Detecting and correcting automatic speech recognition errors with a new model

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dc.contributor.authorArslan, Recep Sinan
dc.contributor.authorBariSci, Necaattin
dc.contributor.authorArici, Nursal
dc.contributor.authorKocer, Sabri
dc.date.accessioned2024-02-23T14:37:21Z
dc.date.available2024-02-23T14:37:21Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractThe purpose of automatic speech recognition (ASR) systems is to recognize speech signals obtained from people and convert them into text so that they can be processed by a computer. Although many ASR applications are versatile and widely used in the real world, they still generate relatively inaccurate results. They tend to generate spelling errors in recognized words, especially in noisy environments, in situations where the vocabulary size is increased, and at times when the input speech is of poor quality. The permanent presence of errors in ASR systems has led to the need to find alternative methods for automatic detection and correction of such errors. In this study, the basic principles of ASR evaluation are first summarized, and then a new approach based on the suggestion of an alternative hypothesis is proposed for the detection and correction of these errors generated by ASR systems. The proposed method involves a series of processes such as identifying incorrect words, selecting the ones that can be corrected, and identifying candidate words to replace these words. As a result of the tests carried out by creating different test environments, significant performance improvements for Turkish were achieved and an average of 4.60 % performance improvement was provided.en_US
dc.identifier.doi10.3906/elk-2010-117
dc.identifier.endpage2311en_US
dc.identifier.issn1300-0632
dc.identifier.issn1303-6203
dc.identifier.issue5en_US
dc.identifier.scopus2-s2.0-85117134759en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage2298en_US
dc.identifier.urihttps://doi.org/10.3906/elk-2010-117
dc.identifier.urihttps://hdl.handle.net/20.500.12452/16060
dc.identifier.volume29en_US
dc.identifier.wosWOS:000703667100003en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal Of Electrical Engineering And Computer Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAutomatic Speech Recognitionen_US
dc.subjectAutomatic Speech Recognition Error Correctionen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectAlternative Hypothesis Suggestionen_US
dc.subjectNatural Language Processingen_US
dc.titleDetecting and correcting automatic speech recognition errors with a new modelen_US
dc.typeArticleen_US

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