# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 3 Results and Discussions
### Performance Evaluation
From the figure, it clearly states that G.729 suffers poor accuracy for airport noise with \(\sim\)50% on average at various SNRs, whereas VAD algorithms by [13; 36] performs better accuracy for airport noise, gradually varying at different SNR levels averaging \(\sim\)71% and \(\sim\)73% respectively. The proposed FE-SVM based VAD outperforms the rest, yielding \(\sim\)92% average and \(\sim\)90% without spectral subtraction (SS). Similarly, for the \(k\)-NN classifier, the accuracy is \(\sim\)89% and \(\sim\)88% with and without SS, respectively. For babble noise, the proposed FE-SVM based VAD has an accuracy of \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Noise Type**} & \multicolumn{5}{c}{**SNR Levels**} \\ \cline{2-6}  & \(\mathbf{-10}\) & \(\mathbf{-5}\) & \(\mathbf{0}\) & \(\mathbf{5}\) & \(\mathbf{10}\) \\ \cline{2-6}  & **SVM, \(\boldsymbol{k}\)-NN** & **SVM, \(\boldsymbol{k}\)-NN** & **SVM, \(\boldsymbol{k}\)-NN** & **SVM, \(\boldsymbol{k}\)-NN** \\ \hline \multirow{2}{*}{Airport} & 3.52 \(\pm\) 0.53, & 4.79 \(\pm\) 0.96, & 8.56 \(\pm\) 1.84, & 9.5 \(\pm\) 1.93, & 9.26 \(\pm\) 1.89, \\  & 11.07 \(\pm\) 1.13 & 11.7 \(\pm\) 1.86 & 10.21 \(\pm\) 2.09 & 10.38 \(\pm\) 2.13 & 11.94 \(\pm\) 2.23 \\ \hline \multirow{2}{*}{Babble} & 2.61 \(\pm\) 0.37, & 4.82 \(\pm\) 0.98, & 7.16 \(\pm\) 1.47, & 9.02 \(\pm\) 1.88, & 9.07 \(\pm\) 1.9, \\  & 11.59 \(\pm\) 2.17 & 13.15 \(\pm\) 1.76 & 11.76 \(\pm\) 1.74 & 11.59 \(\pm\) 1.53 & 11.76 \(\pm\) 1.56 \\ \hline \multirow{2}{*}{Car} & 8.72 \(\pm\) 0.94, & 10.74 \(\pm\) 0.98, & 11.49 \(\pm\) 1.13, & 10.14 \(\pm\) 1.71, & 9.7 \(\pm\) 1.62, \\  & 10.55 \(\pm\) 1.16 & 11.42 \(\pm\) 1.01 & 11.76 \(\pm\) 1.25 & 9.86 \(\pm\) 1.32 & 10.9 \(\pm\) 1.8 \\ \hline \multirow{2}{*}{Train} & 5.17\(\pm\) 0.47, & 3.61 \(\pm\) 0.15, & 2.34 \(\pm\) 0.11, & 1.97 \(\pm\) 0.15, & 2.1 \(\pm\) 0.09, \\  & 10.38 \(\pm\) 1.25 & 11.25 \(\pm\) 2.01 & 7.44 \(\pm\) 1.51 & 10.03 \(\pm\) 1.92 & 11.25 \(\pm\) 1.97 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error rate of the VAD by support vector machine (SVM), \(k\)-nearest neighbor (\(k\)-NN) in % for the corrupted speech signal under various noise types for different signal-to-noise ratio (SNR) levels. average \(\sim\)93% and \(\sim\)89% without SS, whereas [13] produces \(\sim\)81% and [36] and G.729 manages \(\sim\)64% and \(\sim\)50%, respectively. Similarly, using \(k\)-NN classifier, an accuracy of \(\sim\)88% and \(\sim\)89% was obtained with and without SS, respectively. For car noise, [36] dominates with an average of \(\sim\)93% and the proposed FE-SVM based VAD produces \(\sim\)90% and \(\sim\)84% without SS, whereas [13] yields around \(\sim\)82% and G.729 with an accuracy of \(\sim\)74%. The accuracy from \(k\)-NN classifier is \(\sim\)88% with and without SS. Finally, for train noise, G.729 yields an accuracy of \(\sim\)60%, [13,36] were around \(\sim\)82% and \(\sim\)86% respectively, but the proposed FE-SVM based VAD outperforms with the best accuracy of \(\sim\)96% and \(\sim\)97% with and without SS, respectively. Similarly, while using \(k\)-NN classifier of \(\sim\)92% and \(\sim\)90% with and without using SS, respectively. This shows that the proposed FE-SVM based VAD yields a better accuracy rate for all noises except for the car interior noise. This is due to the effect of SS where the portion of speech was also cancelled along with the complex audible event encountered in the car interior noise. Figure 4: Average computing time of the SVM classifier for the various noises such as airport, babble, car, and train. Figure 5: Average of (**a**) sensitivity (**b**) specificity and (**c**) F-Measure for the proposed FuzzyEn based VAD for the various noises such as airport, babble, car, and train. Figures 8 and 9 show hit rate performance evaluation metrics for five SNRs for different kinds of noises computed for G.729B, [13; 36] and proposed FE-SVM based VAD algorithms. Figure 8 provides comparison results for HR\({}_{\rm s}\), and Figure 9 provides HR\({}_{\rm ns}\) results for the various VAD algorithms.