# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 3 Results and Discussions
### Performance Evaluation
It is clearly observed that, for the different VAD algorithms considered, G.729B produces a better performance for HR\({}_{\rm s}\) at different SNR levels for the different noises and suffers worst performance for HR\({}_{\rm ns}\). For airport noise, the proposed FE-SVM based VAD lags in HR\({}_{\rm s}\) to [13] (\(\sim\)12%) and [36] (\(\sim\)11%) and for FE-SVM \(-\) SS, HR\({}_{\rm s}\) lags to [13] (\(\sim\)14%) and [36] (\(\sim\)13%). For babble noise, proposed FE-SVM lags to [13] (\(\sim\)10%) and excels by [36] (\(\sim\)9%) and for FE-SVM \(-\) SS the HR\({}_{\rm s}\) performance lags to [13] (\(\sim\)0.4%) and excels [36] (\(\sim\)0.2%). For car noise, the proposed FE-SVM based VAD lags to both [13; 36] (\(\sim\)13% and \(\sim\)9%, respectively). Finally, for train noise, the proposed FE-SVM based VAD offers better HR\({}_{\rm s}\) against [36] (\(\sim\)2.4%) and suffers a lag against [13] (\(\sim\)0.4%). Most VAD algorithms rely on the use of post processing techniques like hangover schemes [37] which smooths the decisions  
Figure 6: Standard deviation 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. Figure 7: Accuracy comparisons for G.729, [PERSON] [36], [PERSON] [13], and proposed FE-SVM based VAD for SNR values from \(-\)10 dB to 10 dB (**a**) airport (**b**) babble (**c**) car and (**d**) train. at the frame level after the initial VAD decisions were made. This scheme reduces the risk of lower energy regions of speech at the ends of speech falsely rejected as noise. The number in the bracket indicates the approximate average hit-rates of speech and non-speech by which the proposed FE-SVM based VAD is better or worse than [13; 36]. Similarly, for HRns performance, as shown in Figure 9, the proposed FE-SVM based VAD outperforms all the VADs considered under different SNR levels for various noises. The average HRns of the proposed FE-SVM based VAD is \(\sim\)96% for airport noise, \(\sim\)98% for babble noise, \(\sim\)95% for car noise, and \(\sim\)96% for train noise. Without this post processing scheme, the proposed FE-SVM based VAD yields \(\sim\)90% average for HRs and \(\sim\)95% above for HRns. In Table 2, the average performance and average standard deviation of various VADs is compared with the proposed FE-SVM based VAD. From Table 2, it is clear that in terms of accuracy, the proposed FE-SVM based VAD is the best among all reference VAD algorithms considered here. The results  
Figure 8: HR comparisons for three VAD methods with our proposed method for five different SNR levels {\(-10\) dB, \(-5\) dB, \(0\) dB, \(5\) dB, \(10\) dB} (**a**) Airport noise—HRs (**b**) Babble noise—HRs (**c**) Car noise—HRs (**d**) Train noise—HRs. Figure 9: HRns comparisons for three VAD methods with our proposed method for five different SNR levels {\(-10\) dB, \(-5\) dB, \(0\) dB, \(5\) dB, \(10\) dB} (**a**) Airport noise—HRns (**b**) Babble noise—HRns (**c**) Car noise—HRns (**d**) Train noise—HRns. show that the proposed VAD outperforms the existing VADs under all noisy conditions at different SNR levels, \(\sim\)13% higher than that of VAD proposed by [13] in accuracy and \(\sim\)14% higher than [36]. Similarly, for hit rates, the proposed FE-SVM based VAD excels [13; 36] by \(\sim\)36% and \(\sim\)35%, respectively, for \(\mathrm{HR_{ns}}\) and lags [13; 36] by \(\sim\)9% and \(\sim\)6% for \(\mathrm{HR_{s}}\). Also for clean conditions, accuracy rate of the proposed FE-SVM based VAD is \(\sim\)15% higher than that of [13] and\(\sim\)2.5% higher than that of [36]. For hit-rates, the proposed FE-SVM based VAD lags to [13; 36] for \(\mathrm{HR_{s}}\) by \(\sim\)2% and \(\sim\)1%, respectively, and for \(\mathrm{HR_{ns}}\), the proposed FE-SVM based VAD leads by \(\sim\)36% and \(\sim\)8% to [13; 36], respectively. The performance metrics ensures that the proposed FE-SVM based VAD detects speech and non-speech frames efficiently, especially under low SNR conditions.