{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:H3FIRBWXNV3L57VPHOGYQ2CLJA","short_pith_number":"pith:H3FIRBWX","canonical_record":{"source":{"id":"1901.10055","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-22T21:37:07Z","cross_cats_sorted":["cs.CL","cs.LG","cs.SD"],"title_canon_sha256":"25814be4d94b0bb44df68962ae6637d6cf3ce8d456924b7f45542e9fdc116588","abstract_canon_sha256":"5042319643a9795e5276a628986726a615d2f8c003c6f4712b9a475293388196"},"schema_version":"1.0"},"canonical_sha256":"3eca8886d76d76befeaf3b8d88684b482425fc298d002acf75931435bba8b0c8","source":{"kind":"arxiv","id":"1901.10055","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.10055","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"arxiv_version","alias_value":"1901.10055v2","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.10055","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"pith_short_12","alias_value":"H3FIRBWXNV3L","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"H3FIRBWXNV3L57VP","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"H3FIRBWX","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:H3FIRBWXNV3L57VPHOGYQ2CLJA","target":"record","payload":{"canonical_record":{"source":{"id":"1901.10055","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-22T21:37:07Z","cross_cats_sorted":["cs.CL","cs.LG","cs.SD"],"title_canon_sha256":"25814be4d94b0bb44df68962ae6637d6cf3ce8d456924b7f45542e9fdc116588","abstract_canon_sha256":"5042319643a9795e5276a628986726a615d2f8c003c6f4712b9a475293388196"},"schema_version":"1.0"},"canonical_sha256":"3eca8886d76d76befeaf3b8d88684b482425fc298d002acf75931435bba8b0c8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:53.517496Z","signature_b64":"NAsAphmlcm+BouX5ZcR4foOmap39LO0V5NHtNHpAVCebEcvcZToJsnMNn75TcZ/yR7ZBGovz44VJuNgE9Pc8BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3eca8886d76d76befeaf3b8d88684b482425fc298d002acf75931435bba8b0c8","last_reissued_at":"2026-05-17T23:41:53.516839Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:53.516839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.10055","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":"2026-05-17T23:41:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ccsnRm4g4trmzEuKElJu1OkKhEBdld7zZGxSflqlXwm6O5eGMvHB/vs2cmJSmTEBqfSXf59wgLE9cJyEKQF6Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:47:14.672573Z"},"content_sha256":"e345fbafb450a6d1709cf9c534334aa900a83edecc26a4d08150bda307db8158","schema_version":"1.0","event_id":"sha256:e345fbafb450a6d1709cf9c534334aa900a83edecc26a4d08150bda307db8158"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:H3FIRBWXNV3L57VPHOGYQ2CLJA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.SD"],"primary_cat":"eess.AS","authors_text":"Julian Salazar, Katrin Kirchhoff, Zhiheng Huang","submitted_at":"2019-01-22T21:37:07Z","abstract_excerpt":"The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, with character err"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.10055","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":""},"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":"2026-05-17T23:41:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Rw4OrFo8+n0Ub2JBIaxfpmNm9h6vF85qFL5pyGrFnfqZmhDKj2i+XrjBI/OtuIIPWrAK5w36yEPg30GU1UjfAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:47:14.673250Z"},"content_sha256":"4dec8a54c10dd8aaf3f2e3f2d88442fba5d3187d7a634dee66fc9a393a14c00d","schema_version":"1.0","event_id":"sha256:4dec8a54c10dd8aaf3f2e3f2d88442fba5d3187d7a634dee66fc9a393a14c00d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA/bundle.json","state_url":"https://pith.science/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA/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":"2026-06-07T22:47:14Z","links":{"resolver":"https://pith.science/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA","bundle":"https://pith.science/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA/bundle.json","state":"https://pith.science/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/H3FIRBWXNV3L57VPHOGYQ2CLJA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:H3FIRBWXNV3L57VPHOGYQ2CLJA","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":"5042319643a9795e5276a628986726a615d2f8c003c6f4712b9a475293388196","cross_cats_sorted":["cs.CL","cs.LG","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-22T21:37:07Z","title_canon_sha256":"25814be4d94b0bb44df68962ae6637d6cf3ce8d456924b7f45542e9fdc116588"},"schema_version":"1.0","source":{"id":"1901.10055","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.10055","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"arxiv_version","alias_value":"1901.10055v2","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.10055","created_at":"2026-05-17T23:41:53Z"},{"alias_kind":"pith_short_12","alias_value":"H3FIRBWXNV3L","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"H3FIRBWXNV3L57VP","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"H3FIRBWX","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:4dec8a54c10dd8aaf3f2e3f2d88442fba5d3187d7a634dee66fc9a393a14c00d","target":"graph","created_at":"2026-05-17T23:41:53Z","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"},"paper":{"abstract_excerpt":"The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, with character err","authors_text":"Julian Salazar, Katrin Kirchhoff, Zhiheng Huang","cross_cats":["cs.CL","cs.LG","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-22T21:37:07Z","title":"Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.10055","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:e345fbafb450a6d1709cf9c534334aa900a83edecc26a4d08150bda307db8158","target":"record","created_at":"2026-05-17T23:41:53Z","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":"5042319643a9795e5276a628986726a615d2f8c003c6f4712b9a475293388196","cross_cats_sorted":["cs.CL","cs.LG","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-22T21:37:07Z","title_canon_sha256":"25814be4d94b0bb44df68962ae6637d6cf3ce8d456924b7f45542e9fdc116588"},"schema_version":"1.0","source":{"id":"1901.10055","kind":"arxiv","version":2}},"canonical_sha256":"3eca8886d76d76befeaf3b8d88684b482425fc298d002acf75931435bba8b0c8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3eca8886d76d76befeaf3b8d88684b482425fc298d002acf75931435bba8b0c8","first_computed_at":"2026-05-17T23:41:53.516839Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:53.516839Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"NAsAphmlcm+BouX5ZcR4foOmap39LO0V5NHtNHpAVCebEcvcZToJsnMNn75TcZ/yR7ZBGovz44VJuNgE9Pc8BA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:53.517496Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.10055","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e345fbafb450a6d1709cf9c534334aa900a83edecc26a4d08150bda307db8158","sha256:4dec8a54c10dd8aaf3f2e3f2d88442fba5d3187d7a634dee66fc9a393a14c00d"],"state_sha256":"03277861c7d2d0d92bb04a1806636b565d71455a8834b16c8d880e042ed91000"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cKk7cFxIhNDepboDk+jxAJL++SQf0J61BT8XWjoAWtJZWMzrbxiIxuTY3cM00y695A/8EW0Ol8H4Gqzs6jnaBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T22:47:14.676710Z","bundle_sha256":"6f086309eefdd9e000b54fc0662a900ef2b50df9c1ebe0028ec67f2e18b5dba8"}}