{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:FZU5HAJQ52MO4UIVB5VYBL7D7W","short_pith_number":"pith:FZU5HAJQ","canonical_record":{"source":{"id":"2107.10708","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-07-22T14:29:21Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"c76e08e41f0f6bb8211e0a5583ec4d49aab6177824a6f058235635ed993d2b46","abstract_canon_sha256":"273af324fabde3d3caba40b85e60982168317b255d4b7cd09b85c472c9561563"},"schema_version":"1.0"},"canonical_sha256":"2e69d38130ee98ee51150f6b80afe3fd87d009de2b5f506e8a1a90f1d5443e79","source":{"kind":"arxiv","id":"2107.10708","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10708","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10708v1","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10708","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"pith_short_12","alias_value":"FZU5HAJQ52MO","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"pith_short_16","alias_value":"FZU5HAJQ52MO4UIV","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"pith_short_8","alias_value":"FZU5HAJQ","created_at":"2026-07-05T03:00:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:FZU5HAJQ52MO4UIVB5VYBL7D7W","target":"record","payload":{"canonical_record":{"source":{"id":"2107.10708","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-07-22T14:29:21Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"c76e08e41f0f6bb8211e0a5583ec4d49aab6177824a6f058235635ed993d2b46","abstract_canon_sha256":"273af324fabde3d3caba40b85e60982168317b255d4b7cd09b85c472c9561563"},"schema_version":"1.0"},"canonical_sha256":"2e69d38130ee98ee51150f6b80afe3fd87d009de2b5f506e8a1a90f1d5443e79","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:02.898830Z","signature_b64":"UvJCouUQlBsues9nQp9sMKxq2WR9w6q14/N2MHUUwKiiVArZLMVxwAlvusq0XVYa2wKDSFX4fEmP0tdedxIlAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2e69d38130ee98ee51150f6b80afe3fd87d009de2b5f506e8a1a90f1d5443e79","last_reissued_at":"2026-07-05T03:00:02.898399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:02.898399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.10708","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":"2026-07-05T03:00:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ZYxVSGhOn8cU0Hx8rEzieHvHAYfoGnOCyRpaLvOcbRKUw2gh3q8girPE947NlEgDmtSWMEb1QycpGbvCusYAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T09:13:18.834657Z"},"content_sha256":"1565f03e540c697ab42151fafbcca4acf63543eba78b4d8891a37a568ffa0b2b","schema_version":"1.0","event_id":"sha256:1565f03e540c697ab42151fafbcca4acf63543eba78b4d8891a37a568ffa0b2b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:FZU5HAJQ52MO4UIVB5VYBL7D7W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CarneliNet: Neural Mixture Model for Automatic Speech Recognition","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Aleksei Kalinov, Boris Ginsburg, Jagadeesh Balam, Somshubra Majumdar","submitted_at":"2021-07-22T14:29:21Z","abstract_excerpt":"End-to-end automatic speech recognition systems have achieved great accuracy by using deeper and deeper models. However, the increased depth comes with a larger receptive field that can negatively impact model performance in streaming scenarios. We propose an alternative approach that we call Neural Mixture Model. The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network. To validate this idea we design CarneliNet -- a CTC-based neural network composed of three mega-blocks. Each mega-block consists of multiple parallel shallow sub-networks based on 1D"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10708","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/2107.10708/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":"2026-07-05T03:00:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ql4W9JFcRDSlz9Du07VDG7P52wNb6HHLNcEp7yEwscQeAE1zsaL3/5oLauJgfA6xYfriSGgxfFwY2E2MO5VcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T09:13:18.835024Z"},"content_sha256":"b3334ce7494d3f07f6629f16f4b71790ac1816b6ffd83dcd1d1285692d7c281d","schema_version":"1.0","event_id":"sha256:b3334ce7494d3f07f6629f16f4b71790ac1816b6ffd83dcd1d1285692d7c281d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W/bundle.json","state_url":"https://pith.science/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W/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":"2026-07-06T09:13:18Z","links":{"resolver":"https://pith.science/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W","bundle":"https://pith.science/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W/bundle.json","state":"https://pith.science/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FZU5HAJQ52MO4UIVB5VYBL7D7W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:FZU5HAJQ52MO4UIVB5VYBL7D7W","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":"273af324fabde3d3caba40b85e60982168317b255d4b7cd09b85c472c9561563","cross_cats_sorted":["cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-07-22T14:29:21Z","title_canon_sha256":"c76e08e41f0f6bb8211e0a5583ec4d49aab6177824a6f058235635ed993d2b46"},"schema_version":"1.0","source":{"id":"2107.10708","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.10708","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"arxiv_version","alias_value":"2107.10708v1","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.10708","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"pith_short_12","alias_value":"FZU5HAJQ52MO","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"pith_short_16","alias_value":"FZU5HAJQ52MO4UIV","created_at":"2026-07-05T03:00:02Z"},{"alias_kind":"pith_short_8","alias_value":"FZU5HAJQ","created_at":"2026-07-05T03:00:02Z"}],"graph_snapshots":[{"event_id":"sha256:b3334ce7494d3f07f6629f16f4b71790ac1816b6ffd83dcd1d1285692d7c281d","target":"graph","created_at":"2026-07-05T03:00:02Z","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/2107.10708/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"End-to-end automatic speech recognition systems have achieved great accuracy by using deeper and deeper models. However, the increased depth comes with a larger receptive field that can negatively impact model performance in streaming scenarios. We propose an alternative approach that we call Neural Mixture Model. The basic idea is to introduce a parallel mixture of shallow networks instead of a very deep network. To validate this idea we design CarneliNet -- a CTC-based neural network composed of three mega-blocks. Each mega-block consists of multiple parallel shallow sub-networks based on 1D","authors_text":"Aleksei Kalinov, Boris Ginsburg, Jagadeesh Balam, Somshubra Majumdar","cross_cats":["cs.SD"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-07-22T14:29:21Z","title":"CarneliNet: Neural Mixture Model for Automatic Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.10708","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":"sha256:1565f03e540c697ab42151fafbcca4acf63543eba78b4d8891a37a568ffa0b2b","target":"record","created_at":"2026-07-05T03:00:02Z","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":"273af324fabde3d3caba40b85e60982168317b255d4b7cd09b85c472c9561563","cross_cats_sorted":["cs.SD"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2021-07-22T14:29:21Z","title_canon_sha256":"c76e08e41f0f6bb8211e0a5583ec4d49aab6177824a6f058235635ed993d2b46"},"schema_version":"1.0","source":{"id":"2107.10708","kind":"arxiv","version":1}},"canonical_sha256":"2e69d38130ee98ee51150f6b80afe3fd87d009de2b5f506e8a1a90f1d5443e79","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2e69d38130ee98ee51150f6b80afe3fd87d009de2b5f506e8a1a90f1d5443e79","first_computed_at":"2026-07-05T03:00:02.898399Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:00:02.898399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UvJCouUQlBsues9nQp9sMKxq2WR9w6q14/N2MHUUwKiiVArZLMVxwAlvusq0XVYa2wKDSFX4fEmP0tdedxIlAA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:00:02.898830Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.10708","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1565f03e540c697ab42151fafbcca4acf63543eba78b4d8891a37a568ffa0b2b","sha256:b3334ce7494d3f07f6629f16f4b71790ac1816b6ffd83dcd1d1285692d7c281d"],"state_sha256":"364e2fbedb6fb3190fdf7c64185e8344a73841d9baae45a59c2b4cb7608c58be"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8YjxNHgaNXifsdePNKy7P/a5jvK/fGfS6e+UsBg/4hL2RU1Bj5F2iN+bi0N4AeepN/mFdIvkftsfcFm7xJUaBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T09:13:18.836936Z","bundle_sha256":"6856e02a9773471a224146c4095a55aab4f925fa1ecea048a3e31ec40fa4a476"}}