A Review on Deep Learning Architectures for Speech Recognition

Basit Öğe Kaydı
dc.contributor.authorDokuz, Yeşim
dc.contributor.authorTüfekçi, Zekeriya
dc.date.accessioned2024-11-07T13:19:01Z
dc.date.available2024-11-07T13:19:01Z
dc.date.issued2020
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractDeep learning is a branch of machine learning that uses several algorithms which tries to model datasets by using deep architectureswith many processing layers. With the popularity and successful applications of deep learning architectures, they are being used inspeech recognition, as well. Researchers utilized these architectures for speech recognition and its applications, such as speechemotion recognition, voice activity detection, and speaker recognition and verification to better model speech inputs with outputs andto reduce error rates of speech recognition systems. Many studies are performed in the literature that use deep learning architecturesfor speech recognition systems. The literature studies show that using deep learning architectures for speech recognition and itsapplications provide benefits for many speech recognition areas and have ability to reduce error rates and provide better performance.In this study, first of all, we explained speech recognition problem and the steps of speech recognition. Then, we analyzed the studiesrelated to deep learning based speech recognition. In particular, deep learning architectures of Deep Neural Networks, ConvolutionalNeural Networks, and Recurrent Neural Networks and hybrid approaches that use these architectures are evaluated and the literaturestudies related to these architectures for speech recognition and the application areas of speech recognition are investigated. As aresult, we observed that RNNs are the most utilized and powerful deep learning architecture among all of the deep learningarchitectures in terms of error rates and speech recognition performance. CNNs are other successful deep learning architectures andhave closer results with RNN in terms of error rates and speech recognition performance. Also, we observed that new deeparchitectures that use either hybrid of DNNs, CNNs, and RNNs or other deep learning architectures are getting attention and haveincreasing performance and could reduce error rates in speech recognition.
dc.identifier.doi10.31590/ejosat.araconf22
dc.identifier.endpage176
dc.identifier.issn2148-2683
dc.identifier.issueEjosat Özel Sayı 2020 (ARACONF)
dc.identifier.startpage169
dc.identifier.trdizinid364765
dc.identifier.urihttps://doi.org/10.31590/ejosat.araconf22
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/364765
dc.identifier.urihttps://hdl.handle.net/11480/12829
dc.identifier.volume0
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofAvrupa Bilim ve Teknoloji Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241107
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectBilgisayar Bilimleri
dc.subjectSibernitik
dc.subjectBilgisayar Bilimleri
dc.subjectBilgi Sistemleri
dc.subjectBilgisayar Bilimleri
dc.subjectDonanım ve Mimari
dc.subjectBilgisayar Bilimleri
dc.subjectTeori ve Metotlar
dc.subjectAkustik
dc.subjectBilgisayar Bilimleri
dc.subjectYapay Zeka
dc.titleA Review on Deep Learning Architectures for Speech Recognition
dc.typeReview Article

Dosyalar

Koleksiyon

TR-Dizin İndeksli Yayınlar Koleksiyonu