Deep Learning Architectures for Image and Speech Recognition: A Comprehensive Review

Abstract

Machines can now learn hierarchical feature representations from raw data thanks to deep learning, which has transformed picture and speech recognition. Recognition systems have been much better and more reliable over the last decade, thanks to sophisticated neural network topologies that outperform the old-school machine learning methods. Due to its superior performance in picture categorization, object detection, and feature extraction, Convolutional Neural Networks (CNNs) have emerged as the backbone of image recognition tasks. Similarly, designs based on Transformers, Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks have all done an excellent job at modeling sequential data for voice recognition. thorough examination of deep learning architectures employed in voice and picture recognition, showcasing their development, essential traits, and comparative efficacy. For picture processing, it looks at ResNet, VGG, and EfficientNet; for voice processing, it looks at DeepSpeech, WaveNet, and Transformer-based models. combine convolutional and sequential layers into a hybrid model to improve performance in multimodal applications.