{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:CVQYOPL36VB4KJY4ODA3VVFESG","short_pith_number":"pith:CVQYOPL3","canonical_record":{"source":{"id":"1901.02348","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-05T06:22:40Z","cross_cats_sorted":["cs.CL","cs.LG","cs.SD","stat.ML"],"title_canon_sha256":"3ab15f479094257584c2de6377a9581a0f1bfc4596f1abe21c79415af687a9a8","abstract_canon_sha256":"5c0067bf8f70545cdbc8fc2ec495a8afcf6fefff721057e83ea3310e4aaf6d20"},"schema_version":"1.0"},"canonical_sha256":"1561873d7bf543c5271c70c1bad4a4918215357eea3847397154d954490b7c0e","source":{"kind":"arxiv","id":"1901.02348","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02348","created_at":"2026-05-17T23:51:09Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02348v3","created_at":"2026-05-17T23:51:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02348","created_at":"2026-05-17T23:51:09Z"},{"alias_kind":"pith_short_12","alias_value":"CVQYOPL36VB4","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"CVQYOPL36VB4KJY4","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"CVQYOPL3","created_at":"2026-05-18T12:33:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:CVQYOPL36VB4KJY4ODA3VVFESG","target":"record","payload":{"canonical_record":{"source":{"id":"1901.02348","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-05T06:22:40Z","cross_cats_sorted":["cs.CL","cs.LG","cs.SD","stat.ML"],"title_canon_sha256":"3ab15f479094257584c2de6377a9581a0f1bfc4596f1abe21c79415af687a9a8","abstract_canon_sha256":"5c0067bf8f70545cdbc8fc2ec495a8afcf6fefff721057e83ea3310e4aaf6d20"},"schema_version":"1.0"},"canonical_sha256":"1561873d7bf543c5271c70c1bad4a4918215357eea3847397154d954490b7c0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:09.398098Z","signature_b64":"DRuEMijjrAQ/++9ERRrtFJbWN2cuBmnr9CxVMOnt+so2Slur3v2BEw9Pg3+c7E/3ge9hh0zhe8wByxmwDS55Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1561873d7bf543c5271c70c1bad4a4918215357eea3847397154d954490b7c0e","last_reissued_at":"2026-05-17T23:51:09.397584Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:09.397584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1901.02348","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:51:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"63MMV4GvuGV6Xs1B1X9jGziGL0GYLjjAYpuUdh5AQCOHvlJqrF6MjL4c5Ft3OOhA+b34SNaiXdmu77SEMaYNDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:43:35.727350Z"},"content_sha256":"e83285e741aad5f97ea341c48fd4d6f8c2889a7b58b3557260563d0a6ca99f87","schema_version":"1.0","event_id":"sha256:e83285e741aad5f97ea341c48fd4d6f8c2889a7b58b3557260563d0a6ca99f87"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:CVQYOPL36VB4KJY4ODA3VVFESG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG","cs.SD","stat.ML"],"primary_cat":"eess.AS","authors_text":"Anirudh Raju, Bj\\\"orn Hoffmeister, Kenichi Kumatani, Ladislav Mo\\v{s}ner, Minhua Wu, Roland Maas, Shiva Sundaram, Sree Hari Krishnan Parthasarathi","submitted_at":"2019-01-05T06:22:40Z","abstract_excerpt":"For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise. On top of that, we apply a logits selection method which only preserves the k highest values to prevent wrong emphasis of knowledge from the teacher and to reduce bandwidth needed for transferring data. We incorporate up to 8000 hours of untranscribed data for training and present our results on sequence trained models"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02348","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":""},"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":"2026-05-17T23:51:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ozmQZatXBziYL0fOupAKivlf1dA0Zo5zY9a302UUTATiumJhnXD7sN/djXqAtUlMn+/7j3jlubIAWw1py5DRCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T21:43:35.727999Z"},"content_sha256":"f9208d2bee356ff6ecb6db6f96baf4d6f5e31bace3246ed1fb88c44d586f8c02","schema_version":"1.0","event_id":"sha256:f9208d2bee356ff6ecb6db6f96baf4d6f5e31bace3246ed1fb88c44d586f8c02"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CVQYOPL36VB4KJY4ODA3VVFESG/bundle.json","state_url":"https://pith.science/pith/CVQYOPL36VB4KJY4ODA3VVFESG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CVQYOPL36VB4KJY4ODA3VVFESG/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":"2026-05-25T21:43:35Z","links":{"resolver":"https://pith.science/pith/CVQYOPL36VB4KJY4ODA3VVFESG","bundle":"https://pith.science/pith/CVQYOPL36VB4KJY4ODA3VVFESG/bundle.json","state":"https://pith.science/pith/CVQYOPL36VB4KJY4ODA3VVFESG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CVQYOPL36VB4KJY4ODA3VVFESG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:CVQYOPL36VB4KJY4ODA3VVFESG","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":"5c0067bf8f70545cdbc8fc2ec495a8afcf6fefff721057e83ea3310e4aaf6d20","cross_cats_sorted":["cs.CL","cs.LG","cs.SD","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-05T06:22:40Z","title_canon_sha256":"3ab15f479094257584c2de6377a9581a0f1bfc4596f1abe21c79415af687a9a8"},"schema_version":"1.0","source":{"id":"1901.02348","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1901.02348","created_at":"2026-05-17T23:51:09Z"},{"alias_kind":"arxiv_version","alias_value":"1901.02348v3","created_at":"2026-05-17T23:51:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1901.02348","created_at":"2026-05-17T23:51:09Z"},{"alias_kind":"pith_short_12","alias_value":"CVQYOPL36VB4","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_16","alias_value":"CVQYOPL36VB4KJY4","created_at":"2026-05-18T12:33:15Z"},{"alias_kind":"pith_short_8","alias_value":"CVQYOPL3","created_at":"2026-05-18T12:33:15Z"}],"graph_snapshots":[{"event_id":"sha256:f9208d2bee356ff6ecb6db6f96baf4d6f5e31bace3246ed1fb88c44d586f8c02","target":"graph","created_at":"2026-05-17T23:51:09Z","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"},"paper":{"abstract_excerpt":"For real-world speech recognition applications, noise robustness is still a challenge. In this work, we adopt the teacher-student (T/S) learning technique using a parallel clean and noisy corpus for improving automatic speech recognition (ASR) performance under multimedia noise. On top of that, we apply a logits selection method which only preserves the k highest values to prevent wrong emphasis of knowledge from the teacher and to reduce bandwidth needed for transferring data. We incorporate up to 8000 hours of untranscribed data for training and present our results on sequence trained models","authors_text":"Anirudh Raju, Bj\\\"orn Hoffmeister, Kenichi Kumatani, Ladislav Mo\\v{s}ner, Minhua Wu, Roland Maas, Shiva Sundaram, Sree Hari Krishnan Parthasarathi","cross_cats":["cs.CL","cs.LG","cs.SD","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-05T06:22:40Z","title":"Improving noise robustness of automatic speech recognition via parallel data and teacher-student learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1901.02348","kind":"arxiv","version":3},"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:e83285e741aad5f97ea341c48fd4d6f8c2889a7b58b3557260563d0a6ca99f87","target":"record","created_at":"2026-05-17T23:51:09Z","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":"5c0067bf8f70545cdbc8fc2ec495a8afcf6fefff721057e83ea3310e4aaf6d20","cross_cats_sorted":["cs.CL","cs.LG","cs.SD","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2019-01-05T06:22:40Z","title_canon_sha256":"3ab15f479094257584c2de6377a9581a0f1bfc4596f1abe21c79415af687a9a8"},"schema_version":"1.0","source":{"id":"1901.02348","kind":"arxiv","version":3}},"canonical_sha256":"1561873d7bf543c5271c70c1bad4a4918215357eea3847397154d954490b7c0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1561873d7bf543c5271c70c1bad4a4918215357eea3847397154d954490b7c0e","first_computed_at":"2026-05-17T23:51:09.397584Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:51:09.397584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DRuEMijjrAQ/++9ERRrtFJbWN2cuBmnr9CxVMOnt+so2Slur3v2BEw9Pg3+c7E/3ge9hh0zhe8wByxmwDS55Aw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:51:09.398098Z","signed_message":"canonical_sha256_bytes"},"source_id":"1901.02348","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e83285e741aad5f97ea341c48fd4d6f8c2889a7b58b3557260563d0a6ca99f87","sha256:f9208d2bee356ff6ecb6db6f96baf4d6f5e31bace3246ed1fb88c44d586f8c02"],"state_sha256":"c3e6e3ae51105fbcabd0df69d475c5e72be6e455879a86483b81ed8a171a5b43"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Uk2HE21viX8SbfBW4VV9mkcTBPHveUxay88SFIUAl2tsAJpFSZ5Ez2BFOCv9JSRE5uCaRez4unKW+BhQ6wmaCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T21:43:35.731999Z","bundle_sha256":"5e57001a110cf8e2eb0f1249a90d83973cb70cc436d9c2a6ebd9deda60a94156"}}