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Accent identification has grown over the past decade. There has been decent success when a priori knowledge about the accents is available. A typical approach entails detection of certain syllables and phonemes, which in turn requires phoneme-based models. Recently, Gaussian Mixture Models (GMMs) have been used as an unsupervised alternative to these phoneme-based models, but they have had limited success unless they used a priori knowledge. We studied extensions of the GMMs using ensemble learning (i. e. bagging and Boosting).