{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PTZ2OM4JHB543G7RZKZKTMCAPK","short_pith_number":"pith:PTZ2OM4J","canonical_record":{"source":{"id":"1906.12170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-25T14:52:54Z","cross_cats_sorted":["cs.LG","cs.SD","eess.AS","eess.IV"],"title_canon_sha256":"e9628aae76eb95996292237832b2c22f047ef41595778d787c7930c253f9ff6e","abstract_canon_sha256":"439542619ffcad6787a3886697e5916099aab29008758ddd0cbd2e5c32ade4e1"},"schema_version":"1.0"},"canonical_sha256":"7cf3a73389387bcd9bf1cab2a9b0407aa5c46f842383b6e0814409bf8f7a0499","source":{"kind":"arxiv","id":"1906.12170","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.12170","created_at":"2026-05-17T23:41:59Z"},{"alias_kind":"arxiv_version","alias_value":"1906.12170v1","created_at":"2026-05-17T23:41:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.12170","created_at":"2026-05-17T23:41:59Z"},{"alias_kind":"pith_short_12","alias_value":"PTZ2OM4JHB54","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PTZ2OM4JHB543G7R","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PTZ2OM4J","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PTZ2OM4JHB543G7RZKZKTMCAPK","target":"record","payload":{"canonical_record":{"source":{"id":"1906.12170","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-25T14:52:54Z","cross_cats_sorted":["cs.LG","cs.SD","eess.AS","eess.IV"],"title_canon_sha256":"e9628aae76eb95996292237832b2c22f047ef41595778d787c7930c253f9ff6e","abstract_canon_sha256":"439542619ffcad6787a3886697e5916099aab29008758ddd0cbd2e5c32ade4e1"},"schema_version":"1.0"},"canonical_sha256":"7cf3a73389387bcd9bf1cab2a9b0407aa5c46f842383b6e0814409bf8f7a0499","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:59.097288Z","signature_b64":"FK//9HUo4+fXQeUQ7epRBiNY8Z0tLJQ3obOFLd1Hr0IJKdi0/+vpOsxW4sbj1kAtbOhqBK7S7o5wMdFmXjqjAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7cf3a73389387bcd9bf1cab2a9b0407aa5c46f842383b6e0814409bf8f7a0499","last_reissued_at":"2026-05-17T23:41:59.096903Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:59.096903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.12170","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":"2026-05-17T23:41:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aVXNPpuxvmBbRVbDrhPSOJHU9SfiF40I7xqCzOTgFBvnzwJQ7DFDqNEtbDH6K1F9t5FwwP1x5vdsjyeI4zH4Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T17:26:42.507097Z"},"content_sha256":"7e72cbfde08a3c202cc031c038b626d5e3b176d5670b8e5f65761ebb5b9687be","schema_version":"1.0","event_id":"sha256:7e72cbfde08a3c202cc031c038b626d5e3b176d5670b8e5f65761ebb5b9687be"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PTZ2OM4JHB543G7RZKZKTMCAPK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LipReading with 3D-2D-CNN BLSTM-HMM and word-CTC models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SD","eess.AS","eess.IV"],"primary_cat":"cs.CV","authors_text":"Abhinav Thanda, Dilip Kumar Margam, Pujitha A K, Rohith Aralikatti, Shankar M Venkatesan, Sharad Roy, Tanay Sharma","submitted_at":"2019-06-25T14:52:54Z","abstract_excerpt":"In recent years, deep learning based machine lipreading has gained prominence. To this end, several architectures such as LipNet, LCANet and others have been proposed which perform extremely well compared to traditional lipreading DNN-HMM hybrid systems trained on DCT features. In this work, we propose a simpler architecture of 3D-2D-CNN-BLSTM network with a bottleneck layer. We also present analysis of two different approaches for lipreading on this architecture. In the first approach, 3D-2D-CNN-BLSTM network is trained with CTC loss on characters (ch-CTC). Then BLSTM-HMM model is trained on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.12170","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":""},"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":"2026-05-17T23:41:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QZafNJO8cSNAnn+CbpDkrjJULjXaMzpyMVBPE7uu/xDQRjrv1pjZS66DfHGok7ee1P4K3lhPISghfzC9VpNlCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T17:26:42.507793Z"},"content_sha256":"a93f3946dd656299d3526c068ea8647172c8c10219f2436a55967451ffc994eb","schema_version":"1.0","event_id":"sha256:a93f3946dd656299d3526c068ea8647172c8c10219f2436a55967451ffc994eb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PTZ2OM4JHB543G7RZKZKTMCAPK/bundle.json","state_url":"https://pith.science/pith/PTZ2OM4JHB543G7RZKZKTMCAPK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PTZ2OM4JHB543G7RZKZKTMCAPK/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":"2026-05-26T17:26:42Z","links":{"resolver":"https://pith.science/pith/PTZ2OM4JHB543G7RZKZKTMCAPK","bundle":"https://pith.science/pith/PTZ2OM4JHB543G7RZKZKTMCAPK/bundle.json","state":"https://pith.science/pith/PTZ2OM4JHB543G7RZKZKTMCAPK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PTZ2OM4JHB543G7RZKZKTMCAPK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PTZ2OM4JHB543G7RZKZKTMCAPK","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":"439542619ffcad6787a3886697e5916099aab29008758ddd0cbd2e5c32ade4e1","cross_cats_sorted":["cs.LG","cs.SD","eess.AS","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-25T14:52:54Z","title_canon_sha256":"e9628aae76eb95996292237832b2c22f047ef41595778d787c7930c253f9ff6e"},"schema_version":"1.0","source":{"id":"1906.12170","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.12170","created_at":"2026-05-17T23:41:59Z"},{"alias_kind":"arxiv_version","alias_value":"1906.12170v1","created_at":"2026-05-17T23:41:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.12170","created_at":"2026-05-17T23:41:59Z"},{"alias_kind":"pith_short_12","alias_value":"PTZ2OM4JHB54","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"PTZ2OM4JHB543G7R","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"PTZ2OM4J","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:a93f3946dd656299d3526c068ea8647172c8c10219f2436a55967451ffc994eb","target":"graph","created_at":"2026-05-17T23:41:59Z","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":"In recent years, deep learning based machine lipreading has gained prominence. To this end, several architectures such as LipNet, LCANet and others have been proposed which perform extremely well compared to traditional lipreading DNN-HMM hybrid systems trained on DCT features. In this work, we propose a simpler architecture of 3D-2D-CNN-BLSTM network with a bottleneck layer. We also present analysis of two different approaches for lipreading on this architecture. In the first approach, 3D-2D-CNN-BLSTM network is trained with CTC loss on characters (ch-CTC). Then BLSTM-HMM model is trained on ","authors_text":"Abhinav Thanda, Dilip Kumar Margam, Pujitha A K, Rohith Aralikatti, Shankar M Venkatesan, Sharad Roy, Tanay Sharma","cross_cats":["cs.LG","cs.SD","eess.AS","eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-25T14:52:54Z","title":"LipReading with 3D-2D-CNN BLSTM-HMM and word-CTC models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.12170","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":"sha256:7e72cbfde08a3c202cc031c038b626d5e3b176d5670b8e5f65761ebb5b9687be","target":"record","created_at":"2026-05-17T23:41:59Z","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":"439542619ffcad6787a3886697e5916099aab29008758ddd0cbd2e5c32ade4e1","cross_cats_sorted":["cs.LG","cs.SD","eess.AS","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-25T14:52:54Z","title_canon_sha256":"e9628aae76eb95996292237832b2c22f047ef41595778d787c7930c253f9ff6e"},"schema_version":"1.0","source":{"id":"1906.12170","kind":"arxiv","version":1}},"canonical_sha256":"7cf3a73389387bcd9bf1cab2a9b0407aa5c46f842383b6e0814409bf8f7a0499","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7cf3a73389387bcd9bf1cab2a9b0407aa5c46f842383b6e0814409bf8f7a0499","first_computed_at":"2026-05-17T23:41:59.096903Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:59.096903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FK//9HUo4+fXQeUQ7epRBiNY8Z0tLJQ3obOFLd1Hr0IJKdi0/+vpOsxW4sbj1kAtbOhqBK7S7o5wMdFmXjqjAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:59.097288Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.12170","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7e72cbfde08a3c202cc031c038b626d5e3b176d5670b8e5f65761ebb5b9687be","sha256:a93f3946dd656299d3526c068ea8647172c8c10219f2436a55967451ffc994eb"],"state_sha256":"034393d0f3cb0b4e805f1cc48bb0e30fdb70f830337b7b9bf88dbda1531eb485"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aje/i9olHm9aBPXQf97tVeBgJzEvW6PBw2swjMyqNd4TPzWtYKxej4oGPL2FxmpKBIm7Xn9NzAkxUg4W0X/WDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T17:26:42.511143Z","bundle_sha256":"be38d9a225e1f6d8636e1612bc40115cc94140fbdb08469fee4e612dfb1631f2"}}