语音合成与识别

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Matlab

evalKmeansModel.m：源码内容

							% Resubstitution performance evaluation of GMM for speaker recognition
addpath /users/jang/matlab/toolbox/dcpr
addpath /users/jang/matlab/toolbox/cbmr
more off
if ~exist('speakerData'),
	fprintf('Loading speakerData.mat...n');
	load speakerData.mat
end
if ~exist('waveData'),
	fprintf('Loading waveData.mat...n');
	load waveData.mat
end
% Get the sample data
sampleData = [];
for i=1:length(speakerData),
	feature = speakerData(i).kmeansParam;
	out = i*ones(size(feature,1), 1);
	sampleData = [sampleData; [feature, out]];
end
%profile on -detail builtin
toc
for i=1:length(speakerData),
	speaker = speakerData(i).speaker;
	index = findcell({waveData.speaker}, speaker);
	speakerData(i).kmeansRank = zeros*index;
	for j = 1:length(index),
		fprintf('Evaluate wave file %g/%g of %s...n', j, length(index), speaker);
		feature = waveData(index(j)).feature;
		[eachClass, ensembleClass] = knn(sampleData, feature', 1);
		[a, b] = countele(eachClass);
		index2 = find(a==i);
		if isempty(index2),
			speakerData(i).kmeansRank(j) = 10000;
		else
			speakerData(i).kmeansRank(j) = sum(b>=b(index2));
		end
	end
end
fprintf('Total execution time = %gn', toc);
%profile report
figure;
rank = cat(2, speakerData.kmeansRank);
plotpie(rank);
title('Performance of Kmeans');
save speakerData speakerData

						