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This paper shows an effective speech/non-speech discrimination method for improving the performance of speech processing systems working in noisy environment. The proposed method uses a trained support vector machine (SVM) that defines an optimized non-linear decision rule over different sets of speech features. Two alternative feature extraction processes(More)
Detecting the presence of speech in a noisy signal is an unsolved problem affecting numerous speech processing applications. This paper shows an effective method employing support vector machines (SVM) for voice activity detection (VAD) in noisy environments. The use of kernels in SVM enables to map the data into some other dot product space (called feature(More)
An effective speech event detector is presented in this work for improving the performance of speech processing systems working in noisy environment. The proposed method is based on a trained support vector machine (SVM) that defines an optimized non-linear decision rule involving the subband SNRs of the input speech. It is analyzed the classification rule(More)
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