Speech Recognition: Techniques and Applications
Speech Recognition: Techniques and Applications Speech recognition turns spoken language into written text. It powers captions, voice search, and hands-free devices. Over the last decade, progress has moved from rule-based pipelines to end-to-end neural models that learn from large data. This shift makes systems more accurate and easier to deploy on phones, computers, and cloud services. Techniques Modern systems blend traditional signal processing with neural networks. Early work used MFCC features and HMM-GMM models, which map audio frames to phonemes. Today, end-to-end architectures like Transformer-based models learn to map audio directly to text, often with a separate acoustic model and a language model. ...