{"id":"https://openalex.org/W7135016529","doi":"https://doi.org/10.48550/arxiv.2603.10701","title":"AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow","display_name":"AlphaFlowTSE: One-Step Generative Target Speaker Extraction via Conditional AlphaFlow","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135016529","doi":"https://doi.org/10.48550/arxiv.2603.10701"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.10701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10701","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":null,"license_id":null,"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.2603.10701","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5114126417","display_name":"Duojia Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Duojia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102815719","display_name":"Shuhan Zhang","orcid":"https://orcid.org/0009-0000-4340-1435"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Shuhan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128805867","display_name":"Zihan Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Zihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052774438","display_name":"Wenxuan Wu","orcid":"https://orcid.org/0000-0001-5459-8990"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wu, Wenxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128880059","display_name":"Shuai Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Shuai","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011997254","display_name":"Qingyang Hong","orcid":"https://orcid.org/0000-0001-7380-8690"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hong, Qingyang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128855934","display_name":"Lin Li","orcid":"https://orcid.org/0009-0006-4463-0161"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Lin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128811988","display_name":"Haizhou Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Haizhou","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/T10201","display_name":"Speech Recognition and Synthesis","score":0.7670999765396118,"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"}},"topics":[{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.7670999765396118,"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/T10860","display_name":"Speech and Audio Processing","score":0.14990000426769257,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T10667","display_name":"Emotion and Mood Recognition","score":0.02070000022649765,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5674999952316284},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5314000248908997},{"id":"https://openalex.org/keywords/utterance","display_name":"Utterance","score":0.515500009059906},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5146999955177307},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.47929999232292175},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4636000096797943},{"id":"https://openalex.org/keywords/trajectory","display_name":"Trajectory","score":0.40869998931884766},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.3959999978542328}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6870999932289124},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.6617000102996826},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5674999952316284},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.534600019454956},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5314000248908997},{"id":"https://openalex.org/C2775852435","wikidata":"https://www.wikidata.org/wiki/Q258403","display_name":"Utterance","level":2,"score":0.515500009059906},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5146999955177307},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.47929999232292175},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.40869998931884766},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.3959999978542328},{"id":"https://openalex.org/C133892786","wikidata":"https://www.wikidata.org/wiki/Q1145189","display_name":"Speaker recognition","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.3231000006198883},{"id":"https://openalex.org/C2776517306","wikidata":"https://www.wikidata.org/wiki/Q29017317","display_name":"Similarity measure","level":2,"score":0.3163999915122986},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.31380000710487366},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28360000252723694},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.28200000524520874},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C59656382","wikidata":"https://www.wikidata.org/wiki/Q191536","display_name":"Conjunction (astronomy)","level":2,"score":0.2533999979496002}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.10701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10701","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.10701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.10701","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.6419852375984192,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0],"target":[1,9],"speaker":[2],"extraction":[3],"(TSE),":[4],"we":[5],"aim":[6],"to":[7],"recover":[8],"speech":[10,117],"from":[11,80],"a":[12,16,44,58,65,76],"multi-talker":[13],"mixture":[14],"using":[15],"short":[17],"enrollment":[18],"utterance":[19],"as":[20],"reference.":[21],"Recent":[22],"studies":[23],"on":[24,43,101],"diffusion":[25],"and":[26,38,88,103,111],"flow-matching":[27],"generators":[28],"have":[29],"improved":[30],"target-speech":[31],"fidelity.":[32],"However,":[33],"multi-step":[34],"sampling":[35],"increases":[36],"latency,":[37],"one-step":[39,59],"solutions":[40],"often":[41],"rely":[42],"mixture-dependent":[45],"time":[46],"coordinate":[47],"that":[48,106],"can":[49],"be":[50],"unreliable":[51],"for":[52,114],"real-world":[53],"conversations.":[54],"We":[55],"present":[56],"AlphaFlowTSE,":[57],"conditional":[60],"generative":[61],"model":[62],"trained":[63],"with":[64,95],"Jacobian-vector":[66],"product":[67],"(JVP)-free":[68],"AlphaFlow":[69],"objective.":[70],"AlphaFlowTSE":[71,107],"learns":[72],"mean-velocity":[73],"transport":[74],"along":[75],"mixture-to-target":[77],"trajectory":[78],"starting":[79],"the":[81],"observed":[82],"mixture,":[83],"eliminating":[84],"auxiliary":[85],"mixing-ratio":[86],"prediction,":[87],"stabilizes":[89],"training":[90],"by":[91],"combining":[92],"flow":[93],"matching":[94],"an":[96],"interval-consistency":[97],"teacher-student":[98],"target.":[99],"Experiments":[100],"Libri2Mix":[102],"REAL-T":[104],"confirm":[105],"improves":[108],"target-speaker":[109],"similarity":[110],"real-mixture":[112],"generalization":[113],"downstream":[115],"automatic":[116],"recognition":[118],"(ASR).":[119]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-13T00:00:00"}
