{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZOPDXBUGUIX7QWDBAYWHEZ5P2R","short_pith_number":"pith:ZOPDXBUG","schema_version":"1.0","canonical_sha256":"cb9e3b8686a22ff85861062c7267afd474d4643f3ba3fd46b329eb26fba94a3f","source":{"kind":"arxiv","id":"1801.09866","version":1},"attestation_state":"computed","paper":{"title":"Accelerating recurrent neural network language model based online speech recognition system","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Chiyoun Park, Jaewon Lee, Kyungmin Lee, Namhoon Kim","submitted_at":"2018-01-30T06:58:50Z","abstract_excerpt":"This paper presents methods to accelerate recurrent neural network based language models (RNNLMs) for online speech recognition systems. Firstly, a lossy compression of the past hidden layer outputs (history vector) with caching is introduced in order to reduce the number of LM queries. Next, RNNLM computations are deployed in a CPU-GPU hybrid manner, which computes each layer of the model on a more advantageous platform. The added overhead by data exchanges between CPU and GPU is compensated through a frame-wise batching strategy. The performance of the proposed methods evaluated on LibriSpee"},"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":"1801.09866","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-01-30T06:58:50Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"fb3e9db177f7fe837a330651c99d4b19eba12fddb0f3e64fb580eb5ef38676f1","abstract_canon_sha256":"d0c85884db9b1f41eb422408a353c072ce020a32492f5106763f2f2abb5c55c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:46.561671Z","signature_b64":"CJvfK+qijgnJCZhf8PkAg8mUtoKAt22tIfemrZrIP1dZA3LnefrxLnEAykok0i2pKesiXI3/b/fFp74s5ONZAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cb9e3b8686a22ff85861062c7267afd474d4643f3ba3fd46b329eb26fba94a3f","last_reissued_at":"2026-05-18T00:24:46.561123Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:46.561123Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Accelerating recurrent neural network language model based online speech recognition system","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Chiyoun Park, Jaewon Lee, Kyungmin Lee, Namhoon Kim","submitted_at":"2018-01-30T06:58:50Z","abstract_excerpt":"This paper presents methods to accelerate recurrent neural network based language models (RNNLMs) for online speech recognition systems. Firstly, a lossy compression of the past hidden layer outputs (history vector) with caching is introduced in order to reduce the number of LM queries. Next, RNNLM computations are deployed in a CPU-GPU hybrid manner, which computes each layer of the model on a more advantageous platform. The added overhead by data exchanges between CPU and GPU is compensated through a frame-wise batching strategy. The performance of the proposed methods evaluated on LibriSpee"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.09866","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1801.09866","created_at":"2026-05-18T00:24:46.561194+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.09866v1","created_at":"2026-05-18T00:24:46.561194+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.09866","created_at":"2026-05-18T00:24:46.561194+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZOPDXBUGUIX7","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZOPDXBUGUIX7QWDB","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZOPDXBUG","created_at":"2026-05-18T12:33:07.085635+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/ZOPDXBUGUIX7QWDBAYWHEZ5P2R","json":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R.json","graph_json":"https://pith.science/api/pith-number/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/graph.json","events_json":"https://pith.science/api/pith-number/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/events.json","paper":"https://pith.science/paper/ZOPDXBUG"},"agent_actions":{"view_html":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R","download_json":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R.json","view_paper":"https://pith.science/paper/ZOPDXBUG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.09866&json=true","fetch_graph":"https://pith.science/api/pith-number/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/graph.json","fetch_events":"https://pith.science/api/pith-number/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/action/storage_attestation","attest_author":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/action/author_attestation","sign_citation":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/action/citation_signature","submit_replication":"https://pith.science/pith/ZOPDXBUGUIX7QWDBAYWHEZ5P2R/action/replication_record"}},"created_at":"2026-05-18T00:24:46.561194+00:00","updated_at":"2026-05-18T00:24:46.561194+00:00"}