{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:33JAM7KL3NAEICFMY3BNNBQVCM","short_pith_number":"pith:33JAM7KL","canonical_record":{"source":{"id":"2106.08637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-16T08:54:31Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"2d26584d221e9c47bc2cf7bfa48fc7ecdf2b00b3163eddbad4739dc8cf1eedc2","abstract_canon_sha256":"45a56d1d2b9364fa45e309163c68acbf7743a9630d0b9264529c1edd810eb49c"},"schema_version":"1.0"},"canonical_sha256":"ded2067d4bdb404408acc6c2d68615133e5ffb2c09d475cb4e3451c8de4e9a0d","source":{"kind":"arxiv","id":"2106.08637","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.08637","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"arxiv_version","alias_value":"2106.08637v1","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.08637","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"pith_short_12","alias_value":"33JAM7KL3NAE","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"pith_short_16","alias_value":"33JAM7KL3NAEICFM","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"pith_short_8","alias_value":"33JAM7KL","created_at":"2026-07-05T02:50:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:33JAM7KL3NAEICFMY3BNNBQVCM","target":"record","payload":{"canonical_record":{"source":{"id":"2106.08637","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-16T08:54:31Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"2d26584d221e9c47bc2cf7bfa48fc7ecdf2b00b3163eddbad4739dc8cf1eedc2","abstract_canon_sha256":"45a56d1d2b9364fa45e309163c68acbf7743a9630d0b9264529c1edd810eb49c"},"schema_version":"1.0"},"canonical_sha256":"ded2067d4bdb404408acc6c2d68615133e5ffb2c09d475cb4e3451c8de4e9a0d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:00.143063Z","signature_b64":"+J7jP+RiAusUtghpVYqVYfmOlADNkIfw2lOUlLf9LnjNe4gczP24ptBQ+F2+/EonVYlbD8pNy71OJ/BqBrwhDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ded2067d4bdb404408acc6c2d68615133e5ffb2c09d475cb4e3451c8de4e9a0d","last_reissued_at":"2026-07-05T02:50:00.142725Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:00.142725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2106.08637","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:50:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Kf58TrkfZC7icOdl1F6G3EBBZP0kIXHkSjKB/Vmo/ETKIPI6GfSu1g01hueiSHpdzP6hpyffHeXYkGOyye27AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:03:43.158172Z"},"content_sha256":"5e7334e4762621b83f385110f81bc813fc458168d93ec02e81a376938a414927","schema_version":"1.0","event_id":"sha256:5e7334e4762621b83f385110f81bc813fc458168d93ec02e81a376938a414927"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:33JAM7KL3NAEICFMY3BNNBQVCM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Bin Gu, Tan Liu, Wu Guo","submitted_at":"2021-06-16T08:54:31Z","abstract_excerpt":"Topic classification systems on spoken documents usually consist of two modules: an automatic speech recognition (ASR) module to convert speech into text and a text topic classification (TTC) module to predict the topic class from the decoded text. In this paper, instead of using the ASR transcripts, the fusion of deep acoustic and linguistic features is used for topic classification on spoken documents. More specifically, a conventional CTC-based acoustic model (AM) using phonemes as output units is first trained, and the outputs of the layer before the linear phoneme classifier in the traine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.08637","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/2106.08637/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:50:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sqfcQ17At84GMSfIZf47az0xiMkmkgYEdH2Rj+z5Zu2mjpunKIUy1RUiPkS8yfjnnK195D8XHLroTc+VODC/Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T19:03:43.158744Z"},"content_sha256":"58a156fbee9c40d4ce7750219ef4134796b119343abf2b1665bce94347c02fda","schema_version":"1.0","event_id":"sha256:58a156fbee9c40d4ce7750219ef4134796b119343abf2b1665bce94347c02fda"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/33JAM7KL3NAEICFMY3BNNBQVCM/bundle.json","state_url":"https://pith.science/pith/33JAM7KL3NAEICFMY3BNNBQVCM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/33JAM7KL3NAEICFMY3BNNBQVCM/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-12T19:03:43Z","links":{"resolver":"https://pith.science/pith/33JAM7KL3NAEICFMY3BNNBQVCM","bundle":"https://pith.science/pith/33JAM7KL3NAEICFMY3BNNBQVCM/bundle.json","state":"https://pith.science/pith/33JAM7KL3NAEICFMY3BNNBQVCM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/33JAM7KL3NAEICFMY3BNNBQVCM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:33JAM7KL3NAEICFMY3BNNBQVCM","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"45a56d1d2b9364fa45e309163c68acbf7743a9630d0b9264529c1edd810eb49c","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-16T08:54:31Z","title_canon_sha256":"2d26584d221e9c47bc2cf7bfa48fc7ecdf2b00b3163eddbad4739dc8cf1eedc2"},"schema_version":"1.0","source":{"id":"2106.08637","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2106.08637","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"arxiv_version","alias_value":"2106.08637v1","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.08637","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"pith_short_12","alias_value":"33JAM7KL3NAE","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"pith_short_16","alias_value":"33JAM7KL3NAEICFM","created_at":"2026-07-05T02:50:00Z"},{"alias_kind":"pith_short_8","alias_value":"33JAM7KL","created_at":"2026-07-05T02:50:00Z"}],"graph_snapshots":[{"event_id":"sha256:58a156fbee9c40d4ce7750219ef4134796b119343abf2b1665bce94347c02fda","target":"graph","created_at":"2026-07-05T02:50:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2106.08637/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Topic classification systems on spoken documents usually consist of two modules: an automatic speech recognition (ASR) module to convert speech into text and a text topic classification (TTC) module to predict the topic class from the decoded text. In this paper, instead of using the ASR transcripts, the fusion of deep acoustic and linguistic features is used for topic classification on spoken documents. More specifically, a conventional CTC-based acoustic model (AM) using phonemes as output units is first trained, and the outputs of the layer before the linear phoneme classifier in the traine","authors_text":"Bin Gu, Tan Liu, Wu Guo","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-16T08:54:31Z","title":"Topic Classification on Spoken Documents Using Deep Acoustic and Linguistic Features"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.08637","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:5e7334e4762621b83f385110f81bc813fc458168d93ec02e81a376938a414927","target":"record","created_at":"2026-07-05T02:50:00Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"45a56d1d2b9364fa45e309163c68acbf7743a9630d0b9264529c1edd810eb49c","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-16T08:54:31Z","title_canon_sha256":"2d26584d221e9c47bc2cf7bfa48fc7ecdf2b00b3163eddbad4739dc8cf1eedc2"},"schema_version":"1.0","source":{"id":"2106.08637","kind":"arxiv","version":1}},"canonical_sha256":"ded2067d4bdb404408acc6c2d68615133e5ffb2c09d475cb4e3451c8de4e9a0d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ded2067d4bdb404408acc6c2d68615133e5ffb2c09d475cb4e3451c8de4e9a0d","first_computed_at":"2026-07-05T02:50:00.142725Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:50:00.142725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+J7jP+RiAusUtghpVYqVYfmOlADNkIfw2lOUlLf9LnjNe4gczP24ptBQ+F2+/EonVYlbD8pNy71OJ/BqBrwhDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:50:00.143063Z","signed_message":"canonical_sha256_bytes"},"source_id":"2106.08637","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5e7334e4762621b83f385110f81bc813fc458168d93ec02e81a376938a414927","sha256:58a156fbee9c40d4ce7750219ef4134796b119343abf2b1665bce94347c02fda"],"state_sha256":"0439818547183a73c2c7972900e8c3b06eae65c07b2dbb310b18170a4f8fecaa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vjEav+ytnhapujBMPdJXT4D/mYadNiLoXaCgG3kXfnecpA/4bqWIIPKQxNqAChXasTgFAVH521YhVBgo/ubNDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T19:03:43.163168Z","bundle_sha256":"4e8ce442bd15f3d145910c7d1266cf82ff8916dfb94bcf1420137227354c92a6"}}