{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CSKFCEYZUY7EI5JQQE345UO5AU","short_pith_number":"pith:CSKFCEYZ","canonical_record":{"source":{"id":"1701.08071","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-01-27T14:50:36Z","cross_cats_sorted":[],"title_canon_sha256":"e15ad962ff62820cfe856ba5219b4600bf2ed61c0bdc2f5776ffd7e698dd2734","abstract_canon_sha256":"014f2af8dcd27a3e6000bc8cc86bf52c3d9cca9ace69a95b819d6a5dcea4a790"},"schema_version":"1.0"},"canonical_sha256":"1494511319a63e4475308137ced1dd053b78972ffe81ead2b3b584fd8a041777","source":{"kind":"arxiv","id":"1701.08071","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.08071","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"arxiv_version","alias_value":"1701.08071v2","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.08071","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"pith_short_12","alias_value":"CSKFCEYZUY7E","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CSKFCEYZUY7EI5JQ","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CSKFCEYZ","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CSKFCEYZUY7EI5JQQE345UO5AU","target":"record","payload":{"canonical_record":{"source":{"id":"1701.08071","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-01-27T14:50:36Z","cross_cats_sorted":[],"title_canon_sha256":"e15ad962ff62820cfe856ba5219b4600bf2ed61c0bdc2f5776ffd7e698dd2734","abstract_canon_sha256":"014f2af8dcd27a3e6000bc8cc86bf52c3d9cca9ace69a95b819d6a5dcea4a790"},"schema_version":"1.0"},"canonical_sha256":"1494511319a63e4475308137ced1dd053b78972ffe81ead2b3b584fd8a041777","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:11:28.302171Z","signature_b64":"Rbuy12fhpY0qnIAOwxotPCyKLECNnkTj0jReUY97sFUUNnmY+tMN2ZPEvQXuqjbsXIODQdgCLZ4bfOgti88fCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1494511319a63e4475308137ced1dd053b78972ffe81ead2b3b584fd8a041777","last_reissued_at":"2026-05-18T00:11:28.301690Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:11:28.301690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.08071","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:11:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mygJuMerwlNLsFDVheTFiszAeeW+rpmuCx2yvIvDoMDOt5wDXVcxnJ3j4fE9uJ9KjaJrSR7yMcyr7epdqxzOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T14:37:07.090400Z"},"content_sha256":"d4bfba3a25f5c59729b4bf4620da0d3f78cf80eeb0871671994c73254f0c62c4","schema_version":"1.0","event_id":"sha256:d4bfba3a25f5c59729b4bf4620da0d3f78cf80eeb0871671994c73254f0c62c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CSKFCEYZUY7EI5JQQE345UO5AU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Emotion Recognition From Speech With Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Pavel Prikhodko, Vladimir Chernykh","submitted_at":"2017-01-27T14:50:36Z","abstract_excerpt":"In this paper the task of emotion recognition from speech is considered. Proposed approach uses deep recurrent neural network trained on a sequence of acoustic features calculated over small speech intervals. At the same time special probabilistic-nature CTC loss function allows to consider long utterances containing both emotional and neutral parts. The effectiveness of such an approach is shown in two ways. Firstly, the comparison with recent advances in this field is carried out. Secondly, human performance on the same task is measured. Both criteria show the high quality of the proposed me"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.08071","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":""},"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":"2026-05-18T00:11:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ng9gmvjD2nDi8rFtPi0nSKoHVo0D46zm45dHf8y2jndGSEN129Yd3VoVLFEEPn23poONoGYIba9vDILURsTjAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T14:37:07.090784Z"},"content_sha256":"f95f4b78faf025e0f5775118a0500c5b63a250e78a6c104b0893c58aa7fd57df","schema_version":"1.0","event_id":"sha256:f95f4b78faf025e0f5775118a0500c5b63a250e78a6c104b0893c58aa7fd57df"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CSKFCEYZUY7EI5JQQE345UO5AU/bundle.json","state_url":"https://pith.science/pith/CSKFCEYZUY7EI5JQQE345UO5AU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CSKFCEYZUY7EI5JQQE345UO5AU/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":"2026-06-30T14:37:07Z","links":{"resolver":"https://pith.science/pith/CSKFCEYZUY7EI5JQQE345UO5AU","bundle":"https://pith.science/pith/CSKFCEYZUY7EI5JQQE345UO5AU/bundle.json","state":"https://pith.science/pith/CSKFCEYZUY7EI5JQQE345UO5AU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CSKFCEYZUY7EI5JQQE345UO5AU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CSKFCEYZUY7EI5JQQE345UO5AU","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":"014f2af8dcd27a3e6000bc8cc86bf52c3d9cca9ace69a95b819d6a5dcea4a790","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-01-27T14:50:36Z","title_canon_sha256":"e15ad962ff62820cfe856ba5219b4600bf2ed61c0bdc2f5776ffd7e698dd2734"},"schema_version":"1.0","source":{"id":"1701.08071","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.08071","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"arxiv_version","alias_value":"1701.08071v2","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.08071","created_at":"2026-05-18T00:11:28Z"},{"alias_kind":"pith_short_12","alias_value":"CSKFCEYZUY7E","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CSKFCEYZUY7EI5JQ","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CSKFCEYZ","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:f95f4b78faf025e0f5775118a0500c5b63a250e78a6c104b0893c58aa7fd57df","target":"graph","created_at":"2026-05-18T00:11:28Z","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"},"paper":{"abstract_excerpt":"In this paper the task of emotion recognition from speech is considered. Proposed approach uses deep recurrent neural network trained on a sequence of acoustic features calculated over small speech intervals. At the same time special probabilistic-nature CTC loss function allows to consider long utterances containing both emotional and neutral parts. The effectiveness of such an approach is shown in two ways. Firstly, the comparison with recent advances in this field is carried out. Secondly, human performance on the same task is measured. Both criteria show the high quality of the proposed me","authors_text":"Pavel Prikhodko, Vladimir Chernykh","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-01-27T14:50:36Z","title":"Emotion Recognition From Speech With Recurrent Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.08071","kind":"arxiv","version":2},"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:d4bfba3a25f5c59729b4bf4620da0d3f78cf80eeb0871671994c73254f0c62c4","target":"record","created_at":"2026-05-18T00:11:28Z","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":"014f2af8dcd27a3e6000bc8cc86bf52c3d9cca9ace69a95b819d6a5dcea4a790","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-01-27T14:50:36Z","title_canon_sha256":"e15ad962ff62820cfe856ba5219b4600bf2ed61c0bdc2f5776ffd7e698dd2734"},"schema_version":"1.0","source":{"id":"1701.08071","kind":"arxiv","version":2}},"canonical_sha256":"1494511319a63e4475308137ced1dd053b78972ffe81ead2b3b584fd8a041777","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1494511319a63e4475308137ced1dd053b78972ffe81ead2b3b584fd8a041777","first_computed_at":"2026-05-18T00:11:28.301690Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:11:28.301690Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Rbuy12fhpY0qnIAOwxotPCyKLECNnkTj0jReUY97sFUUNnmY+tMN2ZPEvQXuqjbsXIODQdgCLZ4bfOgti88fCA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:11:28.302171Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.08071","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4bfba3a25f5c59729b4bf4620da0d3f78cf80eeb0871671994c73254f0c62c4","sha256:f95f4b78faf025e0f5775118a0500c5b63a250e78a6c104b0893c58aa7fd57df"],"state_sha256":"4f002fc89b42408a6d2bb42e33b8984a7e44ebb7bb2d7063c02b54d2e9739f41"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p8XGVlR1kBJrjDoEZnUeKvPaaYyYtgpTQPLj1F3QXm+J9a5fNHCjUkYb+m4ms49kWSkYMQ+eAZcfk5Fq+/W7Dg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T14:37:07.093266Z","bundle_sha256":"d71df9f9fd555052b6ae1ae5b38f3aa0496089cb140052c9f23b2b8a80e62aef"}}