{"id":"https://openalex.org/W48893395","doi":"https://doi.org/10.21437/interspeech.2010-282","title":"Predicting word accuracy for the automatic speech recognition of non-native speech","display_name":"Predicting word accuracy for the automatic speech recognition of non-native speech","publication_year":2010,"publication_date":"2010-09-26","ids":{"openalex":"https://openalex.org/W48893395","doi":"https://doi.org/10.21437/interspeech.2010-282","mag":"48893395"},"language":"en","primary_location":{"id":"doi:10.21437/interspeech.2010-282","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2010-282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2010","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5008718449","display_name":"Su\u2010Youn Yoon","orcid":null},"institutions":[{"id":"https://openalex.org/I1341030882","display_name":"Educational Testing Service","ror":"https://ror.org/03b5q4637","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1341030882"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Su-Youn Yoon","raw_affiliation_strings":["Educational Testing Service"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Educational Testing Service","institution_ids":["https://openalex.org/I1341030882"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100333516","display_name":"Lei Chen","orcid":"https://orcid.org/0000-0002-8257-5806"},"institutions":[{"id":"https://openalex.org/I1341030882","display_name":"Educational Testing Service","ror":"https://ror.org/03b5q4637","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1341030882"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Chen","raw_affiliation_strings":["Educational Testing Service"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Educational Testing Service","institution_ids":["https://openalex.org/I1341030882"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5069537054","display_name":"Klaus Zechner","orcid":"https://orcid.org/0000-0002-9465-6929"},"institutions":[{"id":"https://openalex.org/I1341030882","display_name":"Educational Testing Service","ror":"https://ror.org/03b5q4637","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1341030882"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Klaus Zechner","raw_affiliation_strings":["Educational Testing Service"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Educational Testing Service","institution_ids":["https://openalex.org/I1341030882"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I1341030882"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"773","last_page":"776"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9998000264167786,"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":0.9998000264167786,"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/T12031","display_name":"Speech and dialogue systems","score":0.9994999766349792,"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":0.9970999956130981,"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/computer-science","display_name":"Computer science","score":0.7582312822341919},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.705878496170044},{"id":"https://openalex.org/keywords/prosody","display_name":"Prosody","score":0.6212292313575745},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.610014796257019},{"id":"https://openalex.org/keywords/fluency","display_name":"Fluency","score":0.5968756675720215},{"id":"https://openalex.org/keywords/word-error-rate","display_name":"Word error rate","score":0.5536184310913086},{"id":"https://openalex.org/keywords/binary-classification","display_name":"Binary classification","score":0.5040473341941833},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.4871227741241455},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.47253894805908203},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4506995975971222},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.41443219780921936},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.13874465227127075},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09872409701347351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7582312822341919},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.705878496170044},{"id":"https://openalex.org/C542774811","wikidata":"https://www.wikidata.org/wiki/Q10880526","display_name":"Prosody","level":2,"score":0.6212292313575745},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.610014796257019},{"id":"https://openalex.org/C2777413886","wikidata":"https://www.wikidata.org/wiki/Q3276013","display_name":"Fluency","level":2,"score":0.5968756675720215},{"id":"https://openalex.org/C40969351","wikidata":"https://www.wikidata.org/wiki/Q3516228","display_name":"Word error rate","level":2,"score":0.5536184310913086},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.5040473341941833},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.4871227741241455},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.47253894805908203},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4506995975971222},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.41443219780921936},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.13874465227127075},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09872409701347351},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C145420912","wikidata":"https://www.wikidata.org/wiki/Q853077","display_name":"Mathematics education","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.21437/interspeech.2010-282","is_oa":false,"landing_page_url":"https://doi.org/10.21437/interspeech.2010-282","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Interspeech 2010","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.652.6441","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.652.6441","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://mkzechner.net/is2010_war_july7_FINAL.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.75,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W151797875","https://openalex.org/W1557757161","https://openalex.org/W1843577977","https://openalex.org/W1979364015","https://openalex.org/W2069029003","https://openalex.org/W2075959218","https://openalex.org/W2109933326","https://openalex.org/W2131694695","https://openalex.org/W2133990480","https://openalex.org/W2139439365","https://openalex.org/W2159250980","https://openalex.org/W2169621245","https://openalex.org/W3132946990"],"related_works":["https://openalex.org/W2365169615","https://openalex.org/W1970538215","https://openalex.org/W2400151637","https://openalex.org/W2975827637","https://openalex.org/W2354089692","https://openalex.org/W595497825","https://openalex.org/W4382934300","https://openalex.org/W2121061354","https://openalex.org/W4285388059","https://openalex.org/W3094220251"],"abstract_inverted_index":{"We":[0],"have":[1,57],"developed":[2],"an":[3,133],"automated":[4],"method":[5,48],"that":[6],"predicts":[7],"the":[8,20,47,87],"word":[9,38,106,149],"accuracy":[10,107,130,134,150],"of":[11,22,109,115,135,143],"a":[12,103,113,139],"speech":[13,34,147,155],"recognition":[14],"system":[15],"for":[16,51,60,124],"non-native":[17,52,61,152],"speech,":[18,53],"in":[19,90,127],"context":[21],"speaking":[23],"proficiency":[24,64],"scoring.":[25],"A":[26,97,121],"model":[27,98],"was":[28,49],"trained":[29],"using":[30,99,117],"features":[31,79],"based":[32],"on":[33],"recognizer":[35],"scores,":[36],"func-tion":[37],"distributions,":[39],"prosody,":[40],"background":[41],"noise,":[42],"and":[43,85],"speak-ing":[44],"fluency.":[45],"Since":[46],"implemented":[50,67],"fluency":[54,78],"features,":[55],"which":[56],"been":[58],"used":[59,73],"speak-ers":[62],"\u2019":[63,154],"scoring,":[65],"were":[66],"along":[68],"with":[69,92,105],"several":[70],"feature":[71],"groups":[72],"from":[74],"past":[75],"research.":[76],"The":[77],"showed":[80],"promising":[81],"performance":[82,89],"by":[83],"themselves,":[84],"improved":[86],"overall":[88],"tandem":[91],"other":[93],"more":[94],"traditional":[95],"features.":[96],"stepwise":[100],"regression":[101],"achieved":[102,132],"correlation":[104],"rates":[108],"0.76,":[110],"compared":[111,137],"to":[112,138],"baseline":[114,142],"0.63":[116],"only":[118],"confidence":[119],"scores.":[120],"binary":[122],"classifier":[123],"plac-ing":[125],"utterances":[126],"high-or":[128],"low-word":[129],"bins":[131],"84%,":[136],"majority":[140],"class":[141],"64%.":[144],"Index":[145],"Terms:":[146],"recognition,":[148],"rate,":[151],"speakers":[153]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
