{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LQ5A4IYFMYTIPBSK2DAUYCDVJY","short_pith_number":"pith:LQ5A4IYF","canonical_record":{"source":{"id":"2502.05356","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.AS","submitted_at":"2025-02-07T22:08:12Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"236de149e0bc2810f951e9eff9c213b0deddd11514ffa738c5f635e32c7cafae","abstract_canon_sha256":"246620e2f9f22e2035c251d75f12a3c549f189b077976e0f4a6e067bd5cb0665"},"schema_version":"1.0"},"canonical_sha256":"5c3a0e2305662687864ad0c14c08754e3fc51275213b2ef572394ed4fb2eb0da","source":{"kind":"arxiv","id":"2502.05356","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05356","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05356v1","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05356","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"pith_short_12","alias_value":"LQ5A4IYFMYTI","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"pith_short_16","alias_value":"LQ5A4IYFMYTIPBSK","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"pith_short_8","alias_value":"LQ5A4IYF","created_at":"2026-07-05T10:11:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LQ5A4IYFMYTIPBSK2DAUYCDVJY","target":"record","payload":{"canonical_record":{"source":{"id":"2502.05356","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.AS","submitted_at":"2025-02-07T22:08:12Z","cross_cats_sorted":["cs.SD"],"title_canon_sha256":"236de149e0bc2810f951e9eff9c213b0deddd11514ffa738c5f635e32c7cafae","abstract_canon_sha256":"246620e2f9f22e2035c251d75f12a3c549f189b077976e0f4a6e067bd5cb0665"},"schema_version":"1.0"},"canonical_sha256":"5c3a0e2305662687864ad0c14c08754e3fc51275213b2ef572394ed4fb2eb0da","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:36.827694Z","signature_b64":"mnoS9ajp0LUtjQb33luG90DxD7g8PcV73sHLaFBiRgoeN7HIgPJxn1b9uDqm8xN7DkakRxub7xEY/EVjODTgDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c3a0e2305662687864ad0c14c08754e3fc51275213b2ef572394ed4fb2eb0da","last_reissued_at":"2026-07-05T10:11:36.827237Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:36.827237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.05356","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:11:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1RIxNKCh2Y282OAaJjyyqLq1rKnaLV5sZuli2c+OSTsmPUeKvikoVS9HKAvNs1W7xp1lPNoo8Fn89oFUNvWTCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:21:59.070696Z"},"content_sha256":"01ab5d074d45fcb9aef019e17bce8516fcfbca75bbb62e0a4b6474e7df4863db","schema_version":"1.0","event_id":"sha256:01ab5d074d45fcb9aef019e17bce8516fcfbca75bbb62e0a4b6474e7df4863db"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LQ5A4IYFMYTIPBSK2DAUYCDVJY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.SD"],"primary_cat":"eess.AS","authors_text":"Benjamin Stahl, Hannes Gamper","submitted_at":"2025-02-07T22:08:12Z","abstract_excerpt":"In this paper, we investigate distillation and pruning methods to reduce model size for non-intrusive speech quality assessment based on self-supervised representations. Our experiments build on XLS-R-SQA, a speech quality assessment model using wav2vec 2.0 XLS-R embeddings. We retrain this model on a large compilation of mean opinion score datasets, encompassing over 100,000 labeled clips. For distillation, using this model as a teacher, we generate pseudo-labels on unlabeled degraded speech signals and train student models of varying sizes. For pruning, we use a data-driven strategy. While d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05356","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.05356/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":"2026-07-05T10:11:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VAABuPNDtJF3HK/i+fnZp9wsY62tVuNbKGyFLpFCDjkbY3ua3NwYChH4M8fJTUEPHDUv2x8FZF41x4WNclXyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T11:21:59.071245Z"},"content_sha256":"252851bc2a69e800c9e9ea80a35e28465b6e38c586d36749089ff376117c34ff","schema_version":"1.0","event_id":"sha256:252851bc2a69e800c9e9ea80a35e28465b6e38c586d36749089ff376117c34ff"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY/bundle.json","state_url":"https://pith.science/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY/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":"2026-08-09T11:21:59Z","links":{"resolver":"https://pith.science/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY","bundle":"https://pith.science/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY/bundle.json","state":"https://pith.science/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LQ5A4IYFMYTIPBSK2DAUYCDVJY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LQ5A4IYFMYTIPBSK2DAUYCDVJY","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":"246620e2f9f22e2035c251d75f12a3c549f189b077976e0f4a6e067bd5cb0665","cross_cats_sorted":["cs.SD"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.AS","submitted_at":"2025-02-07T22:08:12Z","title_canon_sha256":"236de149e0bc2810f951e9eff9c213b0deddd11514ffa738c5f635e32c7cafae"},"schema_version":"1.0","source":{"id":"2502.05356","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.05356","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"arxiv_version","alias_value":"2502.05356v1","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.05356","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"pith_short_12","alias_value":"LQ5A4IYFMYTI","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"pith_short_16","alias_value":"LQ5A4IYFMYTIPBSK","created_at":"2026-07-05T10:11:36Z"},{"alias_kind":"pith_short_8","alias_value":"LQ5A4IYF","created_at":"2026-07-05T10:11:36Z"}],"graph_snapshots":[{"event_id":"sha256:252851bc2a69e800c9e9ea80a35e28465b6e38c586d36749089ff376117c34ff","target":"graph","created_at":"2026-07-05T10:11:36Z","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/2502.05356/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we investigate distillation and pruning methods to reduce model size for non-intrusive speech quality assessment based on self-supervised representations. Our experiments build on XLS-R-SQA, a speech quality assessment model using wav2vec 2.0 XLS-R embeddings. We retrain this model on a large compilation of mean opinion score datasets, encompassing over 100,000 labeled clips. For distillation, using this model as a teacher, we generate pseudo-labels on unlabeled degraded speech signals and train student models of varying sizes. For pruning, we use a data-driven strategy. While d","authors_text":"Benjamin Stahl, Hannes Gamper","cross_cats":["cs.SD"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.AS","submitted_at":"2025-02-07T22:08:12Z","title":"Distillation and Pruning for Scalable Self-Supervised Representation-Based Speech Quality Assessment"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.05356","kind":"arxiv","version":1},"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":"sha256:01ab5d074d45fcb9aef019e17bce8516fcfbca75bbb62e0a4b6474e7df4863db","target":"record","created_at":"2026-07-05T10:11:36Z","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":"246620e2f9f22e2035c251d75f12a3c549f189b077976e0f4a6e067bd5cb0665","cross_cats_sorted":["cs.SD"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"eess.AS","submitted_at":"2025-02-07T22:08:12Z","title_canon_sha256":"236de149e0bc2810f951e9eff9c213b0deddd11514ffa738c5f635e32c7cafae"},"schema_version":"1.0","source":{"id":"2502.05356","kind":"arxiv","version":1}},"canonical_sha256":"5c3a0e2305662687864ad0c14c08754e3fc51275213b2ef572394ed4fb2eb0da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5c3a0e2305662687864ad0c14c08754e3fc51275213b2ef572394ed4fb2eb0da","first_computed_at":"2026-07-05T10:11:36.827237Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:11:36.827237Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"mnoS9ajp0LUtjQb33luG90DxD7g8PcV73sHLaFBiRgoeN7HIgPJxn1b9uDqm8xN7DkakRxub7xEY/EVjODTgDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T10:11:36.827694Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.05356","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:01ab5d074d45fcb9aef019e17bce8516fcfbca75bbb62e0a4b6474e7df4863db","sha256:252851bc2a69e800c9e9ea80a35e28465b6e38c586d36749089ff376117c34ff"],"state_sha256":"a2a0eec5bf2b6d2b72b47aac4c5ea118ec5822ba3aa85080bf542fae2f95b9e6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IRgk9GJiWIYUwrwMtY+KDdU9iIRowTG0sjTjXegnBEiAZ5RP7QggdeDyt21skHesA1VJ4ijthUbYvjxPOjtdAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T11:21:59.076906Z","bundle_sha256":"b8176f7a284ddd6713e41b0904c65e2d71262495920037ee60a19009a9696c43"}}