{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:DS4NTRONIXVO3RGPY42DZ5LKM3","short_pith_number":"pith:DS4NTRON","canonical_record":{"source":{"id":"2204.00291","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-01T08:53:44Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"a3549ed10e80a91f7e85e9ad36432be8a27b28df0355ac0a5e18b7da33495fe1","abstract_canon_sha256":"994354378f58e096ba25f609254c878386b84da7c2517529cb30d59f4343867c"},"schema_version":"1.0"},"canonical_sha256":"1cb8d9c5cd45eaedc4cfc7343cf56a66e857999c6804796b38df7057e8ebfde4","source":{"kind":"arxiv","id":"2204.00291","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.00291","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"arxiv_version","alias_value":"2204.00291v1","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.00291","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"pith_short_12","alias_value":"DS4NTRONIXVO","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"pith_short_16","alias_value":"DS4NTRONIXVO3RGP","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"pith_short_8","alias_value":"DS4NTRON","created_at":"2026-07-05T04:10:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:DS4NTRONIXVO3RGPY42DZ5LKM3","target":"record","payload":{"canonical_record":{"source":{"id":"2204.00291","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-01T08:53:44Z","cross_cats_sorted":["cs.SD","eess.AS"],"title_canon_sha256":"a3549ed10e80a91f7e85e9ad36432be8a27b28df0355ac0a5e18b7da33495fe1","abstract_canon_sha256":"994354378f58e096ba25f609254c878386b84da7c2517529cb30d59f4343867c"},"schema_version":"1.0"},"canonical_sha256":"1cb8d9c5cd45eaedc4cfc7343cf56a66e857999c6804796b38df7057e8ebfde4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:10:45.383739Z","signature_b64":"U2WCLYvMUSCh6W484PCRRWjcOSrYQ2Jodt+A54l6CiZgCQTQKtoQBVk/JI/E/bXOX0g6TTiVMu8Zf/ULZFtOBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1cb8d9c5cd45eaedc4cfc7343cf56a66e857999c6804796b38df7057e8ebfde4","last_reissued_at":"2026-07-05T04:10:45.383347Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:10:45.383347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.00291","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":"2026-07-05T04:10:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UkMKHAOALu/REC7thUifv5zlx/t2n+sglGnL5tCypbS3qbyEcwo7HJCrv19oKOqFfDFtk3liO2EkDcBIPc/oAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T20:11:49.957012Z"},"content_sha256":"6f48d3fcdb9a05748aee2aab29ae1e481efe7b4cb890b8a9273b65c5ad3d842c","schema_version":"1.0","event_id":"sha256:6f48d3fcdb9a05748aee2aab29ae1e481efe7b4cb890b8a9273b65c5ad3d842c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:DS4NTRONIXVO3RGPY42DZ5LKM3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Text-To-Speech Data Augmentation for Low Resource Speech Recognition","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.SD","eess.AS"],"primary_cat":"cs.CL","authors_text":"Rodolfo Zevallos","submitted_at":"2022-04-01T08:53:44Z","abstract_excerpt":"Nowadays, the main problem of deep learning techniques used in the development of automatic speech recognition (ASR) models is the lack of transcribed data. The goal of this research is to propose a new data augmentation method to improve ASR models for agglutinative and low-resource languages. This novel data augmentation method generates both synthetic text and synthetic audio. Some experiments were conducted using the corpus of the Quechua language, which is an agglutinative and low-resource language. In this study, a sequence-to-sequence (seq2seq) model was applied to generate synthetic te"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.00291","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/2204.00291/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":"2026-07-05T04:10:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UGdCFR14nDoTnJpv/OyfNp0BY5LeFa510zSPSDCmMy8Q2dnSYl6Cuni6Vd2bfNPo431GVj5XR99rS12J87ckBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-25T20:11:49.957393Z"},"content_sha256":"97616967d1052dec8ce0500bda896277020d93a214ad6a4096922d2dbc5f4758","schema_version":"1.0","event_id":"sha256:97616967d1052dec8ce0500bda896277020d93a214ad6a4096922d2dbc5f4758"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DS4NTRONIXVO3RGPY42DZ5LKM3/bundle.json","state_url":"https://pith.science/pith/DS4NTRONIXVO3RGPY42DZ5LKM3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DS4NTRONIXVO3RGPY42DZ5LKM3/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":"2026-07-25T20:11:49Z","links":{"resolver":"https://pith.science/pith/DS4NTRONIXVO3RGPY42DZ5LKM3","bundle":"https://pith.science/pith/DS4NTRONIXVO3RGPY42DZ5LKM3/bundle.json","state":"https://pith.science/pith/DS4NTRONIXVO3RGPY42DZ5LKM3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DS4NTRONIXVO3RGPY42DZ5LKM3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:DS4NTRONIXVO3RGPY42DZ5LKM3","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":"994354378f58e096ba25f609254c878386b84da7c2517529cb30d59f4343867c","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-01T08:53:44Z","title_canon_sha256":"a3549ed10e80a91f7e85e9ad36432be8a27b28df0355ac0a5e18b7da33495fe1"},"schema_version":"1.0","source":{"id":"2204.00291","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.00291","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"arxiv_version","alias_value":"2204.00291v1","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.00291","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"pith_short_12","alias_value":"DS4NTRONIXVO","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"pith_short_16","alias_value":"DS4NTRONIXVO3RGP","created_at":"2026-07-05T04:10:45Z"},{"alias_kind":"pith_short_8","alias_value":"DS4NTRON","created_at":"2026-07-05T04:10:45Z"}],"graph_snapshots":[{"event_id":"sha256:97616967d1052dec8ce0500bda896277020d93a214ad6a4096922d2dbc5f4758","target":"graph","created_at":"2026-07-05T04:10:45Z","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/2204.00291/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Nowadays, the main problem of deep learning techniques used in the development of automatic speech recognition (ASR) models is the lack of transcribed data. The goal of this research is to propose a new data augmentation method to improve ASR models for agglutinative and low-resource languages. This novel data augmentation method generates both synthetic text and synthetic audio. Some experiments were conducted using the corpus of the Quechua language, which is an agglutinative and low-resource language. In this study, a sequence-to-sequence (seq2seq) model was applied to generate synthetic te","authors_text":"Rodolfo Zevallos","cross_cats":["cs.SD","eess.AS"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-01T08:53:44Z","title":"Text-To-Speech Data Augmentation for Low Resource Speech Recognition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.00291","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":"sha256:6f48d3fcdb9a05748aee2aab29ae1e481efe7b4cb890b8a9273b65c5ad3d842c","target":"record","created_at":"2026-07-05T04:10:45Z","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":"994354378f58e096ba25f609254c878386b84da7c2517529cb30d59f4343867c","cross_cats_sorted":["cs.SD","eess.AS"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2022-04-01T08:53:44Z","title_canon_sha256":"a3549ed10e80a91f7e85e9ad36432be8a27b28df0355ac0a5e18b7da33495fe1"},"schema_version":"1.0","source":{"id":"2204.00291","kind":"arxiv","version":1}},"canonical_sha256":"1cb8d9c5cd45eaedc4cfc7343cf56a66e857999c6804796b38df7057e8ebfde4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1cb8d9c5cd45eaedc4cfc7343cf56a66e857999c6804796b38df7057e8ebfde4","first_computed_at":"2026-07-05T04:10:45.383347Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:10:45.383347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U2WCLYvMUSCh6W484PCRRWjcOSrYQ2Jodt+A54l6CiZgCQTQKtoQBVk/JI/E/bXOX0g6TTiVMu8Zf/ULZFtOBw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:10:45.383739Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.00291","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f48d3fcdb9a05748aee2aab29ae1e481efe7b4cb890b8a9273b65c5ad3d842c","sha256:97616967d1052dec8ce0500bda896277020d93a214ad6a4096922d2dbc5f4758"],"state_sha256":"e48175901f7c6ed1da346901ef9bb7ba6513aaef22e3c8a6b63f5360e549a4ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LzTDErkM+c1nwJWEs492hmGwf2ePKZLH5iLEh2PG/RVa+2EdRAMD3shdHY6z8Q2ymjCLhUETOpKdP+HKZTgkBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-25T20:11:49.959561Z","bundle_sha256":"a9bd72133b2b3b92a9439c910bc5c1d19704dfd8d483da9a9b4b23a27114c890"}}