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Semi-supervised accent detection and modeling

In this paper, we propose an iterative refinement framework for semi-supervised accent detection, where the accent labels of training corpus were generated by the user's self-judgement with poor accuracy. Firstly, we get the initial accent detection models based on cross-validation (CV) method, and then select the pure accent samples iteratively based on cost criterion derived from neighbor function, which is sensitive to the accent class purity. SVM based accent recognition approach is applied as the basic accent detection method which assumes that certain phones are realized differently across accents. Finally, we update the accent specific acoustic models via adaptation based on the detected specific accent data. The efficiency of the proposed method is demonstrated with experiments on English dictation database.