gmm-ubm的matlab官方demo似乎已经足够解决你的问题，

%%

% Step0: Set the parameters of the experiment

nSpeakers = 20;

nDims = 13;             % dimensionality of feature vectors

nMixtures = 32;         % How many mixtures used to generate data

nChannels = 10;         % Number of channels (sessions) per speaker

nFrames = 1000;         % Frames per speaker (10 seconds assuming 100 Hz)

nWorkers = 1;           % Number of parfor workers, if available

% Pick random centers for all the mixtures.

mixtureVariance = .10;

channelVariance = .05;

mixtureCenters = randn(nDims, nMixtures, nSpeakers);

channelCenters = randn(nDims, nMixtures, nSpeakers, nChannels)*.1;

trainSpeakerData = cell(nSpeakers, nChannels);

testSpeakerData = cell(nSpeakers, nChannels);

speakerID = zeros(nSpeakers, nChannels);

% Create the random data. Both training and testing data have the same

% layout.

for s=1:nSpeakers

trainSpeechData = zeros(nDims, nMixtures);

testSpeechData = zeros(nDims, nMixtures);

for c=1:nChannels

for m=1:nMixtures

% Create data from mixture m for speaker s

frameIndices = m:nMixtures:nFrames;

nMixFrames = length(frameIndices);

trainSpeechData(:,frameIndices) = ...

randn(nDims, nMixFrames)*sqrt(mixtureVariance) + ...

repmat(mixtureCenters(:,m,s),1,nMixFrames) + ...

repmat(channelCenters(:,m,s,c),1,nMixFrames);

testSpeechData(:,frameIndices) = ...

randn(nDims, nMixFrames)*sqrt(mixtureVariance) + ...

repmat(mixtureCenters(:,m,s),1,nMixFrames) + ...

repmat(channelCenters(:,m,s,c),1,nMixFrames);

end

trainSpeakerData{s, c} = trainSpeechData;

testSpeakerData{s, c} = testSpeechData;

speakerID(s,c) = s;                 % Keep track of who this is

end

end

%%

% Step1: Create the universal background model from all the training speaker data

nmix = nMixtures;           % In this case, we know the # of mixtures needed

final_niter = 10;

ds_factor = 1;

ubm = gmm_em(trainSpeakerData(:), nmix, final_niter, ds_factor, nWorkers);

%%

% Step2: Now adapt the UBM to each speaker to create GMM speaker model.

map_tau = 10.0;

config = 'mwv';

gmm = cell(nSpeakers, 1);

for s=1:nSpeakers

gmm{s} = mapAdapt(trainSpeakerData(s, :), ubm, map_tau, config);

end

%%

% Step3: Now calculate the score for each model versus each speaker's data.

% Generate a list that tests each model (first column) against all the

% testSpeakerData.

trials = zeros(nSpeakers*nChannels*nSpeakers, 2);

answers = zeros(nSpeakers*nChannels*nSpeakers, 1);

for ix = 1 : nSpeakers,

b = (ix-1)*nSpeakers*nChannels + 1;

e = b + nSpeakers*nChannels - 1;

trials(b:e, :)  = [ix * ones(nSpeakers*nChannels, 1), (1:nSpeakers*nChannels)'];

answers((ix-1)*nChannels+b : (ix-1)*nChannels+b+nChannels-1) = 1;

end

gmmScores = score_gmm_trials(gmm, reshape(testSpeakerData', nSpeakers*nChannels,1), trials, ubm);

%%

% Step4: Now compute the EER and plot the DET curve and confusion matrix

imagesc(reshape(gmmScores,nSpeakers*nChannels, nSpeakers))

title('Speaker Verification Likelihood (GMM Model)');

ylabel('Test # (Channel x Speaker)'); xlabel('Model #');

colorbar; drawnow; axis xy

figure

eer = compute_eer(gmmScores, answers, true);