{"id":"https://openalex.org/W7155081375","doi":"https://doi.org/10.48550/arxiv.2604.18204","title":"Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages","display_name":"Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155081375","doi":"https://doi.org/10.48550/arxiv.2604.18204"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18204","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.2604.18204","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5093061295","display_name":"V. S. D. S. Mahesh Akavarapu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Akavarapu, V. S. D. S. Mahesh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134115114","display_name":"Michael Daniel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Daniel, Michael","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5019358632","display_name":"Gerhard J\u00e4ger","orcid":"https://orcid.org/0000-0002-9642-9359"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"J\u00e4ger, Gerhard","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":0.6061000227928162,"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":0.6061000227928162,"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/T10403","display_name":"Phonetics and Phonology Research","score":0.07530000060796738,"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/T13194","display_name":"ICT in Developing Communities","score":0.05660000070929527,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/generalization","display_name":"Generalization","score":0.5992000102996826},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.5221999883651733},{"id":"https://openalex.org/keywords/vocabulary","display_name":"Vocabulary","score":0.49900001287460327},{"id":"https://openalex.org/keywords/error-analysis","display_name":"Error analysis","score":0.43880000710487366},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.42320001125335693},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.40799999237060547},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.3601999878883362},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.3409000039100647}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6919000148773193},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5992000102996826},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5921000242233276},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5630999803543091},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.5221999883651733},{"id":"https://openalex.org/C2777601683","wikidata":"https://www.wikidata.org/wiki/Q6499736","display_name":"Vocabulary","level":2,"score":0.49900001287460327},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48669999837875366},{"id":"https://openalex.org/C3018824978","wikidata":"https://www.wikidata.org/wiki/Q2894891","display_name":"Error analysis","level":2,"score":0.43880000710487366},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.42320001125335693},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.3409000039100647},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.30390000343322754},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C148934300","wikidata":"https://www.wikidata.org/wiki/Q40998","display_name":"Phonology","level":2,"score":0.2924000024795532},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.2833999991416931},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.27059999108314514},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.2694000005722046},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C137584468","wikidata":"https://www.wikidata.org/wiki/Q35395","display_name":"Phonetics","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C175293574","wikidata":"https://www.wikidata.org/wiki/Q697133","display_name":"Word lists by frequency","level":3,"score":0.2581000030040741}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18204","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.2604.18204","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18204","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":[{"id":"https://metadata.un.org/sdg/4","score":0.8549771308898926,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"We":[0,58,114],"present":[1],"a":[2,50,75,109,126],"phoneme-level":[3,111,174],"analysis":[4],"of":[5,38,173],"automatic":[6],"speech":[7],"recognition":[8,118],"(ASR)":[9],"for":[10,53,137,176],"two":[11],"low-resourced":[12],"and":[13,20,25,33,43,47,56,63,69,87,103],"phonologically":[14],"complex":[15,183],"East":[16],"Caucasian":[17],"languages,":[18],"Archi":[19],"Rutul,":[21],"based":[22],"on":[23],"curated":[24],"standardized":[26],"speech-transcript":[27],"resources":[28],"totaling":[29],"approximately":[30],"50":[31],"minutes":[32,37],"1":[34],"hour":[35],"20":[36],"audio,":[39],"respectively.":[40],"Existing":[41],"recordings":[42],"transcriptions":[44],"are":[45,162],"consolidated":[46],"processed":[48],"into":[49],"form":[51],"suitable":[52],"ASR":[54,178],"training":[55,123,149],"evaluation.":[57],"evaluate":[59],"several":[60],"state-of-the-art":[61],"audio":[62],"audio-language":[64],"models,":[65],"including":[66],"wav2vec2,":[67,72],"Whisper,":[68,138],"Qwen2-Audio.":[70],"For":[71,131],"we":[73,107],"introduce":[74],"language-specific":[76],"phoneme":[77,117],"vocabulary":[78],"with":[79,122],"heuristic":[80],"output-layer":[81],"initialization,":[82],"which":[83],"yields":[84],"consistent":[85],"improvements":[86],"achieves":[88],"performance":[89],"comparable":[90],"to":[91,140,159],"or":[92],"exceeding":[93],"Whisper":[94],"in":[95,180],"these":[96],"extremely":[97],"low-resource":[98],"settings.":[99],"Beyond":[100],"standard":[101],"word":[102],"character":[104],"error":[105,112],"rates,":[106],"conduct":[108],"detailed":[110],"analysis.":[113],"find":[115],"that":[116,155],"accuracy":[119],"strongly":[120],"correlates":[121],"frequency,":[124],"exhibiting":[125],"characteristic":[127],"sigmoid-shaped":[128],"learning":[129],"curve.":[130],"Archi,":[132],"this":[133],"relationship":[134],"partially":[135],"breaks":[136],"pointing":[139],"model-specific":[141],"generalization":[142],"effects":[143],"beyond":[144],"what":[145],"is":[146],"predicted":[147],"by":[148,165],"frequency.":[150],"Overall,":[151],"our":[152],"results":[153],"indicate":[154],"many":[156],"errors":[157],"attributed":[158],"phonological":[160],"complexity":[161],"better":[163],"explained":[164],"data":[166],"scarcity.":[167],"These":[168],"findings":[169],"demonstrate":[170],"the":[171],"value":[172],"evaluation":[175],"understanding":[177],"behavior":[179],"low-resource,":[181],"typologically":[182],"languages.":[184]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-22T00:00:00"}
