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.

MATLAB-based Endpoint Detection Program Using Energy and Zero-Crossing Rate

A MATLAB implementation of endpoint detection algorithm combining energy analysis and zero-crossing rate calculation for accurate audio signal segmentation

Voice Activity Detection Program with Multi-band Spectral Analysis

Voice Activity Detection program implementing multi-band analysis, entropy measurements, spectral entropy, and Teager energy computation for robust speech signal processing applications