In this paper, three utterance modelling approaches, namely Gaussian Mean Supervector (GMS), i-vector and Gaussian Posterior Probability Supervector (GPPS), are applied to the accent recognition problem. For each utterance modeling method, three different classifiers, namely the Support Vector Machine (SVM), the Naive Bayesian Classifier (NBC) and the Sparse Representation Classifier (SRC), are employed to find out suitable matches between the utterance modelling schemes and the classifiers. The evaluation database is formed by using English utterances of speakers whose native languages are Russian, Hindi, American English, Thai, Vietnamese and Cantonese. These utterances are drawn from the National Institute of Standards and Technology (NIST) 2008 Speaker Recognition Evaluation (SRE) database. The study results show that GPPS and i-vector are more effective than GMS in this accent recognition task. It is also concluded that among the employed classifiers, the best matches for i-vector and GPPS are SVM and SRC, respectively.
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