HMM-Based Speech Recognition System Implementation

I have implemented a speech recognition system based on Hidden Markov Models (HMMs). This program processes audio signals to identify spoken content through robust pattern recognition algorithms. Hidden Markov Models serve as statistical frameworks that effectively model stochastic processes with unobservable states. In speech recognition applications, HMMs mathematically represent the relationship between acoustic features and corresponding textual elements using state transition probabilities and emission distributions. The implementation involves several key components: feature extraction using Mel-Frequency Cepstral Coefficients (MFCCs) to capture vocal characteristics, Baum-Welch algorithm for model training to optimize parameters, and Viterbi algorithm for decoding the most probable word sequence. Through this systematic approach, the program achieves enhanced accuracy in interpreting speech inputs, thereby improving communication efficiency and human-computer interaction capabilities.