{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LUH5QCOWH64AJDHDFEVA7OROJ3","short_pith_number":"pith:LUH5QCOW","schema_version":"1.0","canonical_sha256":"5d0fd809d63fb8048ce3292a0fba2e4ef9a9323760e6536c5bdfd396462cc32d","source":{"kind":"arxiv","id":"2606.11836","version":1},"attestation_state":"computed","paper":{"title":"Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Chengxi Deng, Haoning Xu, Huimeng Wang, Mengzhe Geng, Xunying Liu, Youjun Chen, Zhaoqing Li","submitted_at":"2026-06-10T09:16:29Z","abstract_excerpt":"This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means. More fine-grained, mixed sparsity pruning by layer-level varying number of parameter clusters is also explored. Experiments conducted on the LibriSpeech dataset suggest that when operating with pruning sparsity of 50% on HuBERT-large, consistent WER reductions of 27.73%/18.61% absolute (34.37%/21.91% relative) over the magnitude-based pruning were obtained on the test-clean and test-other subsets before fine-tuning and 0.19%/0.79% absolute (3.36%/4"},"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":"2606.11836","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-10T09:16:29Z","cross_cats_sorted":["cs.AI","eess.AS"],"title_canon_sha256":"84d268bfa71700ea6f07fa8150f93465269eef7d9a20ba5dcf7a0c07da42bc8e","abstract_canon_sha256":"724b99219c1f85462970cb40b4275dd64a102b1086269de6e09306772c4ebf96"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:10:10.881618Z","signature_b64":"+O/wA10OqX4hxKT/i+LLnMjzSBZii42q0OxU/CXKgqLYQkTUyEs6lf34dp5Jn/7YXoGX8kXLhc1CVsygC0YUCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d0fd809d63fb8048ce3292a0fba2e4ef9a9323760e6536c5bdfd396462cc32d","last_reissued_at":"2026-06-11T01:10:10.880789Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:10:10.880789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","eess.AS"],"primary_cat":"cs.SD","authors_text":"Chengxi Deng, Haoning Xu, Huimeng Wang, Mengzhe Geng, Xunying Liu, Youjun Chen, Zhaoqing Li","submitted_at":"2026-06-10T09:16:29Z","abstract_excerpt":"This paper presents a novel data-free and training-free compression approach for speech foundation models using channelwise clustering via k-means. More fine-grained, mixed sparsity pruning by layer-level varying number of parameter clusters is also explored. Experiments conducted on the LibriSpeech dataset suggest that when operating with pruning sparsity of 50% on HuBERT-large, consistent WER reductions of 27.73%/18.61% absolute (34.37%/21.91% relative) over the magnitude-based pruning were obtained on the test-clean and test-other subsets before fine-tuning and 0.19%/0.79% absolute (3.36%/4"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11836","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/2606.11836/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":"2606.11836","created_at":"2026-06-11T01:10:10.880916+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11836v1","created_at":"2026-06-11T01:10:10.880916+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11836","created_at":"2026-06-11T01:10:10.880916+00:00"},{"alias_kind":"pith_short_12","alias_value":"LUH5QCOWH64A","created_at":"2026-06-11T01:10:10.880916+00:00"},{"alias_kind":"pith_short_16","alias_value":"LUH5QCOWH64AJDHD","created_at":"2026-06-11T01:10:10.880916+00:00"},{"alias_kind":"pith_short_8","alias_value":"LUH5QCOW","created_at":"2026-06-11T01:10:10.880916+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2606.11836","citing_title":"Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3","json":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3.json","graph_json":"https://pith.science/api/pith-number/LUH5QCOWH64AJDHDFEVA7OROJ3/graph.json","events_json":"https://pith.science/api/pith-number/LUH5QCOWH64AJDHDFEVA7OROJ3/events.json","paper":"https://pith.science/paper/LUH5QCOW"},"agent_actions":{"view_html":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3","download_json":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3.json","view_paper":"https://pith.science/paper/LUH5QCOW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11836&json=true","fetch_graph":"https://pith.science/api/pith-number/LUH5QCOWH64AJDHDFEVA7OROJ3/graph.json","fetch_events":"https://pith.science/api/pith-number/LUH5QCOWH64AJDHDFEVA7OROJ3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3/action/storage_attestation","attest_author":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3/action/author_attestation","sign_citation":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3/action/citation_signature","submit_replication":"https://pith.science/pith/LUH5QCOWH64AJDHDFEVA7OROJ3/action/replication_record"}},"created_at":"2026-06-11T01:10:10.880916+00:00","updated_at":"2026-06-11T01:10:10.880916+00:00"}