This paper proposes a novel automatic pronunciation scoring framework using learning to rank. Human scores of the utterances are treated as ranks and are used as the ranking ground truths. Scores generated from various existing scoring methods are used as the features to train the learning to rank function. The output of the function is then segmented by the proposed DP-based method and hence boundaries between clusters can be used to determine the discrete computer scores. Experimental results show that the proposed framework improves upon the existing scoring methods. A non-native corpus with human ranks is also released.