{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:PZN6CHOIQPUEBJKRE7ZHQN3REO","short_pith_number":"pith:PZN6CHOI","schema_version":"1.0","canonical_sha256":"7e5be11dc883e840a55127f2783771238546d485fc51607ee448d525fde29a7d","source":{"kind":"arxiv","id":"1808.10239","version":1},"attestation_state":"computed","paper":{"title":"Learning to adapt: a meta-learning approach for speaker adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Joachim Fainberg, Ond\\v{r}ej Klejch, Peter Bell","submitted_at":"2018-08-30T11:47:07Z","abstract_excerpt":"The performance of automatic speech recognition systems can be improved by adapting an acoustic model to compensate for the mismatch between training and testing conditions, for example by adapting to unseen speakers. The success of speaker adaptation methods relies on selecting weights that are suitable for adaptation and using good adaptation schedules to update these weights in order not to overfit to the adaptation data. In this paper we investigate a principled way of adapting all the weights of the acoustic model using a meta-learning. We show that the meta-learner can learn to perform s"},"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":"1808.10239","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-08-30T11:47:07Z","cross_cats_sorted":[],"title_canon_sha256":"9f9b9333dfe188a77fe9fb745691a06ccfe1933586e7e1ce938fb3ca2afe6be6","abstract_canon_sha256":"50f25126a0f858db603cdfbf501c5c503f24d262b566aadbd8d8a4bfb1858732"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:06:49.428222Z","signature_b64":"K+Jg+Z9Eayx1emE2ciofOv4DLXYfv4Sul25aCPmo9zbKGvHp7SAPTttMHdqCo+5fTuOth9q6epY7xTtLYkdvBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7e5be11dc883e840a55127f2783771238546d485fc51607ee448d525fde29a7d","last_reissued_at":"2026-05-18T00:06:49.427592Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:06:49.427592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning to adapt: a meta-learning approach for speaker adaptation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Joachim Fainberg, Ond\\v{r}ej Klejch, Peter Bell","submitted_at":"2018-08-30T11:47:07Z","abstract_excerpt":"The performance of automatic speech recognition systems can be improved by adapting an acoustic model to compensate for the mismatch between training and testing conditions, for example by adapting to unseen speakers. The success of speaker adaptation methods relies on selecting weights that are suitable for adaptation and using good adaptation schedules to update these weights in order not to overfit to the adaptation data. In this paper we investigate a principled way of adapting all the weights of the acoustic model using a meta-learning. We show that the meta-learner can learn to perform s"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.10239","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":""},"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":"1808.10239","created_at":"2026-05-18T00:06:49.427724+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.10239v1","created_at":"2026-05-18T00:06:49.427724+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.10239","created_at":"2026-05-18T00:06:49.427724+00:00"},{"alias_kind":"pith_short_12","alias_value":"PZN6CHOIQPUE","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"PZN6CHOIQPUEBJKR","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"PZN6CHOI","created_at":"2026-05-18T12:32:46.962924+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/PZN6CHOIQPUEBJKRE7ZHQN3REO","json":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO.json","graph_json":"https://pith.science/api/pith-number/PZN6CHOIQPUEBJKRE7ZHQN3REO/graph.json","events_json":"https://pith.science/api/pith-number/PZN6CHOIQPUEBJKRE7ZHQN3REO/events.json","paper":"https://pith.science/paper/PZN6CHOI"},"agent_actions":{"view_html":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO","download_json":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO.json","view_paper":"https://pith.science/paper/PZN6CHOI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.10239&json=true","fetch_graph":"https://pith.science/api/pith-number/PZN6CHOIQPUEBJKRE7ZHQN3REO/graph.json","fetch_events":"https://pith.science/api/pith-number/PZN6CHOIQPUEBJKRE7ZHQN3REO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO/action/storage_attestation","attest_author":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO/action/author_attestation","sign_citation":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO/action/citation_signature","submit_replication":"https://pith.science/pith/PZN6CHOIQPUEBJKRE7ZHQN3REO/action/replication_record"}},"created_at":"2026-05-18T00:06:49.427724+00:00","updated_at":"2026-05-18T00:06:49.427724+00:00"}