{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SJ3FU2L3MLE4H7XHRYCWCB2WIH","short_pith_number":"pith:SJ3FU2L3","canonical_record":{"source":{"id":"2506.00422","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-31T06:53:25Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"d132a80bdc3c8f84454dc96c7e385c2ee9b24d1b9028b3ca3ba2eb3384c0f09b","abstract_canon_sha256":"4aa65f32d66b07de46c61a1ccae7023b3fad5184fe15f61011e0e793069f1817"},"schema_version":"1.0"},"canonical_sha256":"92765a697b62c9c3fee78e0561075641d887cd592c089abc99500f5dbc2ad593","source":{"kind":"arxiv","id":"2506.00422","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00422","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00422v1","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00422","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"pith_short_12","alias_value":"SJ3FU2L3MLE4","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"pith_short_16","alias_value":"SJ3FU2L3MLE4H7XH","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"pith_short_8","alias_value":"SJ3FU2L3","created_at":"2026-07-05T11:14:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SJ3FU2L3MLE4H7XHRYCWCB2WIH","target":"record","payload":{"canonical_record":{"source":{"id":"2506.00422","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-31T06:53:25Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"d132a80bdc3c8f84454dc96c7e385c2ee9b24d1b9028b3ca3ba2eb3384c0f09b","abstract_canon_sha256":"4aa65f32d66b07de46c61a1ccae7023b3fad5184fe15f61011e0e793069f1817"},"schema_version":"1.0"},"canonical_sha256":"92765a697b62c9c3fee78e0561075641d887cd592c089abc99500f5dbc2ad593","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:29.820253Z","signature_b64":"G7/fIWFMeSM67irBmlXetFyJz7zsHrjBxxDWibOhLe7Sj+KIQU1QMPKi0BSulMDVEP9Ty9u0NS/wi5wfyasABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92765a697b62c9c3fee78e0561075641d887cd592c089abc99500f5dbc2ad593","last_reissued_at":"2026-07-05T11:14:29.819755Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:29.819755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.00422","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:14:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TIlByOg/jp5nUq09mOGdmvmd/LzuXctYxcN539CsLNcXOsukbcV0O08gsTG0W38UZk2ylu3OzVpOZqzgXNs+DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:48:54.607482Z"},"content_sha256":"96c0b6cb7cb693a1d85602fa17eb75429440c6168f6700ae85686d92faff204d","schema_version":"1.0","event_id":"sha256:96c0b6cb7cb693a1d85602fa17eb75429440c6168f6700ae85686d92faff204d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SJ3FU2L3MLE4H7XHRYCWCB2WIH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DYNAC: Dynamic Vocabulary based Non-Autoregressive Contextualization for Speech Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Chyi-Jiunn Lin, Muhammad Shakeel, Shinji Watanabe, Yifan Peng, Yosuke Fukumoto, Yui Sudo","submitted_at":"2025-05-31T06:53:25Z","abstract_excerpt":"Contextual biasing (CB) improves automatic speech recognition for rare and unseen phrases. Recent studies have introduced dynamic vocabulary, which represents context phrases as expandable tokens in autoregressive (AR) models. This method improves CB accuracy but with slow inference speed. While dynamic vocabulary can be applied to non-autoregressive (NAR) models, such as connectionist temporal classification (CTC), the conditional independence assumption fails to capture dependencies between static and dynamic tokens. This paper proposes DYNAC (Dynamic Vocabulary-based NAR Contextualization),"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00422","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/2506.00422/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":"2026-07-05T11:14:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W7hhu4oujdJvnrs4vETsX8EDjKHSmpUO2LWl8LFAfOWNFDSPWigFdDgtnmNlK1fFwqPMQrO1TaiS0Q4HRSxdCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T23:48:54.607986Z"},"content_sha256":"3b83dbe7029d77bbd3ef90a03b7a077c8212253c7b2aaae9fdb2353a6cd702c0","schema_version":"1.0","event_id":"sha256:3b83dbe7029d77bbd3ef90a03b7a077c8212253c7b2aaae9fdb2353a6cd702c0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH/bundle.json","state_url":"https://pith.science/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH/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":"2026-08-07T23:48:54Z","links":{"resolver":"https://pith.science/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH","bundle":"https://pith.science/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH/bundle.json","state":"https://pith.science/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJ3FU2L3MLE4H7XHRYCWCB2WIH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SJ3FU2L3MLE4H7XHRYCWCB2WIH","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":"4aa65f32d66b07de46c61a1ccae7023b3fad5184fe15f61011e0e793069f1817","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-31T06:53:25Z","title_canon_sha256":"d132a80bdc3c8f84454dc96c7e385c2ee9b24d1b9028b3ca3ba2eb3384c0f09b"},"schema_version":"1.0","source":{"id":"2506.00422","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.00422","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"arxiv_version","alias_value":"2506.00422v1","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.00422","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"pith_short_12","alias_value":"SJ3FU2L3MLE4","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"pith_short_16","alias_value":"SJ3FU2L3MLE4H7XH","created_at":"2026-07-05T11:14:29Z"},{"alias_kind":"pith_short_8","alias_value":"SJ3FU2L3","created_at":"2026-07-05T11:14:29Z"}],"graph_snapshots":[{"event_id":"sha256:3b83dbe7029d77bbd3ef90a03b7a077c8212253c7b2aaae9fdb2353a6cd702c0","target":"graph","created_at":"2026-07-05T11:14:29Z","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/2506.00422/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Contextual biasing (CB) improves automatic speech recognition for rare and unseen phrases. Recent studies have introduced dynamic vocabulary, which represents context phrases as expandable tokens in autoregressive (AR) models. This method improves CB accuracy but with slow inference speed. While dynamic vocabulary can be applied to non-autoregressive (NAR) models, such as connectionist temporal classification (CTC), the conditional independence assumption fails to capture dependencies between static and dynamic tokens. This paper proposes DYNAC (Dynamic Vocabulary-based NAR Contextualization),","authors_text":"Chyi-Jiunn Lin, Muhammad Shakeel, Shinji Watanabe, Yifan Peng, Yosuke Fukumoto, Yui Sudo","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-31T06:53:25Z","title":"DYNAC: Dynamic Vocabulary based Non-Autoregressive Contextualization for Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.00422","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":"sha256:96c0b6cb7cb693a1d85602fa17eb75429440c6168f6700ae85686d92faff204d","target":"record","created_at":"2026-07-05T11:14:29Z","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":"4aa65f32d66b07de46c61a1ccae7023b3fad5184fe15f61011e0e793069f1817","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-31T06:53:25Z","title_canon_sha256":"d132a80bdc3c8f84454dc96c7e385c2ee9b24d1b9028b3ca3ba2eb3384c0f09b"},"schema_version":"1.0","source":{"id":"2506.00422","kind":"arxiv","version":1}},"canonical_sha256":"92765a697b62c9c3fee78e0561075641d887cd592c089abc99500f5dbc2ad593","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"92765a697b62c9c3fee78e0561075641d887cd592c089abc99500f5dbc2ad593","first_computed_at":"2026-07-05T11:14:29.819755Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:14:29.819755Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"G7/fIWFMeSM67irBmlXetFyJz7zsHrjBxxDWibOhLe7Sj+KIQU1QMPKi0BSulMDVEP9Ty9u0NS/wi5wfyasABw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:14:29.820253Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.00422","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96c0b6cb7cb693a1d85602fa17eb75429440c6168f6700ae85686d92faff204d","sha256:3b83dbe7029d77bbd3ef90a03b7a077c8212253c7b2aaae9fdb2353a6cd702c0"],"state_sha256":"645b804a3d6434c4e8c2738c6c0dd9c8960fe328bf2c764f3e015e79b99b7e53"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZoUo93cqADk0QETQ7wxb0FOCz8irMLQlUvqZh57xJAsf/EU/wldD2F5GJFXxKIPKLGMO6j0jAe0KyHp1GTeyBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T23:48:54.611915Z","bundle_sha256":"312498a105a2c6934e1752c64dd6302f00e7a7f39e380c816847884fee0cec14"}}