Well, there is an answer here http://www.mathworks.com/matlabcentral/answers/64475-does-anybody-have-expertise-with-matlab-svm-classifier, with a reference to another thread. I am collecting all the pieces in one place.
Assume your class labels are -1 and +1, assume that you have trained with 'autoscale' set to true by default, let svm be the struct for the trained SVM model, and let Xnew be the new data for which you need to compute the soft scores.
shift = svm.ScaleData.shift;
scale = svm.ScaleData.scaleFactor;
Xnew = bsxfun(@plus,Xnew,shift);
Xnew = bsxfun(@times,Xnew,scale);
sv = svm.SupportVectors;
alphaHat = svm.Alpha;
bias = svm.Bias;
kfun = svm.KernelFunction;
kfunargs = svm.KernelFunctionArgs;
f = kfun(sv,Xnew,kfunargs{:})'*alphaHat(:) + bias;
f = -f; % flip the sign to get the score for the +1 class
Then call perfcurve(true_labels,f,1).
