{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:XH43SOJMHPFVD53ZC7DJJOCMFK","short_pith_number":"pith:XH43SOJM","canonical_record":{"source":{"id":"2305.18640","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2023-05-29T22:27:48Z","cross_cats_sorted":[],"title_canon_sha256":"183956c4222204a9af1e946e4745768a853f549962a7b566d0ae63fd4118b723","abstract_canon_sha256":"17d742d90604e657392b4a1dbdf39313b0015639bd4b61b42fb7265d5acee86e"},"schema_version":"1.0"},"canonical_sha256":"b9f9b9392c3bcb51f77917c694b84c2abf572f4fc7559ecdff0eac907198cf4b","source":{"kind":"arxiv","id":"2305.18640","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.18640","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"arxiv_version","alias_value":"2305.18640v1","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18640","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"pith_short_12","alias_value":"XH43SOJMHPFV","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"pith_short_16","alias_value":"XH43SOJMHPFVD53Z","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"pith_short_8","alias_value":"XH43SOJM","created_at":"2026-07-05T06:15:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:XH43SOJMHPFVD53ZC7DJJOCMFK","target":"record","payload":{"canonical_record":{"source":{"id":"2305.18640","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2023-05-29T22:27:48Z","cross_cats_sorted":[],"title_canon_sha256":"183956c4222204a9af1e946e4745768a853f549962a7b566d0ae63fd4118b723","abstract_canon_sha256":"17d742d90604e657392b4a1dbdf39313b0015639bd4b61b42fb7265d5acee86e"},"schema_version":"1.0"},"canonical_sha256":"b9f9b9392c3bcb51f77917c694b84c2abf572f4fc7559ecdff0eac907198cf4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:15:03.230414Z","signature_b64":"fEAttZnXxGJaJiYBNd9OogTEorhTZtJsnvuibSrPa0xieV9rSok4k0gLTXBV1hOejPnf8keCAzQI4aG248YmCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b9f9b9392c3bcb51f77917c694b84c2abf572f4fc7559ecdff0eac907198cf4b","last_reissued_at":"2026-07-05T06:15:03.229952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:15:03.229952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.18640","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":"2026-07-05T06:15:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pDJDrB5bPmsk9HXAKKF8tQDb/SAFGJ+qqv03QeRhHd4U7g78JlnMTfdzBtOMueyu7sbzAW33zqSihl2/LBDeDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T23:36:23.695157Z"},"content_sha256":"e64397f6d8f76e17b272e2b2847adf9fd7f42f6fb90e888a4fef6151f93bad41","schema_version":"1.0","event_id":"sha256:e64397f6d8f76e17b272e2b2847adf9fd7f42f6fb90e888a4fef6151f93bad41"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:XH43SOJMHPFVD53ZC7DJJOCMFK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Arun Balaji Buduru, Orchid Chetia Phukan, Rajesh Sharma","submitted_at":"2023-05-29T22:27:48Z","abstract_excerpt":"Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields. A current trend in methods used for SER is to leverage embeddings from pre-trained models (PTMs) as input features to downstream models. However, the use of embeddings from speaker recognition PTMs hasn't garnered much focus in comparison to other PTM embeddings. To fill this gap and in order to understand the efficacy of speaker recognition PTM embeddings, we perform a comparative analysis of five PTM embeddings. Among all, x-vector embeddings performed the best possibly du"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18640","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/2305.18640/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":"2026-07-05T06:15:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GPVH7Lb2fLgBYClGglpWV+ee79i3Uvhjnw4MVjcLPRMfE67hDNWnowuZtX5cSXcImuC65zvEbcJZD3XnsgluBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T23:36:23.695589Z"},"content_sha256":"f8271397feea7e1fe4b1eb3738df55f735cef69fa2542c683cdd56a9438af3cb","schema_version":"1.0","event_id":"sha256:f8271397feea7e1fe4b1eb3738df55f735cef69fa2542c683cdd56a9438af3cb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XH43SOJMHPFVD53ZC7DJJOCMFK/bundle.json","state_url":"https://pith.science/pith/XH43SOJMHPFVD53ZC7DJJOCMFK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XH43SOJMHPFVD53ZC7DJJOCMFK/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":"2026-08-13T23:36:23Z","links":{"resolver":"https://pith.science/pith/XH43SOJMHPFVD53ZC7DJJOCMFK","bundle":"https://pith.science/pith/XH43SOJMHPFVD53ZC7DJJOCMFK/bundle.json","state":"https://pith.science/pith/XH43SOJMHPFVD53ZC7DJJOCMFK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XH43SOJMHPFVD53ZC7DJJOCMFK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:XH43SOJMHPFVD53ZC7DJJOCMFK","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":"17d742d90604e657392b4a1dbdf39313b0015639bd4b61b42fb7265d5acee86e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2023-05-29T22:27:48Z","title_canon_sha256":"183956c4222204a9af1e946e4745768a853f549962a7b566d0ae63fd4118b723"},"schema_version":"1.0","source":{"id":"2305.18640","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.18640","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"arxiv_version","alias_value":"2305.18640v1","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.18640","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"pith_short_12","alias_value":"XH43SOJMHPFV","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"pith_short_16","alias_value":"XH43SOJMHPFVD53Z","created_at":"2026-07-05T06:15:03Z"},{"alias_kind":"pith_short_8","alias_value":"XH43SOJM","created_at":"2026-07-05T06:15:03Z"}],"graph_snapshots":[{"event_id":"sha256:f8271397feea7e1fe4b1eb3738df55f735cef69fa2542c683cdd56a9438af3cb","target":"graph","created_at":"2026-07-05T06:15:03Z","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/2305.18640/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Speech emotion recognition (SER) is a field that has drawn a lot of attention due to its applications in diverse fields. A current trend in methods used for SER is to leverage embeddings from pre-trained models (PTMs) as input features to downstream models. However, the use of embeddings from speaker recognition PTMs hasn't garnered much focus in comparison to other PTM embeddings. To fill this gap and in order to understand the efficacy of speaker recognition PTM embeddings, we perform a comparative analysis of five PTM embeddings. Among all, x-vector embeddings performed the best possibly du","authors_text":"Arun Balaji Buduru, Orchid Chetia Phukan, Rajesh Sharma","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2023-05-29T22:27:48Z","title":"Transforming the Embeddings: A Lightweight Technique for Speech Emotion Recognition Tasks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.18640","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:e64397f6d8f76e17b272e2b2847adf9fd7f42f6fb90e888a4fef6151f93bad41","target":"record","created_at":"2026-07-05T06:15:03Z","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":"17d742d90604e657392b4a1dbdf39313b0015639bd4b61b42fb7265d5acee86e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.AS","submitted_at":"2023-05-29T22:27:48Z","title_canon_sha256":"183956c4222204a9af1e946e4745768a853f549962a7b566d0ae63fd4118b723"},"schema_version":"1.0","source":{"id":"2305.18640","kind":"arxiv","version":1}},"canonical_sha256":"b9f9b9392c3bcb51f77917c694b84c2abf572f4fc7559ecdff0eac907198cf4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b9f9b9392c3bcb51f77917c694b84c2abf572f4fc7559ecdff0eac907198cf4b","first_computed_at":"2026-07-05T06:15:03.229952Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:15:03.229952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fEAttZnXxGJaJiYBNd9OogTEorhTZtJsnvuibSrPa0xieV9rSok4k0gLTXBV1hOejPnf8keCAzQI4aG248YmCg==","signature_status":"signed_v1","signed_at":"2026-07-05T06:15:03.230414Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.18640","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e64397f6d8f76e17b272e2b2847adf9fd7f42f6fb90e888a4fef6151f93bad41","sha256:f8271397feea7e1fe4b1eb3738df55f735cef69fa2542c683cdd56a9438af3cb"],"state_sha256":"89ce87d9a4de879d46b518e69093f5018667b4ff7e873aed7f8e54f1022aaff8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"03OmkPG6sfju/96BZ+5beNRmbqgeYfSe2q/RiQ+soMGItZnl2S7QbWiQI+xazyY45/wU2IiIV7AVbysvbQgxCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T23:36:23.709792Z","bundle_sha256":"22bb14f3f34c9b9a059c874052fab97e1b3318c9fa7601a0a0ed1cecb357db3a"}}