{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:OLMV7YWKXE5KU4ZQGYSAZAE3V3","short_pith_number":"pith:OLMV7YWK","canonical_record":{"source":{"id":"2210.16611","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-10-29T14:22:43Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"5b31b50f7f8fb0d289c1a0617f4ffa63cae3fb553450d396a17d2516f720e716","abstract_canon_sha256":"d05c54e80a401d783eff9e39f0872715d790494a37cb248fe8b26fccc06c7b1c"},"schema_version":"1.0"},"canonical_sha256":"72d95fe2cab93aaa733036240c809baed9a91bd45acd556fd1571288115decae","source":{"kind":"arxiv","id":"2210.16611","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.16611","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"arxiv_version","alias_value":"2210.16611v2","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16611","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"pith_short_12","alias_value":"OLMV7YWKXE5K","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"pith_short_16","alias_value":"OLMV7YWKXE5KU4ZQ","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"pith_short_8","alias_value":"OLMV7YWK","created_at":"2026-07-05T06:11:42Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:OLMV7YWKXE5KU4ZQGYSAZAE3V3","target":"record","payload":{"canonical_record":{"source":{"id":"2210.16611","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-10-29T14:22:43Z","cross_cats_sorted":["cs.CL","cs.SD"],"title_canon_sha256":"5b31b50f7f8fb0d289c1a0617f4ffa63cae3fb553450d396a17d2516f720e716","abstract_canon_sha256":"d05c54e80a401d783eff9e39f0872715d790494a37cb248fe8b26fccc06c7b1c"},"schema_version":"1.0"},"canonical_sha256":"72d95fe2cab93aaa733036240c809baed9a91bd45acd556fd1571288115decae","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:11:42.974201Z","signature_b64":"6ZELp4LQu+Bj/4VBit3Blh/qkOMvRSKL86ML59iOGE+a1j1x/bZnNjGrK9erFw9FfdCVNLqTX1qwZAO6c6rlBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"72d95fe2cab93aaa733036240c809baed9a91bd45acd556fd1571288115decae","last_reissued_at":"2026-07-05T06:11:42.973770Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:11:42.973770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.16611","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:11:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"99QURLyLxfmCnljAaFsw0jOJCG924zyMzU0BaWpqYF/N0XvEmXv1hppx5OJe0z0Hoep+Smyg3F1UusnnjZruAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:25:48.522532Z"},"content_sha256":"9bd3995b04384b31a9f82831ddc3ed9c93f49944b57a45d73a0dfab1983b77ac","schema_version":"1.0","event_id":"sha256:9bd3995b04384b31a9f82831ddc3ed9c93f49944b57a45d73a0dfab1983b77ac"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:OLMV7YWKXE5KU4ZQGYSAZAE3V3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Application of Knowledge Distillation to Multi-task Speech Representation Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.SD"],"primary_cat":"eess.AS","authors_text":"Erik Visser, Mine Kerpicci, Shuhua Zhang, Van Nguyen","submitted_at":"2022-10-29T14:22:43Z","abstract_excerpt":"Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a large number of parameters, the smallest version of which has 95 million parameters. This constitutes a challenge for edge AI device deployments. In this paper, we investigate the application of knowledge distillation to speech representation learning (SRL) models followed by join"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16611","kind":"arxiv","version":2},"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/2210.16611/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":"2026-07-05T06:11:42Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0H5r1uKcnckYkO3hMtTU2fnCsTc1lEMslwCq5kLbcQivykwDVD+Lb9o1yt52REpZIg4Y7WrXxT76trmJHdebAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:25:48.523436Z"},"content_sha256":"963a0bbbe707754284bb0f6631cbce558d34c399982257cff9b86ae6f1187c46","schema_version":"1.0","event_id":"sha256:963a0bbbe707754284bb0f6631cbce558d34c399982257cff9b86ae6f1187c46"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3/bundle.json","state_url":"https://pith.science/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3/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":"2026-08-04T20:25:48Z","links":{"resolver":"https://pith.science/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3","bundle":"https://pith.science/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3/bundle.json","state":"https://pith.science/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OLMV7YWKXE5KU4ZQGYSAZAE3V3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:OLMV7YWKXE5KU4ZQGYSAZAE3V3","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":"d05c54e80a401d783eff9e39f0872715d790494a37cb248fe8b26fccc06c7b1c","cross_cats_sorted":["cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-10-29T14:22:43Z","title_canon_sha256":"5b31b50f7f8fb0d289c1a0617f4ffa63cae3fb553450d396a17d2516f720e716"},"schema_version":"1.0","source":{"id":"2210.16611","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.16611","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"arxiv_version","alias_value":"2210.16611v2","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16611","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"pith_short_12","alias_value":"OLMV7YWKXE5K","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"pith_short_16","alias_value":"OLMV7YWKXE5KU4ZQ","created_at":"2026-07-05T06:11:42Z"},{"alias_kind":"pith_short_8","alias_value":"OLMV7YWK","created_at":"2026-07-05T06:11:42Z"}],"graph_snapshots":[{"event_id":"sha256:963a0bbbe707754284bb0f6631cbce558d34c399982257cff9b86ae6f1187c46","target":"graph","created_at":"2026-07-05T06:11:42Z","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/2210.16611/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model architectures such as wav2vec 2.0 and HuBERT have been proposed to learn speech representations from audio waveforms in a self-supervised manner. When they are combined with downstream tasks such as keyword spotting and speaker verification, they provide state-of-the-art performance. However, these models use a large number of parameters, the smallest version of which has 95 million parameters. This constitutes a challenge for edge AI device deployments. In this paper, we investigate the application of knowledge distillation to speech representation learning (SRL) models followed by join","authors_text":"Erik Visser, Mine Kerpicci, Shuhua Zhang, Van Nguyen","cross_cats":["cs.CL","cs.SD"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-10-29T14:22:43Z","title":"Application of Knowledge Distillation to Multi-task Speech Representation Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16611","kind":"arxiv","version":2},"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":"sha256:9bd3995b04384b31a9f82831ddc3ed9c93f49944b57a45d73a0dfab1983b77ac","target":"record","created_at":"2026-07-05T06:11:42Z","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":"d05c54e80a401d783eff9e39f0872715d790494a37cb248fe8b26fccc06c7b1c","cross_cats_sorted":["cs.CL","cs.SD"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.AS","submitted_at":"2022-10-29T14:22:43Z","title_canon_sha256":"5b31b50f7f8fb0d289c1a0617f4ffa63cae3fb553450d396a17d2516f720e716"},"schema_version":"1.0","source":{"id":"2210.16611","kind":"arxiv","version":2}},"canonical_sha256":"72d95fe2cab93aaa733036240c809baed9a91bd45acd556fd1571288115decae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"72d95fe2cab93aaa733036240c809baed9a91bd45acd556fd1571288115decae","first_computed_at":"2026-07-05T06:11:42.973770Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:11:42.973770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6ZELp4LQu+Bj/4VBit3Blh/qkOMvRSKL86ML59iOGE+a1j1x/bZnNjGrK9erFw9FfdCVNLqTX1qwZAO6c6rlBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:11:42.974201Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.16611","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9bd3995b04384b31a9f82831ddc3ed9c93f49944b57a45d73a0dfab1983b77ac","sha256:963a0bbbe707754284bb0f6631cbce558d34c399982257cff9b86ae6f1187c46"],"state_sha256":"b8fda895c88167c585712134fe3c389b4934cceff418cd5aea3255fb6b2b9d4f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GTG8xm+jclgtI3XQh3IkpEnTufYSkka992AjzXBGOJVhoj4cErvyrusg9NSdAWkw+R//ilgYSYRUYNKrW4b3Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T20:25:48.539410Z","bundle_sha256":"979aa4342d07dc7b69f9fc87a26713cc1ae4568bd906ee62a6d3c7f580de4e73"}}