Ensemble Learning Approach with Application to Chinese Dialect Identification

Abstract

In this paper we propose ensemble learning based approach to identify Chinese dialects. This new method firstly uses Gaussian Mixture Models and N-gram language models to produce a set of base learners. Then the two typical ensemble learning approach, Bagging and AdaBoost are conducted to combine the base learner to determine the dialect category. The ANN is selected as weak learner. The experimental results show that the ensemble approach not only enhances the performance of the system greatly, but also reduces the contradiction between the training data and the number of parameters in models.