Hussein, Hasan H.Karan, OğuzKurnaz, SeferTürkben, Ayça Kurnaz2025-08-142025-08-142025Hussein, 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/ICHORA65333.2025.110170519798331510886https://hdl.handle.net/20.500.12939/5873Conference 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 : 209351Machine 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.eninfo:eu-repo/semantics/closedAccessdeep learningdeep neural networkSpeech emotion recognitionSpeech recognitionConference Object10.1109/ICHORA65333.2025.110170512-s2.0-105008417619