This paper compares the performance of large scale Support Vector Machine training algorithms tested on a language recognition task. We analyze the behavior of five SVM approaches for training phonetic and acoustic models, and we compare their performance in terms of number of iterations to reach convergence, training time and scalability towards large databases. Our results show that the accuracy of these algorithms is asymptotically equivalent, but they have different behavior with respect to the time required to converge. Some of these algorithms not only scale linearly with the training set size, but are also able to give their best results after just a few iterations on the database.