Deep Learning Techniques for Image and Speech Recognition: Current Trends and Future Directions
DOI:
https://doi.org/10.37296/esci.v5i1.212Keywords:
Deep Learning, Image Recognition, Transformer ModelAbstract
This study examines the latest developments and future directions of deep learning techniques in image and sound recognition. The study focuses on the analysis of various neural network architectures such as Convolutional Neural Networks (CNNs) for image processing and Recurrent Neural Networks (RNNs) for speech recognition. The methodology used includes a comprehensive literature study of the latest studies, evaluation of the performance of various models, and comparative analysis of existing techniques. The results showed a significant improvement in recognition accuracy, with CNNs achieving up to 98% accuracy for image classification and transformer-based models outperforming traditional RNNs in speech recognition. The challenges identified include high computational requirements, reliance on quality datasets, and model interpretability issues. The study also proposes several future development directions, including the integration of attention mechanisms, hybrid architectures, and more efficient learning techniques. In conclusion, despite the rapid progress, there is still significant room for innovation in improving the efficiency and reliability of deep learning-based image and voice recognition systems
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