{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:RJPGZ4OILMZ4KX3WPOOSVH4BHX","short_pith_number":"pith:RJPGZ4OI","schema_version":"1.0","canonical_sha256":"8a5e6cf1c85b33c55f767b9d2a9f813df4b042d8372b51abaecbadd36a22c634","source":{"kind":"arxiv","id":"2307.05956","version":2},"attestation_state":"computed","paper":{"title":"Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Binbin Du, Guodong Ma, Wenxuan Wang, Yuke Li","submitted_at":"2023-07-12T07:00:12Z","abstract_excerpt":"Multilingual speech recognition for both monolingual and code-switching speech is a challenging task. Recently, based on the Mixture of Experts (MoE), many works have made good progress in multilingual and code-switching ASR, but present huge computational complexity with the increase of supported languages. In this work, we propose a computation-efficient network named Language-Routing Mixture of Experts (LR-MoE) for multilingual and code-switching ASR. LR-MoE extracts language-specific representations through the Mixture of Language Experts (MLE), which is guided to learn by a frame-wise lan"},"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":"2307.05956","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2023-07-12T07:00:12Z","cross_cats_sorted":["eess.AS"],"title_canon_sha256":"02a1b3cac9b310674d5dcb6c1e2f6c06577fea1057dd6f43094ebcccd700dbec","abstract_canon_sha256":"9eca4a9c2958e6f5238d7027e922c13f2abdd329c74d6fcdc6c38ff9d7cfaa28"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:30:47.478054Z","signature_b64":"ZNQ3UfJJyzDX5AA1kKVsQVmz/ZzNekhCoL+vpOmuu3QjMUhTWjEteGD9kapXDzTdsLkgsLZEU5qD1t2bwctiAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8a5e6cf1c85b33c55f767b9d2a9f813df4b042d8372b51abaecbadd36a22c634","last_reissued_at":"2026-07-05T06:30:47.477596Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:30:47.477596Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Language-Routing Mixture of Experts for Multilingual and Code-Switching Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.AS"],"primary_cat":"cs.SD","authors_text":"Binbin Du, Guodong Ma, Wenxuan Wang, Yuke Li","submitted_at":"2023-07-12T07:00:12Z","abstract_excerpt":"Multilingual speech recognition for both monolingual and code-switching speech is a challenging task. Recently, based on the Mixture of Experts (MoE), many works have made good progress in multilingual and code-switching ASR, but present huge computational complexity with the increase of supported languages. In this work, we propose a computation-efficient network named Language-Routing Mixture of Experts (LR-MoE) for multilingual and code-switching ASR. LR-MoE extracts language-specific representations through the Mixture of Language Experts (MLE), which is guided to learn by a frame-wise lan"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.05956","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/2307.05956/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":"2307.05956","created_at":"2026-07-05T06:30:47.477656+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.05956v2","created_at":"2026-07-05T06:30:47.477656+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.05956","created_at":"2026-07-05T06:30:47.477656+00:00"},{"alias_kind":"pith_short_12","alias_value":"RJPGZ4OILMZ4","created_at":"2026-07-05T06:30:47.477656+00:00"},{"alias_kind":"pith_short_16","alias_value":"RJPGZ4OILMZ4KX3W","created_at":"2026-07-05T06:30:47.477656+00:00"},{"alias_kind":"pith_short_8","alias_value":"RJPGZ4OI","created_at":"2026-07-05T06:30:47.477656+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.15622","citing_title":"TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch","ref_index":9,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX","json":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX.json","graph_json":"https://pith.science/api/pith-number/RJPGZ4OILMZ4KX3WPOOSVH4BHX/graph.json","events_json":"https://pith.science/api/pith-number/RJPGZ4OILMZ4KX3WPOOSVH4BHX/events.json","paper":"https://pith.science/paper/RJPGZ4OI"},"agent_actions":{"view_html":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX","download_json":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX.json","view_paper":"https://pith.science/paper/RJPGZ4OI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.05956&json=true","fetch_graph":"https://pith.science/api/pith-number/RJPGZ4OILMZ4KX3WPOOSVH4BHX/graph.json","fetch_events":"https://pith.science/api/pith-number/RJPGZ4OILMZ4KX3WPOOSVH4BHX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX/action/storage_attestation","attest_author":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX/action/author_attestation","sign_citation":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX/action/citation_signature","submit_replication":"https://pith.science/pith/RJPGZ4OILMZ4KX3WPOOSVH4BHX/action/replication_record"}},"created_at":"2026-07-05T06:30:47.477656+00:00","updated_at":"2026-07-05T06:30:47.477656+00:00"}