Yazar "Hussein, Hasan H." seçeneğine göre listele

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    Enhancing Driving Control via Speech Recognition Utilizing Influential Parameters in Deep Learning Techniques
    (Multidisciplinary Digital Publishing Institute (MDPI), 2025) Hussein, Hasan H.; Karan, Oğuz; Kurnaz, Sefer
    This study investigates the enhancement of automated driving and command control through speech recognition using a Deep Neural Network (DNN). The method depends on some sequential stages such as noise removal, feature extraction from the audio file, and their classification using a neural network. In the proposed approach, the variables that affect the results in the hidden layers were extracted and stored in a vector to classify them and issue the most influential ones for feedback to the hidden layers in the neural network to increase the accuracy of the result. The result was 93% in terms of accuracy and with a very good response time of 0.75 s, with PSNR 78 dB. The proposed method is considered promising and is highly satisfactory to users. The results encouraged the use of more commands, more data processing, more future exploration, and the addition of sensors to increase the efficiency of the system and obtain more efficient and safe driving, which is the main goal of this research.
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    Speech Recognition of High Impact Model Using Deep Learning Technique: A Review
    (Institute of Electrical and Electronics Engineers Inc., 2025) Hussein, Hasan H.; Karan, Oğuz; Kurnaz, Sefer; Türkben, Ayça Kurnaz
    Machine 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.