Deep Learning Architectures for Real-Time Image and Speech Recognition
Keywords:
Deep Learning, Real-Time Recognition, Image Recognition, Speech Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Transformer Models, Optimization, Hardware Accelerators, AI ApplicationsAbstract
The rapid advancements in deep learning have made significant strides in real-time image and speech recognition tasks, enabling applications across various fields such as healthcare, autonomous driving, and virtual assistants. This paper explores the deep learning architectures specifically designed for real-time recognition, focusing on image and speech modalities. It examines key techniques like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Transformer models, and hybrid approaches. Additionally, we investigate optimization strategies and hardware accelerators that are essential for real-time performance. The paper concludes by highlighting the challenges and opportunities in further advancing these systems for practical, real-world applications.
