MATLAB Implementation of Speech Recognition with HMM and DTW Algorithms

This MATLAB code provides a comprehensive speech recognition solution, complete with test audio samples for validation purposes. The implementation incorporates two fundamental approaches: HMM (Hidden Markov Model) and DTW (Dynamic Time Warping). Speech recognition represents a sophisticated and crucial technology that converts human speech signals into text or executable commands. The HMM implementation utilizes statistical modeling to represent speech patterns through state transitions and probability distributions. Key functions include training HMM parameters using Baum-Welch algorithm and recognition via Viterbi decoding, which calculates the most probable state sequence for input speech features. The DTW approach employs dynamic programming to measure similarity between variable-length speech sequences by finding optimal alignment paths. The code implements endpoint detection, feature extraction (MFCC coefficients), and warping path calculation to handle temporal variations in speech signals. By combining these methodologies, the system enhances recognition accuracy and robustness - HMM handles temporal pattern modeling while DTW addresses timing variations. The MATLAB implementation includes complete workflow from audio preprocessing (frame blocking, windowing) to final classification, making it suitable for testing and research applications. The code structure modularizes feature extraction, model training, and pattern matching components for easy customization and extension.