MATLAB Speech Recognition Program Implementation

I am developing a speech recognition program using MATLAB, with the primary objective of converting user's voice input into recognizable text. The implementation leverages MATLAB's robust speech processing toolkit and sophisticated algorithms including Mel-Frequency Cepstral Coefficients (MFCCs) for feature extraction and Hidden Markov Models (HMMs) or Deep Neural Networks (DNNs) for pattern recognition. The program analyzes audio characteristics across multiple domains: frequency spectrum analysis using FFT (Fast Fourier Transform), intensity measurements through power spectral density, and temporal features including zero-crossing rate and energy contours. Key MATLAB functions being utilized include 'audioread' for input processing, 'mfcc' for feature extraction, and pattern recognition tools from the Signal Processing and Deep Learning Toolboxes. This system aims to enhance speech recognition accuracy and practical applicability by implementing noise reduction techniques (using spectral subtraction or Wiener filtering) and dynamic time warping for temporal alignment. The continuous optimization process involves testing with diverse datasets, tuning model parameters, and implementing confidence scoring mechanisms to improve reliability. Through this project, I aim to deepen understanding of speech recognition technologies while contributing to future research applications in human-computer interaction and automated transcription systems. The code architecture follows modular design principles, separating feature extraction, model training, and recognition phases for maintainability and performance optimization.