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Assamese Dialect Identification using Semi-supervised Learning

The novel approach of this paper is the introduction of a novel information blending paradigm. Two basic classifiers are first trained using the shifting delta cepstra (SDC) and prosodic features, respectively. To improve Assamese dialect detection accuracy, the co-training approach is used in semi-supervised learning. This system evaluates four Assamese dialects. The experimental data show that the suggested system performs better than the existing system that used GMM.