-05T11:34:51.781226Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:34:51.781226Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r7568Zubi2OsnAcOfnuqm1e3P1GrEjivl+Bo0InKyDB4W3KGlVvzFYc3NoeNKMJKeL7Gjd9wGZbPqqu9eNfFBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T11:34:51.781701Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.00981","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f8bf35a889b74ba40ed9e6b96b8a8815fb79402ba98170a7031e75febef23d85","sha256:c5996c9ebca91869f546fa240756fb5d4477bfce2cbebd9c973084cf1fda8338"],"state_sha256":"6190c4afbbbdb52b25379e7896481eef2b2e3e63a5f21fd1b909cdd396c9c611"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Sicpn4j+IKFCHmseZ6pCNj2LK2zjjTBVFEOdH6dI7t/eiV4PjD2oXStvT6dpqzpD4xb71J3Iz2E76LGgHbCVDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:31:41.265081Z","bundle_sha256":"07c2941b29ba5ca1627c0d164cb8c3c937fbf539e172309be97bbd3c25276803"}}