% Evaluate utterance-based recognition rate for speaker identification

tInit=clock;
addMyPath;
eval(sprintf('load %s\gmmParam.mat', sidOpt.outputDir));
% ====== Outside test for speakerSet3
sidOpt=sidOptSet;
sidOpt.waveDir='\dataSet\【鈦映科技】語者確認錄音2';
% ====== Generate speakerSet
if ~exist([sidOpt.outputDir, '/speakerSet3.mat'])
	fprintf('Get info of all wave files from %s\n', sidOpt.waveDir);
	speakerSet3=speakerSetRead(sidOpt.waveDir, sidOpt.sentenceNumPerSpeaker);
	speakerSet3=speakerSet3(1:min(sidOpt.maxSpeakerNum, length(speakerSet3)));
	% ====== Add mfcc to speakerSet
	fprintf('Read wave files and do feature extraction...\n');
	speakerSet3=speakerSetAddFea(speakerSet3, sidOpt);
	save output\mat\speakerSet3 speakerSet3
else
	load output\mat\speakerSet3.mat
end

% ====== Remove individuals not in the training data
load output\mat\speakerSet.mat
name1={speakerSet1.name};
name3={speakerSet3.name};
missingName=setdiff(name3, name1);
deleteIndex=[];
for i=1:length(missingName)
	deleteIndex=[deleteIndex, find(strcmp(missingName{i}, name3))];
end
speakerSet3(deleteIndex)=[];

% ====== Compute RR for speakerSet3
fprintf('Outside test for speakerSet3:\n');
[overallRecogRate3, confMat3, speakerSet3]=speakerIdentify(speakerSet3, gmm, sidOpt.useIntGmm);
fprintf('Overall outside-test recognition rate is %.2f%%.\n', overallRecogRate3*100);
listPersonRr(speakerSet3, confMat3);
% ====== Plot confusion matrix
className={speakerSet3.name};
figure; confMatPlot(confMat3, className);
eval(['print -dpng output\png\', mfilename]);
fprintf('%s ===> %g seconds\n', mfilename, etime(clock, tInit));