In this paper we present the system we submitted to the WMT13 shared task on Quality Estimation. We participated to the Task 1.1. Each translated sentence is given a score between 0 and 1. The score is obtained by using several numerical or boolean features calculated according to the source and target sentences. We perform a linear regression of the feature space against scores in the range [0..1], to this end, we use a Support Vector Machine with 66 features. In this paper, we propose to increase the size of the training corpus. For that, we decide to use the post-edited and reference corpora in the training step after assigning a score to each sentence of these corpora. Then, we tune these scores on a development corpus. This leads to an improvement of 10.5% on the development corpus, in terms of Mean Average Error, but achieves only a sligth improvement on the test corpus.