Isolated Word Speech Recognition Based on Hidden Markov Models (HMM)

This implementation demonstrates isolated word speech recognition using Hidden Markov Models (HMM) with both personalized audio samples and core technical components. The system includes critical feature extraction methods such as Mel-Frequency Cepstral Coefficients (MFCC) for audio preprocessing and HMM parameter optimization through Baum-Welch algorithm for model training. We have enhanced recognition accuracy by carefully tuning HMM parameters including state transitions, emission probabilities, and initial state distributions. The acoustic model incorporates advanced speech processing techniques like dynamic time warping for temporal alignment and Viterbi algorithm for optimal path decoding. These improvements enable robust isolated word recognition, presenting new possibilities for both research and practical applications in speech recognition technology. The code structure includes modular components for feature extraction, model training, and recognition phases, allowing for easy customization and extension.