Acoustics

Speech Recognition Systems: Design Considerations Designing a speech recognition system means balancing accuracy, speed, and practicality. The core idea is to turn sound into text reliably, even in real rooms. A typical setup includes an acoustic model, a language model, and a decoding step. The choices you make for each part shape how well the system performs in your target environment. Core components Acoustic models translate audio frames into symbols that resemble speech sounds. You can choose end-to-end approaches (like RNN-T or CTC) for a simpler pipeline, or traditional modular setups that separate acoustic, pronunciation, and language models. Language models predict likely word sequences and help the transcript sound natural. The decoder then combines these parts in real time or after collection. ...

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. ...