We present a comprehensive analysis of the use of I-vector based classifiers for the classification of unlabelled acoustic data as native British accents. We demonstrate the different behaviours of various popular dimensionality reduction techniques that have been previously used in problems such as speaker and language classification. Our results show that a fusion of I-vector based systems gives state-of-the-art performance for unlabelled classification of British accent speech data, reaching .81% accuracy.
Bibliographic reference. DeMarco, Andrea / Cox, Stephen J. (2013): "Native accent classification via i-vectors and speaker compensation fusion", In INTERSPEECH-2013, 1472-1476.