{"id":"https://openalex.org/W7165393014","doi":"https://doi.org/10.48550/arxiv.2606.19398","title":"S-JEPA : Soft Clustering Anchors for Self-Supervised Speech Representation Learning","display_name":"S-JEPA : Soft Clustering Anchors for Self-Supervised Speech Representation Learning","publication_year":2026,"publication_date":"2026-06-17","ids":{"openalex":"https://openalex.org/W7165393014","doi":"https://doi.org/10.48550/arxiv.2606.19398"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.19398","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19398","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.19398","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":false,"raw_author_name":"Ioannides, Georgios","raw_affiliation_strings":[],"raw_orcid":null,"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":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139018845","display_name":"Judah Goldfeder","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Goldfeder, Judah","raw_affiliation_strings":[],"raw_orcid":null,"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":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138989323","display_name":"Aman Chadha","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chadha, Aman","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138682942","display_name":"Aaron Elkins","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Elkins, Aaron","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138999911","display_name":"Yann LeCun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"LeCun, Yann","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138986754","display_name":"Ravid Shwartz-Ziv","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shwartz-Ziv, Ravid","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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":0.6913999915122986,"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":0.6913999915122986,"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":0.21549999713897705,"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":0.03009999915957451,"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/mixture-model","display_name":"Mixture model","score":0.6478999853134155},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6007999777793884},{"id":"https://openalex.org/keywords/ambiguity","display_name":"Ambiguity","score":0.5613999962806702},{"id":"https://openalex.org/keywords/mel-frequency-cepstrum","display_name":"Mel-frequency cepstrum","score":0.5239999890327454},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47530001401901245},{"id":"https://openalex.org/keywords/speaker-recognition","display_name":"Speaker recognition","score":0.461899995803833},{"id":"https://openalex.org/keywords/hidden-markov-model","display_name":"Hidden Markov model","score":0.375900000333786},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.37450000643730164},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.357699990272522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6736999750137329},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6608999967575073},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.6478999853134155},{"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/C2780522230","wikidata":"https://www.wikidata.org/wiki/Q1140419","display_name":"Ambiguity","level":2,"score":0.5613999962806702},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.5239999890327454},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4819999933242798},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47530001401901245},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.461899995803833},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.357699990272522},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.35260000824928284},{"id":"https://openalex.org/C171752962","wikidata":"https://www.wikidata.org/wiki/Q255166","display_name":"Kullback\u2013Leibler divergence","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.3440000116825104},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32690000534057617},{"id":"https://openalex.org/C155635449","wikidata":"https://www.wikidata.org/wiki/Q4674699","display_name":"Acoustic model","level":3,"score":0.31380000710487366},{"id":"https://openalex.org/C105611402","wikidata":"https://www.wikidata.org/wiki/Q2976589","display_name":"Spectral clustering","level":3,"score":0.3082999885082245},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C88485024","wikidata":"https://www.wikidata.org/wiki/Q1054571","display_name":"Cepstrum","level":2,"score":0.29679998755455017},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.29109999537467957},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C91863865","wikidata":"https://www.wikidata.org/wiki/Q4349497","display_name":"Speech corpus","level":3,"score":0.27480000257492065},{"id":"https://openalex.org/C13895895","wikidata":"https://www.wikidata.org/wiki/Q3270773","display_name":"Speech coding","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26570001244544983},{"id":"https://openalex.org/C164866538","wikidata":"https://www.wikidata.org/wiki/Q367351","display_name":"Cluster (spacecraft)","level":2,"score":0.2547000050544739},{"id":"https://openalex.org/C2780762811","wikidata":"https://www.wikidata.org/wiki/Q1784941","display_name":"Cosine similarity","level":3,"score":0.2526000142097473},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.25200000405311584}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.19398","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19398","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.19398","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19398","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Self-supervised":[0],"speech":[1,157],"encoders":[2],"are":[3],"predominantly":[4],"trained":[5,42],"by":[6],"predicting":[7],"discrete":[8],"hard":[9,189],"cluster":[10,107],"IDs":[11],"at":[12,21,53,131,196],"masked":[13,54],"positions,":[14],"a":[15,38,49,69,89,138,159,163,172],"recipe":[16],"that":[17,180,188],"collapses":[18],"acoustic":[19,186],"ambiguity":[20,187],"category":[22],"boundaries":[23],"and":[24,98,125],"requires":[25],"interrupting":[26],"training":[27],"to":[28,43,106],"re-cluster":[29,96],"the":[30,45,83,94,99,110,115,151,169,181,185],"entire":[31],"corpus":[32],"between":[33],"iterations.":[34],"We":[35],"introduce":[36],"S-JEPA,":[37],"JEPA-style":[39],"encoder-predictor":[40],"pair":[41],"match":[44],"soft":[46],"posteriors":[47],"of":[48,102,150,166,171],"Gaussian":[50],"Mixture":[51],"Model":[52],"positions":[55],"via":[56],"KL":[57],"divergence.":[58],"Training":[59],"runs":[60],"as":[61],"one":[62],"continuous":[63],"optimization":[64],"trajectory":[65],"in":[66],"two":[67],"phases:":[68],"fixed":[70],"GMM":[71,78],"over":[72,79],"MFCC":[73],"features,":[74,81],"then":[75],"an":[76],"online":[77],"encoder":[80],"with":[82,162],"input":[84],"layer":[85,105],"selected":[86],"adaptively":[87],"from":[88],"label-free":[90],"signal,":[91],"removing":[92],"both":[93],"offline":[95,143],"step":[97],"hand-tuned":[100],"choice":[101],"which":[103],"transformer":[104],"on.":[108],"Under":[109],"SUPERB":[111],"protocol,":[112],"S-JEPA":[113],"achieves":[114],"lowest":[116],"WER":[117],"among":[118],"evaluated":[119],"SSL":[120],"methods":[121],"below":[122],"90M":[123],"parameters":[124],"matches":[126],"HuBERT-Base":[127],"on":[128,155],"emotion":[129],"recognition":[130],"roughly":[132],"half":[133],"its":[134],"parameter":[135],"count,":[136],"establishing":[137],"new":[139],"Pareto":[140],"frontier":[141],"without":[142],"re-clustering":[144],"or":[145],"teacher":[146],"distillation.":[147],"An":[148],"analysis":[149],"predictor's":[152],"per-frame":[153],"entropy":[154,170],"held-out":[156],"reveals":[158],"bimodal":[160],"distribution":[161],"substantial":[164],"minority":[165],"frames":[167],"near":[168],"perfect":[173],"two-cluster":[174],"tie,":[175],"providing":[176],"direct":[177],"empirical":[178],"evidence":[179],"soft-target":[182],"objective":[183],"preserves":[184],"targets":[190],"would":[191],"collapse.":[192],"Code":[193],"is":[194],"available":[195],"https://github.com/gioannides/s-jepa.":[197]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-20T00:00:00"}
