  Voice activity detection (VAD), used as the front end of speech enhancement,
speech and speaker recognition algorithms, determines the overall accuracy and
efficiency of the algorithms. Therefore, a VAD with low complexity and high
accuracy is highly desirable for speech processing applications. In this paper,
we propose a novel training method on large dataset for supervised
learning-based VAD system using support vector machine (SVM). Despite of high
classification accuracy of support vector machines (SVM), trivial SVM is not
suitable for classification of large data sets needed for a good VAD system
because of high training complexity. To overcome this problem, a novel
ensemble-based approach using SVM has been proposed in this paper.The
performance of the proposed ensemble structure has been compared with a
feedforward neural network (NN). Although NN performs better than single
SVM-based VAD trained on a small portion of the training data, ensemble SVM
gives accuracy comparable to neural network-based VAD. Ensemble SVM and NN give
88.74% and 86.28% accuracy respectively whereas the stand-alone SVM shows
57.05% accuracy on average on the test dataset.
