function sidOpt=sidOptSet
% sidOptSet: Set parameters for speaker identification/verification 

% ====== Add necessary paths
addpath d:/users/jang/matlab/toolbox/utility
addpath d:/users/jang/matlab/toolbox/machineLearning
addpath d:/users/jang/matlab/toolbox/sap
addpath d:/users/jang/matlab/toolbox/asr -end

% ====== Method
sidOpt.method='dtw';			% 'dtw' (for text-dependent speaker ID) or 'gmm' (for text-independent speaker ID)
sidOpt.useGtEpd=0;				% Use GT EPD labelled by human, only available for "mir-2010-speakerId_label"
sidOpt.epdFcn='epdByVol';		% epdByVol is better for GMM.

% ====== Corpus directories
%sidOpt.auDir01='d:/dataset/鈦映科技-2008-語者辨識/第一次錄音檔(100人)';	% Session 1
%sidOpt.auDir02='d:/dataset/鈦映科技-2008-語者辨識/第二次錄音檔(100人)';	% Session 2
sidOpt.auDir01='d:\dataSet\mir-2010-speakerId_label\session01';		% Session 1, with GT ednpoints
sidOpt.auDir02='d:\dataSet\mir-2010-speakerId_label\session02';		% Session 2, with GT ednpoints
% ====== Output dirs and files
[parentDir, mainName]=fileparts(fileparts(sidOpt.auDir01));
sidOpt.outputDir=['output/', mainName];		% Output directory, such as 'mir-2010-speakerId_label'
sidOpt.errorLogFile=[sidOpt.outputDir, '/error.log'];
% ====== Use partial data for fast verification
sidOpt.maxSpeakerNum=3;
sidOpt.maxSpeakerNum=inf;
sidOpt.sentenceNumPerSpeaker=3;		% Use 3 utterance (1 password) per person, for fast computation
sidOpt.sentenceNumPerSpeaker=inf;
% === Evaluation mode
sidOpt.mixData=0;	% 1 for mix mode (two datasets are mixed, odd-indexed for training and even-indexed for testing)

% ====== Feature/waveform related
% === Feature type
sidOpt.feaType='mfcc';		% 'mfcc', 'spectrum', 'volume', 'pitch'
sidOpt.useEnergy=1;			% mfcc dim=13 if useEnergy = 1; mfcc dim=12 if useEnergy=0
% === Temporal normalization mode
sidOpt.temporalNormMode=1;		% 0: nothing, 1: CMS (cepstrum mean subtraction), 2: CN (cepstrum normalization)
% === Feature transformation mode
sidOpt.feaTransformMode=0;		% 0: nothing, 1: LDA, 2: PCA
sidOpt.transformedFeaDim=10;	% This is used only when the above option is 1 or 2
% === Feature/waveform options
sidOpt.useWaveformNormalization=0;	% 0 or 1
% === Use integer feature for embedded devices
sidOpt.useIntFea=0;			% Use integer feature (MFCC) for smartphone

switch(sidOpt.method)
	case 'gmm'	% ====== GMM parameters
		% === GMM model parameters
		sidOpt.exponent4gaussianNum=1:8;
		sidOpt.gaussianNums=2.^sidOpt.exponent4gaussianNum;
		sidOpt.covType=1;
		% === GMM training parameters
		sidOpt.gmmTrainParam.dispOpt=0;
		sidOpt.gmmTrainParam.useKmeans=1;
		sidOpt.gmmTrainParam.maxIteration=50;
		% === Use integer GMM for smartphone
		sidOpt.useIntGmm=0;
	case 'dtw'	% ====== DTW parameters
		% === DTW type
		sidOpt.dtwFunc='dtw2';		% Set this to 'dtw1' (27-45-63) or 'dtw2' (0-45-90)
		sidOpt.useDtwPartialComputation=0;	% Speedup via partial computation (does not speed up significantly)
	otherwise
		error('Unknown method!');
end