{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:D7WMUECCZFGDDJNBHBEPEVXKAC","short_pith_number":"pith:D7WMUECC","schema_version":"1.0","canonical_sha256":"1fecca1042c94c31a5a13848f256ea0094fd51b351a22bc515be1bc2d6a38fd1","source":{"kind":"arxiv","id":"1906.09825","version":1},"attestation_state":"computed","paper":{"title":"SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Okko R\\\"as\\\"anen, Shreyas Seshadri","submitted_at":"2019-06-24T10:05:23Z","abstract_excerpt":"Automatic syllable count estimation (SCE) is used in a variety of applications ranging from speaking rate estimation to detecting social activity from wearable microphones or developmental research concerned with quantifying speech heard by language-learning children in different environments. The majority of previously utilized SCE methods have relied on heuristic DSP methods, and only a small number of bi-directional long short-term memory (BLSTM) approaches have made use of modern machine learning approaches in the SCE task. This paper presents a novel end-to-end method called SylNet for au"},"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":"1906.09825","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-06-24T10:05:23Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"962536ba91038dce981e3c30b99f67febc2d7ff98d0de8e44ea2744621cdfa9e","abstract_canon_sha256":"6d294ceb661a0a6a1db8e6243d0901212c159f3bdf0f9756807dbec9ca2438b7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:01:56.868534Z","signature_b64":"pYgQy9K2IzGe0X7J4/AymolwU7L5kbnGzeFb1IpKnsXzV+4iqIbPLhI4jS6ezoXf/Cfy/7zTsEoG14BwuAWQBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1fecca1042c94c31a5a13848f256ea0094fd51b351a22bc515be1bc2d6a38fd1","last_reissued_at":"2026-07-05T00:01:56.868159Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:01:56.868159Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Okko R\\\"as\\\"anen, Shreyas Seshadri","submitted_at":"2019-06-24T10:05:23Z","abstract_excerpt":"Automatic syllable count estimation (SCE) is used in a variety of applications ranging from speaking rate estimation to detecting social activity from wearable microphones or developmental research concerned with quantifying speech heard by language-learning children in different environments. The majority of previously utilized SCE methods have relied on heuristic DSP methods, and only a small number of bi-directional long short-term memory (BLSTM) approaches have made use of modern machine learning approaches in the SCE task. This paper presents a novel end-to-end method called SylNet for au"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.09825","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/1906.09825/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":"1906.09825","created_at":"2026-07-05T00:01:56.868218+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.09825v1","created_at":"2026-07-05T00:01:56.868218+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.09825","created_at":"2026-07-05T00:01:56.868218+00:00"},{"alias_kind":"pith_short_12","alias_value":"D7WMUECCZFGD","created_at":"2026-07-05T00:01:56.868218+00:00"},{"alias_kind":"pith_short_16","alias_value":"D7WMUECCZFGDDJNB","created_at":"2026-07-05T00:01:56.868218+00:00"},{"alias_kind":"pith_short_8","alias_value":"D7WMUECC","created_at":"2026-07-05T00:01:56.868218+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/D7WMUECCZFGDDJNBHBEPEVXKAC","json":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC.json","graph_json":"https://pith.science/api/pith-number/D7WMUECCZFGDDJNBHBEPEVXKAC/graph.json","events_json":"https://pith.science/api/pith-number/D7WMUECCZFGDDJNBHBEPEVXKAC/events.json","paper":"https://pith.science/paper/D7WMUECC"},"agent_actions":{"view_html":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC","download_json":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC.json","view_paper":"https://pith.science/paper/D7WMUECC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.09825&json=true","fetch_graph":"https://pith.science/api/pith-number/D7WMUECCZFGDDJNBHBEPEVXKAC/graph.json","fetch_events":"https://pith.science/api/pith-number/D7WMUECCZFGDDJNBHBEPEVXKAC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC/action/storage_attestation","attest_author":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC/action/author_attestation","sign_citation":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC/action/citation_signature","submit_replication":"https://pith.science/pith/D7WMUECCZFGDDJNBHBEPEVXKAC/action/replication_record"}},"created_at":"2026-07-05T00:01:56.868218+00:00","updated_at":"2026-07-05T00:01:56.868218+00:00"}