Deep Learning Approaches for Speech Recognition Systems
Automatic Speech Recognition (ASR) has evolved from rule-based and statistical methods to deep learning approaches, achieving near human-level performance under certain conditions. Traditional HMM-GMM models face limitations in handling long-term dependencies, speaker variability, and noise. Modern architectures such as DNNs, CNNs, RNNs, LSTMs, and Transformers provide improved representation learning and feature extraction from speech signals. This paper presents a comprehensive review of deep learning-based ASR systems, covering their evolution, acoustic feature extraction, end-to-end modeling, and training techniques. A generalized ASR pipeline is proposed, including preprocessing, feature encoding, model design, optimization, and decoding. Performance is evaluated using metrics like Word Error Rate (WER) and Character Error Rate (CER). The study highlights key challenges such as low-resource languages, real-time processing, domain adaptation, and model interpretability. It concludes that deep learning is the standard for ASR, with future research focusing on self-supervised learning, multilingual models, and efficient edge deployment.