Johny Elton

Voice activity detection is currently a challenging task that is applicable in real time applications such as speech coding and recognition. It is due to the low signal-to-noise ratio that affected the structural properties. Voice activity detection helps in detecting the speech region that is present in various nonstationary noises. The literature associated with Voice activity detection suggests that numerous works use unbalanced classification approach with higher and poor, speech and non-speech detection rates, respectively. This leads to the condition that majority of the noisy segments are categorized as speech. Hence, to overcome this issue, we propose a novel modified global thresholding scheme that has a fuzzy entropy tool. Our proposal can effectively identify both regions by locating the transition from non-speech to speech areas and vice versa. This will improve the detection rates as misclassification error of noisy segments as speech segments are minimized. The perform...