{"id":"https://openalex.org/W7128645916","doi":"https://doi.org/10.48550/arxiv.2602.09040","title":"Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures","display_name":"Soft Clustering Anchors for Self-Supervised Speech Representation Learning in Joint Embedding Prediction Architectures","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7128645916","doi":"https://doi.org/10.48550/arxiv.2602.09040"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.09040","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5073370625","display_name":"\u0393\u03b5\u03ce\u03c1\u03b3\u03b9\u03bf\u03c2 \u0399\u03c9\u03b1\u03bd\u03bd\u03af\u03b4\u03b7\u03c2","orcid":"https://orcid.org/0000-0003-3922-8080"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ioannides, Georgios","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107555237","display_name":"Adrian Kieback","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kieback, Adrian","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123001433","display_name":"Judah Allen Goldfeder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goldfeder, Judah","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125674469","display_name":"Linsey Pang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pang, Linsey","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125623670","display_name":"Aman Chadha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chadha, Aman","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081324174","display_name":"Aaron Elkins","orcid":"https://orcid.org/0000-0001-5291-1023"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elkins, Aaron","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113433465","display_name":"Yann Lecun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LeCun, Yann","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Shwartz-Ziv, Ravid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shwartz-Ziv, Ravid","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5073370625"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8108999729156494,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10667","display_name":"Emotion and Mood Recognition","score":0.8108999729156494,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.10599999874830246,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11448","display_name":"Face recognition and analysis","score":0.010099999606609344,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.6740000247955322},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.6615999937057495},{"id":"https://openalex.org/keywords/spectrogram","display_name":"Spectrogram","score":0.6470000147819519},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6007999777793884},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.504800021648407},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.49630001187324524},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4562000036239624},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.41819998621940613},{"id":"https://openalex.org/keywords/dynamic-time-warping","display_name":"Dynamic time warping","score":0.41370001435279846}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7113999724388123},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6740000247955322},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6615999937057495},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.6470000147819519},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6007999777793884},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.508899986743927},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.504800021648407},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.49630001187324524},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.47540000081062317},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4562000036239624},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.41819998621940613},{"id":"https://openalex.org/C88516994","wikidata":"https://www.wikidata.org/wiki/Q1268863","display_name":"Dynamic time warping","level":2,"score":0.41370001435279846},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.39989998936653137},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.37310001254081726},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.36059999465942383},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.3481999933719635},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3190999925136566},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.30239999294281006},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2978000044822693},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2962000072002411},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.28439998626708984},{"id":"https://openalex.org/C159694833","wikidata":"https://www.wikidata.org/wiki/Q2321565","display_name":"Iterative method","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27079999446868896},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.2651999890804291},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.260699987411499},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.2526000142097473},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.09040","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"doi:10.48550/arxiv.2602.09040","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.09040","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.09040","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Joint":[0],"Embedding":[1],"Predictive":[2],"Architectures":[3],"(JEPA)":[4],"offer":[5],"a":[6,28,112],"promising":[7],"approach":[8,74],"to":[9,54,61,111,125,129],"self-supervised":[10],"speech":[11],"representation":[12,17],"learning,":[13],"but":[14],"suffer":[15],"from":[16],"collapse":[18],"without":[19],"explicit":[20],"grounding.":[21],"We":[22],"propose":[23],"GMM-Anchored":[24],"JEPA,":[25],"which":[26,69],"fits":[27],"Gaussian":[29],"Mixture":[30],"Model":[31],"once":[32,78],"on":[33],"log-mel":[34],"spectrograms":[35],"and":[36,67,103],"uses":[37],"its":[38],"frozen":[39],"soft":[40,80],"posteriors":[41],"as":[42],"auxiliary":[43],"targets":[44],"throughout":[45],"training.":[46],"A":[47],"decaying":[48],"supervision":[49],"schedule":[50],"allows":[51],"GMM":[52,90],"regularization":[53],"dominate":[55],"early":[56],"training":[57],"before":[58],"gradually":[59],"yielding":[60],"the":[62],"JEPA":[63],"objective.":[64],"Unlike":[65],"HuBERT":[66],"WavLM,":[68],"require":[70],"iterative":[71],"re-clustering,":[72],"our":[73],"clusters":[75],"input":[76],"features":[77],"with":[79,115],"rather":[81],"than":[82],"hard":[83],"assignments.":[84],"On":[85],"~50k":[86],"hours":[87],"of":[88],"speech,":[89],"anchoring":[91],"improves":[92],"ASR":[93],"(28.68%":[94],"vs.":[95,101,107],"33.22%":[96],"WER),":[97],"emotion":[98],"recognition":[99],"(67.76%":[100],"65.46%),":[102],"slot":[104],"filling":[105],"(64.7%":[106],"59.1%":[108],"F1)":[109],"compared":[110,128],"WavLM-style":[113],"baseline":[114],"matched":[116],"compute.":[117],"Cluster":[118],"analysis":[119],"shows":[120],"GMM-anchored":[121],"representations":[122],"achieve":[123],"up":[124],"98%":[126],"entropy":[127],"31%":[130],"for":[131],"WavLM-style,":[132],"indicating":[133],"substantially":[134],"more":[135],"uniform":[136],"cluster":[137],"utilization.":[138],"Code":[139],"is":[140],"made":[141],"available":[142],"at":[143],"https://github.com/gioannides/clustering-anchored-jepa.":[144]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-12T00:00:00"}
