Description: hmm HMM algorithm file using speech recognition in noisy environments. Which is the endpoint detection process vad.m mfcc.m procedure is to calculate the MFCC parameters pdf.m function is calculated for a given observation vector of the Gaussian probability density function of output probability mixture.m is to calculate the observation vector for a HMM state output probability of observation vector is the number of Gaussian mixture per state output probability of the linear combination getparam.m before the calculation of the probability function, backward probability, calibration coefficients and other parameters viterbi.m is Viterbi algorithm implementation baum.m Baum-Welch algorithm to achieve inithmm.m is the initialization parameters train.m is the training program main.m training program is a script file recog.m is to identify procedures.
- [cdhmm] - under Matlab for the voice recognition a
- [fenci] - Segmentation procedure, HMM model traini
- [Create] - Speech Recognition system using HMM...in
- [HMM] - Hidden Markov classic literature, and us
- [papers] - Speech recognition information, most of
- [HMM-lunwen] - Based on Hidden Markov HMM speech recogn
- [ProjectSpeech_HMM] - Speech recognition using HMM
- [matlabPROGRAM] - Matlab on the speech recognition process
- [HMM] - HMM toolbox matlab available, including
- [hmm] - Speech recognition algorithm written in
File list (Check if you may need any files):
hmm\baum.m
...\getparam.m
...\hmm.mat
...\inithmm.asv
...\inithmm.m
...\main.m
...\mfcc.m
...\mixture.m
...\pdf.m
...\recog.asv
...\recog.m
...\samples.mat
...\train.m
...\vad.m
...\viterbi.m
hmm
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