{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:YANCWDJFMEH4O7SLMX47JMXM4P","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":"025a971cc786096939257cc3560d8e0a482a4c3987dbed1e4fb89fc3f61db0d6","cross_cats_sorted":["cs.AI","cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-08-22T07:34:07Z","title_canon_sha256":"7daea997e3787c287550a01fa778b94ced98c1df97e78c8e55be420bc5475b84"},"schema_version":"1.0","source":{"id":"2308.11241","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.11241","created_at":"2026-07-05T06:49:25Z"},{"alias_kind":"arxiv_version","alias_value":"2308.11241v2","created_at":"2026-07-05T06:49:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.11241","created_at":"2026-07-05T06:49:25Z"},{"alias_kind":"pith_short_12","alias_value":"YANCWDJFMEH4","created_at":"2026-07-05T06:49:25Z"},{"alias_kind":"pith_short_16","alias_value":"YANCWDJFMEH4O7SL","created_at":"2026-07-05T06:49:25Z"},{"alias_kind":"pith_short_8","alias_value":"YANCWDJF","created_at":"2026-07-05T06:49:25Z"}],"graph_snapshots":[{"event_id":"sha256:26e156a65af7ee2d016d9a695c6666731017e698db91a5517c761cbe4331e405","target":"graph","created_at":"2026-07-05T06:49:25Z","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/2308.11241/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Wav2vec2 has achieved success in applying Transformer architecture and self-supervised learning to speech recognition. Recently, these have come to be used not only for speech recognition but also for the entire speech processing. This paper introduces an effective end-to-end speaker identification model applied Transformer-based contextual model. We explored the relationship between the hyper-parameters and the performance in order to discern the structure of an effective model. Furthermore, we propose a pooling method, Temporal Gate Pooling, with powerful learning ability for speaker identif","authors_text":"Harunori Kawano, Sota Shimizu","cross_cats":["cs.AI","cs.LG","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-08-22T07:34:07Z","title":"An Effective Transformer-based Contextual Model and Temporal Gate Pooling for Speaker Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.11241","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":"sha256:5604ce0d054c2e4cb2dd4c285a0355f1cd86526fe7fbd5b3fc73a83aeb4d72bf","target":"record","created_at":"2026-07-05T06:49:25Z","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":"025a971cc786096939257cc3560d8e0a482a4c3987dbed1e4fb89fc3f61db0d6","cross_cats_sorted":["cs.AI","cs.LG","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-08-22T07:34:07Z","title_canon_sha256":"7daea997e3787c287550a01fa778b94ced98c1df97e78c8e55be420bc5475b84"},"schema_version":"1.0","source":{"id":"2308.11241","kind":"arxiv","version":2}},"canonical_sha256":"c01a2b0d25610fc77e4b65f9f4b2ece3ed28feb81cad5f1b82a85f21c27a38eb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c01a2b0d25610fc77e4b65f9f4b2ece3ed28feb81cad5f1b82a85f21c27a38eb","first_computed_at":"2026-07-05T06:49:25.125686Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:49:25.125686Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PZVkX8M65iPOonYFt6wXnD6RgHr285rjNeDX9Q+8eWHT/83K9Xo1U5Faq3mcJhc1+AXf+emXg8fINVn3z0omBA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:49:25.126154Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.11241","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5604ce0d054c2e4cb2dd4c285a0355f1cd86526fe7fbd5b3fc73a83aeb4d72bf","sha256:26e156a65af7ee2d016d9a695c6666731017e698db91a5517c761cbe4331e405"],"state_sha256":"e33be7d33389ccb1ea0438a06df1307f78b5dec645b7f34686c48554132714c5"}