A Comprehensive Review of Speech Recognition Systems: Architectures, Techniques, and Future Directions

– Speech recognition, also known as Automatic Speech Recognition (ASR), has undergone rapid evolution driven by advances in machine learning and deep neural networks. From early statistical models such as Hidden Markov Models (HMMs) to modern end-to-end architectures based on Transformers and self-supervised learning, ASR systems have achieved significant improvements in accuracy and scalability. This paper presents a comprehensive review of speech recognition systems, focusing on architectural developments, modeling techniques, datasets, evaluation metrics, and applications. We compare traditional hybrid approaches with deep learning-based models including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer-based architectures. Furthermore, we discuss challenges such as noise robustness, multilingual processing, and low-resource scenarios. Finally, we outline emerging trends including self-supervised learning and foundation models, providing insights into future research directions.