{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7J736MQYAMZFLJ4OMGVKSPTYCK","short_pith_number":"pith:7J736MQY","schema_version":"1.0","canonical_sha256":"fa7fbf3218033255a78e61aaa93e78129f413ec0aa85ffc10fc3c7fd9caae728","source":{"kind":"arxiv","id":"2302.14120","version":1},"attestation_state":"computed","paper":{"title":"Diagonal State Space Augmented Transformers for Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Ankit Gupta, George Saon, Xiaodong Cui","submitted_at":"2023-02-27T20:08:36Z","abstract_excerpt":"We improve on the popular conformer architecture by replacing the depthwise temporal convolutions with diagonal state space (DSS) models. DSS is a recently introduced variant of linear RNNs obtained by discretizing a linear dynamical system with a diagonal state transition matrix. DSS layers project the input sequence onto a space of orthogonal polynomials where the choice of basis functions, metric and support is controlled by the eigenvalues of the transition matrix. We compare neural transducers with either conformer or our proposed DSS-augmented transformer (DSSformer) encoders on three pu"},"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":"2302.14120","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2023-02-27T20:08:36Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"570fc35b3de9608eed915775d98d7e51aab9755b3a77fb8cf1cad5824275f970","abstract_canon_sha256":"c9cd1af9df6b17f80203ebae13aa861013a0dd8b9c1d81cc68f49f916a6957a9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:46:40.740177Z","signature_b64":"mceeeeZch++4lX5CBL4eSxDW7qEzoxfAocKyI58F2DERrf8KCnl8eLSeCANblzHyfaoQCZ+0KMJfHBGh91DoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa7fbf3218033255a78e61aaa93e78129f413ec0aa85ffc10fc3c7fd9caae728","last_reissued_at":"2026-07-05T05:46:40.739631Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:46:40.739631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Diagonal State Space Augmented Transformers for Speech Recognition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Ankit Gupta, George Saon, Xiaodong Cui","submitted_at":"2023-02-27T20:08:36Z","abstract_excerpt":"We improve on the popular conformer architecture by replacing the depthwise temporal convolutions with diagonal state space (DSS) models. DSS is a recently introduced variant of linear RNNs obtained by discretizing a linear dynamical system with a diagonal state transition matrix. DSS layers project the input sequence onto a space of orthogonal polynomials where the choice of basis functions, metric and support is controlled by the eigenvalues of the transition matrix. We compare neural transducers with either conformer or our proposed DSS-augmented transformer (DSSformer) encoders on three pu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.14120","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.14120/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":"2302.14120","created_at":"2026-07-05T05:46:40.739696+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.14120v1","created_at":"2026-07-05T05:46:40.739696+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.14120","created_at":"2026-07-05T05:46:40.739696+00:00"},{"alias_kind":"pith_short_12","alias_value":"7J736MQYAMZF","created_at":"2026-07-05T05:46:40.739696+00:00"},{"alias_kind":"pith_short_16","alias_value":"7J736MQYAMZFLJ4O","created_at":"2026-07-05T05:46:40.739696+00:00"},{"alias_kind":"pith_short_8","alias_value":"7J736MQY","created_at":"2026-07-05T05:46:40.739696+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/7J736MQYAMZFLJ4OMGVKSPTYCK","json":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK.json","graph_json":"https://pith.science/api/pith-number/7J736MQYAMZFLJ4OMGVKSPTYCK/graph.json","events_json":"https://pith.science/api/pith-number/7J736MQYAMZFLJ4OMGVKSPTYCK/events.json","paper":"https://pith.science/paper/7J736MQY"},"agent_actions":{"view_html":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK","download_json":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK.json","view_paper":"https://pith.science/paper/7J736MQY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.14120&json=true","fetch_graph":"https://pith.science/api/pith-number/7J736MQYAMZFLJ4OMGVKSPTYCK/graph.json","fetch_events":"https://pith.science/api/pith-number/7J736MQYAMZFLJ4OMGVKSPTYCK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK/action/storage_attestation","attest_author":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK/action/author_attestation","sign_citation":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK/action/citation_signature","submit_replication":"https://pith.science/pith/7J736MQYAMZFLJ4OMGVKSPTYCK/action/replication_record"}},"created_at":"2026-07-05T05:46:40.739696+00:00","updated_at":"2026-07-05T05:46:40.739696+00:00"}