# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 1 Introduction  
Voice activity detection (VAD) is a speech-processing technique which discriminates speech from non-speech regions. Silence, noise, or other unrelated acoustic information can be treated as non-speech regions. But the challenge to VAD is to detect speech under low signal-to-noise ratio (SNR) scenarios and also under the influence of nonstationary noises which cause significant errors. The main applications related to VAD are speech coding [1] and speech recognition [2]. VAD stands as a preprocessing stage for major speech processing applications. The applications of VAD extend to mobile communications [3], transmitting speech signals over internet [4], and suppressing noises in digital hearing aids [5]. Being a predominant stage in many speech processing applications, the basic design of a VAD can be summarized by the following steps: a noise reduction step, feature extraction step, and, finally, a classification step in order to distinguish speech and non-speech regions. The noise reduction step plays a crucial role in VAD, because it detects speech pauses in the process of estimating noises present in the speech signal. The well-known noise reduction algorithms proposed by [6; 7] are widely used in robust speech recognition tasks, which eventually helps VAD in attaining high performance. In the feature extraction step, acoustic features are usually considered in order to distinguish speech and noise. Traditional VADs rely on energy [8] and some other VADs include zero crossing rate (ZCR) and energy difference between speech and non-speech proposed by [9]. Some algorithmsuse correlation coefficients [10], others wavelet coefficients [11] and cepstral distance features [12]. [PERSON] et al. [13] proposed long-term spectral divergence, speech periodicity [14], and speech periodic component to aperiodic component ratio [15]. But these traditional features lack robustness under noisy conditions. So various algorithms include multiple features to detect speech under various noisy conditions. All-band spectra and sub-band spectra of Wiener filter are used in [16], and higher order statistics in [17]. These features, however, improve the accuracy of the VAD in some conditions, but are still lacking for low SNR conditions. VAD accuracy and performance rely on the final classification phase. Finally, with the classification step, VAD results mainly rely on decision rule or by a threshold on the features extracted from the speech signal. The decision rule is either a simple threshold-based approach or some statistical models. Different classifiers based on machine learning algorithms (MLA) are also invoked for VAD results. One way is with the use of neural networks. Supervised neural networks have been widely used in classification, but the disadvantage with neural networks is its cost expensive training procedures. Other frequently used classifiers are \(k\)-nearest neighbor (\(k\)-NN) and support vector machine (SVM) classifiers [18; 19; 20]. \(k\)-NN algorithm is a nonparametric MLA used in classification, where the classification takes place using the majority votes of its neighbors. Another nonparametric MLA proposed by [21] is the SVM classifier, which is one of the powerful tools used to classify speech and non-speech signals, because of its convergence speed in the training phase, which is faster when compared to other classifiers. In our proposed method, SVM classifier is used for VAD under various noisy conditions. Irrespective of decision rules or any other classifier, selection of appropriate features will always impact the performance of VAD, because there are no unique features or multiple features considered with regard to improving the performance of VAD under various noisy conditions. So the problem regarding VAD is extremely challenging to the researchers. In our proposed method, fuzzy entropy is introduced as the feature extracted over the speech signal.