{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:NXEW53IVRY3VIKYJHH2435DCKO","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":"d28267b37774b3e197a0f0b1f90799858dc8d7be1b343c3550f6c25ad75c2584","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-06-23T12:18:03Z","title_canon_sha256":"8e1209b519c4c1c647c50e48fd5aca6df1f4eb382898164171be0439ed87c5ff"},"schema_version":"1.0","source":{"id":"2206.13236","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.13236","created_at":"2026-07-05T04:35:04Z"},{"alias_kind":"arxiv_version","alias_value":"2206.13236v1","created_at":"2026-07-05T04:35:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.13236","created_at":"2026-07-05T04:35:04Z"},{"alias_kind":"pith_short_12","alias_value":"NXEW53IVRY3V","created_at":"2026-07-05T04:35:04Z"},{"alias_kind":"pith_short_16","alias_value":"NXEW53IVRY3VIKYJ","created_at":"2026-07-05T04:35:04Z"},{"alias_kind":"pith_short_8","alias_value":"NXEW53IV","created_at":"2026-07-05T04:35:04Z"}],"graph_snapshots":[{"event_id":"sha256:f7f3fae31db8ca563ea8c742ffba99b79bd503cedf7346126c5d87473ad731b3","target":"graph","created_at":"2026-07-05T04:35:04Z","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/2206.13236/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The RNN-Transducer (RNN-T) framework for speech recognition has been growing in popularity, particularly for deployed real-time ASR systems, because it combines high accuracy with naturally streaming recognition. One of the drawbacks of RNN-T is that its loss function is relatively slow to compute, and can use a lot of memory. Excessive GPU memory usage can make it impractical to use RNN-T loss in cases where the vocabulary size is large: for example, for Chinese character-based ASR. We introduce a method for faster and more memory-efficient RNN-T loss computation. We first obtain pruning boun","authors_text":"Daniel Povey, Fangjun Kuang, Liyong Guo, Long Lin, Mingshuang Luo, Wei Kang, Zengwei Yao","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-06-23T12:18:03Z","title":"Pruned RNN-T for fast, memory-efficient ASR training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.13236","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":"sha256:9b1684ae67be85a74c96d46e040f315f1786a706eb5c3b7f169b19344c3b108a","target":"record","created_at":"2026-07-05T04:35:04Z","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":"d28267b37774b3e197a0f0b1f90799858dc8d7be1b343c3550f6c25ad75c2584","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2022-06-23T12:18:03Z","title_canon_sha256":"8e1209b519c4c1c647c50e48fd5aca6df1f4eb382898164171be0439ed87c5ff"},"schema_version":"1.0","source":{"id":"2206.13236","kind":"arxiv","version":1}},"canonical_sha256":"6dc96eed158e37542b0939f5cdf46253bf4aff246c4733dc3ab327b660447f17","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6dc96eed158e37542b0939f5cdf46253bf4aff246c4733dc3ab327b660447f17","first_computed_at":"2026-07-05T04:35:04.435564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:35:04.435564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"byIzF6Mhnf+Syu2TVqCVankx13TPiNpJ1kkkaF1Bh9NeRlGUZPG0B2FxasC6rTcJgx96PHZUmBcZo+Y7Q4JhBw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:35:04.436000Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.13236","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b1684ae67be85a74c96d46e040f315f1786a706eb5c3b7f169b19344c3b108a","sha256:f7f3fae31db8ca563ea8c742ffba99b79bd503cedf7346126c5d87473ad731b3"],"state_sha256":"20b128312e1a6712c6bb9083f2e514e8ad32120d9af0c55bd0def462ae645b5c"}