Speech Recognition of High Impact Model Using Deep Learning Technique: A Review

Basit Öğe Kaydı
dc.contributor.authorHussein, Hasan H.
dc.contributor.authorKaran, Oğuz
dc.contributor.authorKurnaz, Sefer
dc.contributor.authorTürkben, Ayça Kurnaz
dc.date.accessioned2025-08-14T15:20:35Z
dc.date.available2025-08-14T15:20:35Z
dc.date.issued2025
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
dc.descriptionConference name : 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, ICHORA 2025 Conference city : Ankara Conference date : 23 May 2025 - 24 May 2025 Conference code : 209351
dc.description.abstractMachine learning has been the subject of enormous study in speech processing, particularly in speech recognition, for the last decades. On the other hand, deep learning's potential use in speech recognition has been the subject of much study in recent years. New evidence suggests that deep learning has far-reaching applications across many domains and has significantly contributed to AI. Several applications involving voice have demonstrated encouraging outcomes when using deep learning models. There has been a recent growth of attention-based approaches and models that apply transfer learning to enormous datasets, which offers added motivation for ASR. Focussing on several deep-learning models, it provides a summary and comparison of the state-of-the-art approaches used in this field of study. Additionally, we have evaluated the models on speech datasets to learn how they function on various datasets for practical application. Academics interested in open-source ASR could use this study as a jumping-off point for future research on issues like minimizing data dependence, increasing generalisability across languages with limited resources, speaker variability, noise conditions, and identifying and resolving obstacles to advancing existing research.
dc.identifier.citationHussein, H. H., Karan, O., Kurnaz, S., & Turkben, A. K. (2025, May). Speech Recognition of High Impact Model Using Deep Learning Technique: A Review. In 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications (ICHORA) (pp. 1-10). IEEE. 10.1109/ICHORA65333.2025.11017051
dc.identifier.doi10.1109/ICHORA65333.2025.11017051
dc.identifier.isbn9798331510886
dc.identifier.scopus2-s2.0-105008417619
dc.identifier.urihttps://hdl.handle.net/20.500.12939/5873
dc.indekslendigikaynakScopus
dc.institutionauthorHussein, Hasan H.
dc.institutionauthorKurnaz, Sefer
dc.institutionauthorTürkben, Ayça Kurnaz
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartofICHORA 2025 - 2025 7th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Öğrenci
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectdeep learning
dc.subjectdeep neural network
dc.subjectSpeech emotion recognition
dc.subjectSpeech recognition
dc.titleSpeech Recognition of High Impact Model Using Deep Learning Technique: A Review
dc.typeConference Object

Dosyalar

Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu