HMM-based Speech Recognition in Noisy Environments Using hmm Files

The hmm files utilize Hidden Markov Model (HMM) algorithm to achieve robust speech recognition in noisy environments. Core modules include: vad.m performs voice activity detection using dual-threshold endpoint detection algorithm; mfcc.m extracts MFCC features through frame blocking, windowing, FFT, Mel-filtering, and DCT transformations; pdf.m computes output probabilities for observation vectors using multivariate Gaussian distribution with covariance matrices; mixture.m evaluates observation vectors against HMM states by linearly combining probabilities from multiple Gaussian mixtures; getparam.m calculates forward probabilities (alpha), backward probabilities (beta), and scaling factors for numerical stability; viterbi.m implements dynamic programming-based Viterbi algorithm for finding optimal state sequences; baum.m performs Baum-Welch re-estimation using expectation-maximization for HMM parameter training; inithmm.m initializes HMM parameters including transition matrices and emission probabilities; train.m orchestrates iterative training procedures with convergence checks; main.m serves as main script for training pipeline configuration; recog.m handles recognition process using trained models for pattern matching. Additional supporting files enhance project functionality: util.m provides utilities for data preprocessing (normalization) and postprocessing (smoothing); eval.m evaluates recognition performance using metrics like accuracy and confusion matrices; plot.m generates visualizations for feature distributions and algorithm convergence; save.m handles model persistence and result logging through structured file operations. By integrating these modules, the system achieves improved noise robustness through spectral analysis and probabilistic modeling, enhancing recognition accuracy in challenging acoustic environments through feature enhancement and statistical pattern recognition techniques.