{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:7UY6BMX2Z5LVOOAKBCH45HJ7TK","short_pith_number":"pith:7UY6BMX2","canonical_record":{"source":{"id":"2010.11395","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-22T03:01:21Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"33f19894515168ecb5d5e8b7362c61e32b1d4f0c186db1139fd34ba8399f6723","abstract_canon_sha256":"dd8be88ec1b083c8ac9230b1ab47840b5788161f675a9e2f807445ae6ff39b15"},"schema_version":"1.0"},"canonical_sha256":"fd31e0b2facf5757380a088fce9d3f9a9146304aa5abe3898ee9bdfe08952974","source":{"kind":"arxiv","id":"2010.11395","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.11395","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"arxiv_version","alias_value":"2010.11395v3","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.11395","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"pith_short_12","alias_value":"7UY6BMX2Z5LV","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"pith_short_16","alias_value":"7UY6BMX2Z5LVOOAK","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"pith_short_8","alias_value":"7UY6BMX2","created_at":"2026-07-05T02:18:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:7UY6BMX2Z5LVOOAKBCH45HJ7TK","target":"record","payload":{"canonical_record":{"source":{"id":"2010.11395","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-22T03:01:21Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"33f19894515168ecb5d5e8b7362c61e32b1d4f0c186db1139fd34ba8399f6723","abstract_canon_sha256":"dd8be88ec1b083c8ac9230b1ab47840b5788161f675a9e2f807445ae6ff39b15"},"schema_version":"1.0"},"canonical_sha256":"fd31e0b2facf5757380a088fce9d3f9a9146304aa5abe3898ee9bdfe08952974","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:18:56.864616Z","signature_b64":"HeIAV6YEE3B5GhawduBXprOew3r/WvFbWpdxc4VgKBNPrt0Z2Rty1+zA630jBda2+QM/7cjltrPQ92pNHzw7Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fd31e0b2facf5757380a088fce9d3f9a9146304aa5abe3898ee9bdfe08952974","last_reissued_at":"2026-07-05T02:18:56.864224Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:18:56.864224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.11395","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:18:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ldpnfSGiO09aXMWnaAdk6TyLcUg1aLXwNcl7YbQsVl0SsSo/RWT3pNtQ63reMQEUloCw8ZFbx0YMZL8yp5TlBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T16:31:42.475540Z"},"content_sha256":"411e1319f17f937c5662d373be59a4fe3e5eef59b6bb3cacb9c8995bab1064a5","schema_version":"1.0","event_id":"sha256:411e1319f17f937c5662d373be59a4fe3e5eef59b6bb3cacb9c8995bab1064a5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:7UY6BMX2Z5LVOOAKBCH45HJ7TK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.CL","authors_text":"Jinyu Li, Shujie Liu, Xie Chen, Yu Wu, Zhenghao Wang","submitted_at":"2020-10-22T03:01:21Z","abstract_excerpt":"Recently, Transformer based end-to-end models have achieved great success in many areas including speech recognition. However, compared to LSTM models, the heavy computational cost of the Transformer during inference is a key issue to prevent their applications. In this work, we explored the potential of Transformer Transducer (T-T) models for the fist pass decoding with low latency and fast speed on a large-scale dataset. We combine the idea of Transformer-XL and chunk-wise streaming processing to design a streamable Transformer Transducer model. We demonstrate that T-T outperforms the hybrid"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.11395","kind":"arxiv","version":3},"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/2010.11395/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":"2026-07-05T02:18:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RmES/7Y9SjBCY/Lhc7hBsJYC373uFjQnuM0d1sJlVhB1la5Ji4fXe1g9VpwRbSCoBOUnfXdISHAFpJw6TuSkAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T16:31:42.476051Z"},"content_sha256":"88ec258b276999f0f983eda4b51904c33f228809d98a2a3d43b08b5a23d5aabf","schema_version":"1.0","event_id":"sha256:88ec258b276999f0f983eda4b51904c33f228809d98a2a3d43b08b5a23d5aabf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK/bundle.json","state_url":"https://pith.science/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK/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":"2026-08-20T16:31:42Z","links":{"resolver":"https://pith.science/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK","bundle":"https://pith.science/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK/bundle.json","state":"https://pith.science/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7UY6BMX2Z5LVOOAKBCH45HJ7TK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:7UY6BMX2Z5LVOOAKBCH45HJ7TK","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":"dd8be88ec1b083c8ac9230b1ab47840b5788161f675a9e2f807445ae6ff39b15","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-22T03:01:21Z","title_canon_sha256":"33f19894515168ecb5d5e8b7362c61e32b1d4f0c186db1139fd34ba8399f6723"},"schema_version":"1.0","source":{"id":"2010.11395","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.11395","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"arxiv_version","alias_value":"2010.11395v3","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.11395","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"pith_short_12","alias_value":"7UY6BMX2Z5LV","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"pith_short_16","alias_value":"7UY6BMX2Z5LVOOAK","created_at":"2026-07-05T02:18:56Z"},{"alias_kind":"pith_short_8","alias_value":"7UY6BMX2","created_at":"2026-07-05T02:18:56Z"}],"graph_snapshots":[{"event_id":"sha256:88ec258b276999f0f983eda4b51904c33f228809d98a2a3d43b08b5a23d5aabf","target":"graph","created_at":"2026-07-05T02:18:56Z","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/2010.11395/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, Transformer based end-to-end models have achieved great success in many areas including speech recognition. However, compared to LSTM models, the heavy computational cost of the Transformer during inference is a key issue to prevent their applications. In this work, we explored the potential of Transformer Transducer (T-T) models for the fist pass decoding with low latency and fast speed on a large-scale dataset. We combine the idea of Transformer-XL and chunk-wise streaming processing to design a streamable Transformer Transducer model. We demonstrate that T-T outperforms the hybrid","authors_text":"Jinyu Li, Shujie Liu, Xie Chen, Yu Wu, Zhenghao Wang","cross_cats":["eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-22T03:01:21Z","title":"Developing Real-time Streaming Transformer Transducer for Speech Recognition on Large-scale Dataset"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.11395","kind":"arxiv","version":3},"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":"sha256:411e1319f17f937c5662d373be59a4fe3e5eef59b6bb3cacb9c8995bab1064a5","target":"record","created_at":"2026-07-05T02:18:56Z","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":"dd8be88ec1b083c8ac9230b1ab47840b5788161f675a9e2f807445ae6ff39b15","cross_cats_sorted":["eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-22T03:01:21Z","title_canon_sha256":"33f19894515168ecb5d5e8b7362c61e32b1d4f0c186db1139fd34ba8399f6723"},"schema_version":"1.0","source":{"id":"2010.11395","kind":"arxiv","version":3}},"canonical_sha256":"fd31e0b2facf5757380a088fce9d3f9a9146304aa5abe3898ee9bdfe08952974","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fd31e0b2facf5757380a088fce9d3f9a9146304aa5abe3898ee9bdfe08952974","first_computed_at":"2026-07-05T02:18:56.864224Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:18:56.864224Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HeIAV6YEE3B5GhawduBXprOew3r/WvFbWpdxc4VgKBNPrt0Z2Rty1+zA630jBda2+QM/7cjltrPQ92pNHzw7Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:18:56.864616Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.11395","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:411e1319f17f937c5662d373be59a4fe3e5eef59b6bb3cacb9c8995bab1064a5","sha256:88ec258b276999f0f983eda4b51904c33f228809d98a2a3d43b08b5a23d5aabf"],"state_sha256":"61cca7cba4a31e8818ad4f9cf5dab453704c2cd16b8e602bf006a0a174abf8a5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pEirIbLVeCw+sraJEPdYyx/rltvnw+c+ubFvQW81WWBJZdyhtZrYUsydIaJqpnPFkSO74he1GxaDFbHT5sI7AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T16:31:42.482015Z","bundle_sha256":"19e598b53a9fa178fa18b6f9642c3425f5880c3d4aacfa8fca36b3b46a7f2257"}}