Speech Recognition System Implementation Using Hidden Markov Models

This is a self-developed speech recognition system built upon Hidden Markov Models (HMMs) for audio signal processing. The program analyzes audio waveforms through feature extraction (typically MFCC coefficients) and compares them against pre-trained HMM patterns to identify spoken content. The implementation leverages HMM's probabilistic framework, where Gaussian Mixture Models (GMMs) handle emission probabilities while Viterbi algorithm performs optimal path decoding for recognition. The system architecture supports vocabulary recognition at multiple levels - isolated words, phrases, and continuous speech. Through modular design, the program allows flexibility for performance enhancement by incorporating additional training datasets and optimizing model parameters (transition probabilities, emission distributions). Key implemented functions include Baum-Welch algorithm for HMM training and forward-backward procedure for probability calculation. This HMM-based recognition system demonstrates scalability potential through iterative model refinement and data expansion strategies.