An Extensive Analysis of Deep Learning Architectures for Voice and Picture Recognition

Abstract

Deep learning has revolutionized image and audio recognition by allowing machines to learn hierarchical feature representations from raw data. In the past ten years, recognition systems have greatly improved and become more dependable, all because of the use of advanced neural network topologies that surpass the performance of traditional machine learning techniques. A foundational component of image recognition tasks, Convolutional Neural Networks (CNNs) excel at picture classification, object detection, and feature extraction. In a similar vein, architectures utilizing Transformers, RNNs, and LSTM networks have all demonstrated remarkable proficiency in representing sequential data for speech recognition.  a comprehensive analysis of deep learning architectures used for image and speech recognition, highlighting their evolution, key characteristics, and relative effectiveness. When processing images, it takes a look at ResNet, VGG, and EfficientNet; when processing voices, it examines DeepSpeech, WaveNet, and models based on Transformer. incorporate a hybrid model that combines convolutional and sequential layers to enhance performance in multimodal applications.