{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:7LDKCMTCJPNREVZ5PCBVIEMOTZ","short_pith_number":"pith:7LDKCMTC","schema_version":"1.0","canonical_sha256":"fac6a132624bdb12573d788354118e9e55385e43eb8c55ba08c8f4bc5efab227","source":{"kind":"arxiv","id":"2305.00359","version":3},"attestation_state":"computed","paper":{"title":"A Review of Deep Learning Techniques for Speech Processing","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Ambuj Mehrish, Navonil Majumder, Rada Mihalcea, Rishabh Bhardwaj, Soujanya Poria","submitted_at":"2023-04-30T00:17:42Z","abstract_excerpt":"The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, wi"},"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":"2305.00359","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.AS","submitted_at":"2023-04-30T00:17:42Z","cross_cats_sorted":[],"title_canon_sha256":"acc04492e5ad5a6f3fb98e1b92fa65678b606c0f98b873fc31505fad3ef113a4","abstract_canon_sha256":"74471bda7cef3ad5ee664c9258c7b629e01f6d9b795b94686092f41c6a8482fb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:58.216233Z","signature_b64":"/jOAc5vxPH3Tq8xwnpuSy1LldPQZx1Eo6RNrqFC6YuQ3Ag/hhrbFC9LiACKkTylRrYRKKF04eZT/aq2PaMNJBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fac6a132624bdb12573d788354118e9e55385e43eb8c55ba08c8f4bc5efab227","last_reissued_at":"2026-07-05T06:14:58.215749Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:58.215749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Review of Deep Learning Techniques for Speech Processing","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.AS","authors_text":"Ambuj Mehrish, Navonil Majumder, Rada Mihalcea, Rishabh Bhardwaj, Soujanya Poria","submitted_at":"2023-04-30T00:17:42Z","abstract_excerpt":"The field of speech processing has undergone a transformative shift with the advent of deep learning. The use of multiple processing layers has enabled the creation of models capable of extracting intricate features from speech data. This development has paved the way for unparalleled advancements in speech recognition, text-to-speech synthesis, automatic speech recognition, and emotion recognition, propelling the performance of these tasks to unprecedented heights. The power of deep learning techniques has opened up new avenues for research and innovation in the field of speech processing, wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.00359","kind":"arxiv","version":3},"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/2305.00359/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":"2305.00359","created_at":"2026-07-05T06:14:58.215802+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.00359v3","created_at":"2026-07-05T06:14:58.215802+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.00359","created_at":"2026-07-05T06:14:58.215802+00:00"},{"alias_kind":"pith_short_12","alias_value":"7LDKCMTCJPNR","created_at":"2026-07-05T06:14:58.215802+00:00"},{"alias_kind":"pith_short_16","alias_value":"7LDKCMTCJPNREVZ5","created_at":"2026-07-05T06:14:58.215802+00:00"},{"alias_kind":"pith_short_8","alias_value":"7LDKCMTC","created_at":"2026-07-05T06:14:58.215802+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2302.03286","citing_title":"Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations","ref_index":66,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ","json":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ.json","graph_json":"https://pith.science/api/pith-number/7LDKCMTCJPNREVZ5PCBVIEMOTZ/graph.json","events_json":"https://pith.science/api/pith-number/7LDKCMTCJPNREVZ5PCBVIEMOTZ/events.json","paper":"https://pith.science/paper/7LDKCMTC"},"agent_actions":{"view_html":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ","download_json":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ.json","view_paper":"https://pith.science/paper/7LDKCMTC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.00359&json=true","fetch_graph":"https://pith.science/api/pith-number/7LDKCMTCJPNREVZ5PCBVIEMOTZ/graph.json","fetch_events":"https://pith.science/api/pith-number/7LDKCMTCJPNREVZ5PCBVIEMOTZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ/action/storage_attestation","attest_author":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ/action/author_attestation","sign_citation":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ/action/citation_signature","submit_replication":"https://pith.science/pith/7LDKCMTCJPNREVZ5PCBVIEMOTZ/action/replication_record"}},"created_at":"2026-07-05T06:14:58.215802+00:00","updated_at":"2026-07-05T06:14:58.215802+00:00"}