{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:VVGFJ4QKTFTSRET37CGZGWB7TB","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":"d2d7070aed323066ad4059a92b96b9868091ed57533c31637e3f5123b79b5870","cross_cats_sorted":["cs.CL","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-11-12T03:50:22Z","title_canon_sha256":"49be051f6c639059c2210509a2672bff1582d9638ef9939747bd29fd451c92ee"},"schema_version":"1.0","source":{"id":"2211.06562","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2211.06562","created_at":"2026-07-05T05:15:26Z"},{"alias_kind":"arxiv_version","alias_value":"2211.06562v1","created_at":"2026-07-05T05:15:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.06562","created_at":"2026-07-05T05:15:26Z"},{"alias_kind":"pith_short_12","alias_value":"VVGFJ4QKTFTS","created_at":"2026-07-05T05:15:26Z"},{"alias_kind":"pith_short_16","alias_value":"VVGFJ4QKTFTSRET3","created_at":"2026-07-05T05:15:26Z"},{"alias_kind":"pith_short_8","alias_value":"VVGFJ4QK","created_at":"2026-07-05T05:15:26Z"}],"graph_snapshots":[{"event_id":"sha256:265c1b8f6461d36b3c7a6bfb635e692d314c31c560441b89ace4b2cdc7c78c4b","target":"graph","created_at":"2026-07-05T05:15:26Z","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/2211.06562/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Self-supervised speech representation learning aims to extract meaningful factors from the speech signal that can later be used across different downstream tasks, such as speech and/or emotion recognition. Existing models, such as HuBERT, however, can be fairly large thus may not be suitable for edge speech applications. Moreover, realistic applications typically involve speech corrupted by noise and room reverberation, hence models need to provide representations that are robust to such environmental factors. In this study, we build on the so-called DistilHuBERT model, which distils HuBERT to","authors_text":"Anderson R. Avila, Arthur Pimentel, Heitor R. Guimar\\~aes, Mehdi Rezagholizadeh, Tiago H. Falk","cross_cats":["cs.CL","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-11-12T03:50:22Z","title":"Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.06562","kind":"arxiv","version":1},"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":"sha256:3339e12a826651417cb3146fbe5975b6934f87413dd02e54cf219b4a837dc0ee","target":"record","created_at":"2026-07-05T05:15:26Z","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":"d2d7070aed323066ad4059a92b96b9868091ed57533c31637e3f5123b79b5870","cross_cats_sorted":["cs.CL","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SD","submitted_at":"2022-11-12T03:50:22Z","title_canon_sha256":"49be051f6c639059c2210509a2672bff1582d9638ef9939747bd29fd451c92ee"},"schema_version":"1.0","source":{"id":"2211.06562","kind":"arxiv","version":1}},"canonical_sha256":"ad4c54f20a996728927bf88d93583f9853583342b4898757a207774165d7ef3a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ad4c54f20a996728927bf88d93583f9853583342b4898757a207774165d7ef3a","first_computed_at":"2026-07-05T05:15:26.373782Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:15:26.373782Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0BAhesQDMqS7rZMnMsoBlARepTxGbPd0Xu1a0aKcGWXizF6GjRGVqKPn0O/ZuteqkNafPrpXu3DBGGCk0MIEBw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:15:26.374231Z","signed_message":"canonical_sha256_bytes"},"source_id":"2211.06562","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3339e12a826651417cb3146fbe5975b6934f87413dd02e54cf219b4a837dc0ee","sha256:265c1b8f6461d36b3c7a6bfb635e692d314c31c560441b89ace4b2cdc7c78c4b"],"state_sha256":"c9e35b910639f08eaeb5ac3f577e80751df7cfd881fbb3c7339e21d54d273bd9"}