{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:OIYARRPC2J7BZU2GPDOT2ZCSMD","short_pith_number":"pith:OIYARRPC","schema_version":"1.0","canonical_sha256":"723008c5e2d27e1cd34678dd3d645260e2ec5354853724a1f8682c02e0f979c4","source":{"kind":"arxiv","id":"1812.10233","version":3},"attestation_state":"computed","paper":{"title":"An Investigation of Few-Shot Learning in Spoken Term Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Lifeng Shang, Qing Li, Tom Ko, Xiao Chen, Xin Jiang, Yangbin Chen","submitted_at":"2018-12-26T05:43:23Z","abstract_excerpt":"In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task. We formulate a user-defined scenario of spoken term classification as a few-shot learning problem. In most few-shot learning studies, it is assumed that all the N classes are new in a N-way problem. We suggest that this assumption can be relaxed and define a N+M-way problem where N and M are the number of new classes and fixed classes respectively. We propose a modification to the Model-Agnostic Meta-Learning (MAML) algorithm to solve the problem. Experiments on the Google Speech Commands d"},"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":"1812.10233","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-12-26T05:43:23Z","cross_cats_sorted":["cs.IR"],"title_canon_sha256":"45e096cc3f17b0a4a02777264752b3038e67204d36cf82b38556fcd7fff9ff40","abstract_canon_sha256":"fbcf39fb3670f3d4f3fd42f1d7d53cf541f1bd3946232a4ec325a032dbd2d258"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:34:48.032172Z","signature_b64":"clSfiXCdeNQYadaufT9tQjK5rDg0YCiEoYzQlImKW7y66CClLOOP2u9eoaatjTijAzZje1z9c5wQRxvcUKZcBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"723008c5e2d27e1cd34678dd3d645260e2ec5354853724a1f8682c02e0f979c4","last_reissued_at":"2026-07-05T01:34:48.031688Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:34:48.031688Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Investigation of Few-Shot Learning in Spoken Term Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IR"],"primary_cat":"cs.CL","authors_text":"Lifeng Shang, Qing Li, Tom Ko, Xiao Chen, Xin Jiang, Yangbin Chen","submitted_at":"2018-12-26T05:43:23Z","abstract_excerpt":"In this paper, we investigate the feasibility of applying few-shot learning algorithms to a speech task. We formulate a user-defined scenario of spoken term classification as a few-shot learning problem. In most few-shot learning studies, it is assumed that all the N classes are new in a N-way problem. We suggest that this assumption can be relaxed and define a N+M-way problem where N and M are the number of new classes and fixed classes respectively. We propose a modification to the Model-Agnostic Meta-Learning (MAML) algorithm to solve the problem. Experiments on the Google Speech Commands d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1812.10233","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/1812.10233/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":"1812.10233","created_at":"2026-07-05T01:34:48.031749+00:00"},{"alias_kind":"arxiv_version","alias_value":"1812.10233v3","created_at":"2026-07-05T01:34:48.031749+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1812.10233","created_at":"2026-07-05T01:34:48.031749+00:00"},{"alias_kind":"pith_short_12","alias_value":"OIYARRPC2J7B","created_at":"2026-07-05T01:34:48.031749+00:00"},{"alias_kind":"pith_short_16","alias_value":"OIYARRPC2J7BZU2G","created_at":"2026-07-05T01:34:48.031749+00:00"},{"alias_kind":"pith_short_8","alias_value":"OIYARRPC","created_at":"2026-07-05T01:34:48.031749+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD","json":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD.json","graph_json":"https://pith.science/api/pith-number/OIYARRPC2J7BZU2GPDOT2ZCSMD/graph.json","events_json":"https://pith.science/api/pith-number/OIYARRPC2J7BZU2GPDOT2ZCSMD/events.json","paper":"https://pith.science/paper/OIYARRPC"},"agent_actions":{"view_html":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD","download_json":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD.json","view_paper":"https://pith.science/paper/OIYARRPC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1812.10233&json=true","fetch_graph":"https://pith.science/api/pith-number/OIYARRPC2J7BZU2GPDOT2ZCSMD/graph.json","fetch_events":"https://pith.science/api/pith-number/OIYARRPC2J7BZU2GPDOT2ZCSMD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD/action/storage_attestation","attest_author":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD/action/author_attestation","sign_citation":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD/action/citation_signature","submit_replication":"https://pith.science/pith/OIYARRPC2J7BZU2GPDOT2ZCSMD/action/replication_record"}},"created_at":"2026-07-05T01:34:48.031749+00:00","updated_at":"2026-07-05T01:34:48.031749+00:00"}