Classic MATLAB Implementation of HMM Algorithm
Classic MATLAB implementation of Hidden Markov Model (HMM) algorithm for pattern recognition and artificial intelligence applications, featuring complete training and recognition modules
MATLAB Speech Recognition Code Using Hidden Markov Model (HMM) Algorithm
MATLAB-based speech recognition implementation utilizing Hidden Markov Model (HMM) algorithm with feature extraction and acoustic modeling techniques
HMM-based Speech Recognition in Noisy Environments Using hmm Files
The hmm files implement Hidden Markov Model (HMM) algorithm for speech recognition under noisy conditions. Key components include: vad.m for endpoint detection using energy-based thresholding; mfcc.m for Mel-Frequency Cepstral Coefficients extraction with filter bank processing; pdf.m computing Gaussian probability density output for observation vectors; mixture.m calculating state output probabilities through Gaussian mixture modeling; getparam.m deriving forward/backward probabilities and scaling coefficients; viterbi.m implementing Viterbi algorithm for optimal path decoding; baum.m executing Baum-Welch algorithm for parameter re-estimation; inithmm.m initializing HMM parameters; train.m handling model training procedures.