语音合成与识别

开发平台：

Matlab

getdist.m：源码内容

							load waveData.mat
allSpeakers = {waveData.speaker};
index1 = findcell(allSpeakers, '1Roger');
index2 = findcell(allSpeakers, '1Gavins');
index3 = findcell(allSpeakers, '1Gao');
index4 = findcell(allSpeakers, '1Jtchen');
index = [index1, index2, index3, index4];
waveData = waveData(index);
% ====== Compute distance matrix based on original features
waveNum = length(waveData);
distmat = zeros(waveNum);
for i=1:waveNum;
	fprintf('i = %dn', i);
	for j=(i+1):waveNum;
		featureI = waveData(i).feature;
		featureJ = waveData(j).feature;
		distmat(i,j) = dtwmex0(featureI, featureJ, 2, 1);
	end
end
distmat = distmat+distmat';
save dist0 distmat
% ====== Feature transformation
feature = cat(2, waveData.feature);
mu = mean(feature, 2);
sigma = sqrt(diag(cov(feature')));
feature = (feature-mu*ones(1, size(feature,2)))./(sigma*ones(1,size(feature,2)));
% ====== Compute distance matrix based on normalized features
waveNum = length(waveData);
distmat = zeros(waveNum);
for i=1:waveNum;
	fprintf('i = %dn', i);
	for j=(i+1):waveNum;
		featureI = waveData(i).feature;
		dataNumI = size(featureI, 2);
		featureI = (featureI-mu*ones(1, dataNumI))./(sigma*ones(1,dataNumI));
		featureJ = waveData(j).feature;
		dataNumJ = size(featureJ, 2);
		featureJ = (featureJ-mu*ones(1, dataNumJ))./(sigma*ones(1,dataNumJ));
		distmat(i,j) = dtwmex0(featureI, featureJ, 2, 1);
	end
end
distmat = distmat+distmat';
save dist1 distmat

						