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This paper presents a comparative evaluation of different methods for voice activity detection (VAD). A novel feature set is proposed in order to improve VAD performance in diverse noisy environments. Furthermore, three classifiers for VAD are evaluated. The three classifiers are Gaussian Mixture Model (GMM), Support Vector Machine (SVM) and Decision Tree (DT). Experimental results show that the proposed feature set achieves better performance than spectral entropy. In the comparison of the classifiers, DT shows the best performance in terms of frame-based VAD accuracy as well as computational cost.