DHMM-Based Speech Recognition Program Implementation

This is a MATLAB-based speech recognition program implemented using Discrete Hidden Markov Models (DHMM). The system operates by extracting acoustic features from audio signals and comparing them against pre-trained speech models to identify spoken words. The DHMM framework employs statistical modeling principles to predict hidden state sequences from observed feature sequences, making it particularly effective for temporal pattern recognition tasks. Key implementation aspects include: - Feature extraction using Mel-Frequency Cepstral Coefficients (MFCC) to represent audio characteristics - Baum-Welch algorithm for training DHMM parameters through expectation-maximization - Viterbi algorithm for decoding the most likely word sequence during recognition - Model initialization and optimization techniques for improved accuracy The program structure typically involves separate modules for signal preprocessing, feature vector quantization, model training, and recognition phase. By leveraging DHMM's probabilistic framework, the system achieves efficient and accurate speech recognition performance suitable for various vocabulary sizes and speaking styles.