In earlier studies, we employed a large prosodic feature vec-tor to assess the quality of L2 learner's utterances with respect to sentence melody and rhythm. In this paper, we combine these features with two standard approaches in paralinguistic analysis: (1) features derived from a Gaussian Mixture Model used as Universal Background Model (GMM-UBM), and (2) openSMILE, an open-source toolkit for extracting acoustic fea-tures. We evaluate our approach with English speech from 94 non-native speakers perceptually scored by 62 native labellers. GMM-UBM or openSMILE modelling alone yields lower per-formance than our prosodic feature vector; however, adding in-formation from the GMM-UBM modelling or openSMILE by late fusion improves results.