{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AFBI7ZJRIVLNKHUE6F5BNPI25A","short_pith_number":"pith:AFBI7ZJR","schema_version":"1.0","canonical_sha256":"01428fe5314556d51e84f17a16bd1ae83bdb87daaeefad749e4a44eec0e3f135","source":{"kind":"arxiv","id":"2211.00896","version":2},"attestation_state":"computed","paper":{"title":"Factorized Blank Thresholding for Improved Runtime Efficiency of Neural Transducers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Duc Le, Frank Seide, Kjell Schubert, Michael L. Seltzer, Ozlem Kalinli, Yang Li, Yuhao Wang","submitted_at":"2022-11-02T05:42:53Z","abstract_excerpt":"We show how factoring the RNN-T's output distribution can significantly reduce the computation cost and power consumption for on-device ASR inference with no loss in accuracy. With the rise in popularity of neural-transducer type models like the RNN-T for on-device ASR, optimizing RNN-T's runtime efficiency is of great interest. While previous work has primarily focused on the optimization of RNN-T's acoustic encoder and predictor, this paper focuses the attention on the joiner. We show that despite being only a small part of RNN-T, the joiner has a large impact on the overall model's runtime "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2211.00896","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-11-02T05:42:53Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"1e8c26f781485c4e8722516ce1311b0904d3a55dbe90f069d3a0b1992436e0d9","abstract_canon_sha256":"a82dee7d0b7aa6354b9684556e2181a67f30fd4642e4e0a77bf2f12f8c6beff4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:48:02.936932Z","signature_b64":"cktH25RGRWJ+wS7Qxxk4rjpZ/l+/ap4iayLHdAMbMsW160N7WwnZUVRI+9yPou+aBYly3wH8q9878qV+NOjPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"01428fe5314556d51e84f17a16bd1ae83bdb87daaeefad749e4a44eec0e3f135","last_reissued_at":"2026-07-05T05:48:02.936525Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:48:02.936525Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Factorized Blank Thresholding for Improved Runtime Efficiency of Neural Transducers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Duc Le, Frank Seide, Kjell Schubert, Michael L. Seltzer, Ozlem Kalinli, Yang Li, Yuhao Wang","submitted_at":"2022-11-02T05:42:53Z","abstract_excerpt":"We show how factoring the RNN-T's output distribution can significantly reduce the computation cost and power consumption for on-device ASR inference with no loss in accuracy. With the rise in popularity of neural-transducer type models like the RNN-T for on-device ASR, optimizing RNN-T's runtime efficiency is of great interest. While previous work has primarily focused on the optimization of RNN-T's acoustic encoder and predictor, this paper focuses the attention on the joiner. We show that despite being only a small part of RNN-T, the joiner has a large impact on the overall model's runtime "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.00896","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2211.00896/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2211.00896","created_at":"2026-07-05T05:48:02.936584+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.00896v2","created_at":"2026-07-05T05:48:02.936584+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.00896","created_at":"2026-07-05T05:48:02.936584+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFBI7ZJRIVLN","created_at":"2026-07-05T05:48:02.936584+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFBI7ZJRIVLNKHUE","created_at":"2026-07-05T05:48:02.936584+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFBI7ZJR","created_at":"2026-07-05T05:48:02.936584+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A","json":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A.json","graph_json":"https://pith.science/api/pith-number/AFBI7ZJRIVLNKHUE6F5BNPI25A/graph.json","events_json":"https://pith.science/api/pith-number/AFBI7ZJRIVLNKHUE6F5BNPI25A/events.json","paper":"https://pith.science/paper/AFBI7ZJR"},"agent_actions":{"view_html":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A","download_json":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A.json","view_paper":"https://pith.science/paper/AFBI7ZJR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.00896&json=true","fetch_graph":"https://pith.science/api/pith-number/AFBI7ZJRIVLNKHUE6F5BNPI25A/graph.json","fetch_events":"https://pith.science/api/pith-number/AFBI7ZJRIVLNKHUE6F5BNPI25A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A/action/storage_attestation","attest_author":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A/action/author_attestation","sign_citation":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A/action/citation_signature","submit_replication":"https://pith.science/pith/AFBI7ZJRIVLNKHUE6F5BNPI25A/action/replication_record"}},"created_at":"2026-07-05T05:48:02.936584+00:00","updated_at":"2026-07-05T05:48:02.936584+00:00"}