Audio event recognition is a fascinating and challenging research topic in signal processing, audio retrieval, and pattern recognition. In this paper, we investigate GMM supervector based Universal Background Model (UBM) and Support Vector Machine (SVM) with MFCC features and various kernels for audio event recognition. A GMM supervector is obtained by adapting with UBM and cascading all the mean vector components. After that, the supervectors are applied as input features for SVM classifier. Experimental results belonging to our audio event database demonstrates that the proposed approach outperforms standard GMM-UBM baseline. Moreover, when applying SVM with GUMI kernel, error rate significantly decreases from 26.52% to 14.97% for 16 mixtures.