## 5 Experiment and Result
### Results  
This section presents, the result of noise robustness analysis of Cochleogram and Mel Spectrogram features in speaker identification and verification. The performance of Cochleogram and Mel Spectrogram features in speaker identification and verification is presented in the Tables 5 and 6, respectively. Sample speaker identification performance of both features using VGG-16 architecture is presented graphically as shown in the Figures 6-8 for the dataset without additive noise, medium noise and high noise ratio, respectively. For the clarity of the readers, the ratio of noise added to the VoxCeleb1 dataset is classified into three categories such as: low noise ratio (without additive noise), medium noise ratio (10 dB, 15 dB and 20 dB) and high noise ratio (\(-\)5 dB, 0 dB and 5 dB). At each level of SNR, the performance of both Cochleogram and Mel Spectrogram features in speaker identification and verification is analyzed and presented in the Tables 5 and 6, respectively. From the Figure 6, we can see that the speaker identification performance of both Cochleogram and Mel Spectrogram is approximately equal with the dataset without additive noise. Figure 7 presents that Cochleogram features achieved better performance than Mel Spectrogram features on the datasets with medium noise ratio. Figure 8 shows that Cochleogram shows superior performance than Mel Spectrogram features on the datasets with high noise ratio. Figure 6: Speaker identification Accuracy of Cochleogram and Mel Spectrogram at Low Noise Ratio (Without Additive Noise) using VGG-16 Architecture. Table 5 presents more detail about the identification performance of Cochleogram and Mel Spectrogram at different ratio of noise and using different types of deep learning architectures such as: (Basic 2 DCNN, VGG-16, ResNet-50, TDNN and TitaNet). In the Table 5, the results show that the Cochleogram features achieved superior performance than Mel Spectrogram features on the dataset with high noise ratio. For example, the accuracy of Cochleogram features using VGG-16 at SNR of \(-\)5 dB 0 dB and 5 dB is 75.77%, 89.38% and 93.94% which is much better than the accuracy of Mel Spectrogram features at SNR of \(-\)5 dB, 0 dB and 5 dB which is 51.96%, 70.82% and 85.3%. The results in the Table 5, also shows that Cochleogram features achieved better performance than Mel Spectrogram  
Figure 8: Speaker identification performance of Cochleogram vs. Mel Spectrogram at high noise ratio using VGG-16. Figure 7: Speaker Identification Accuracy of Cochleogram vs. Mel Spectrogram at Medium Noise Ratio using VGG-16 Architecture. features on the dataset with medium noise ratio. For example, the accuracy of Cochleogram using VGG-16 at SNR of 10 dB, 15 dB and 20 dB is 95.96%, 96.79% and 97.32%, respectively, which is better than the accuracy of Mel Spectrogram at SNR of 10 dB, 15 dB and 20 dB which is 91.64%, 92.81 and 95.77%, respectively. On the dataset without additive noise, Cochleogram features achieved comparative accuracy with Mel Spectrogram features. The accuracy of Cochleogram and Mel Spectrogram using VGG-16 network is 98% and 97%, which is comparatively approximate. Generally, Cochleogram features achieved better performance than Mel Spectrogram features in speaker identification on the noisy datasets. Table 6, presents the Speaker verification performance of both Cochleogram and Mel Spectrogram features at SNR level from \(-\)5 dB to 20 dB using deep learning architectures which is discussed at Tables 1-3, at Figures 3 and 5. The results in the Table 6, show that Cochleogram features have superior performance than Mel Spectrogram features in speaker verification at the high noise ratio (\(-\)5 dB, 0 dB and 5 dB) in the dataset. For instance, using VGG-16 architecture Cochleogram features achieved an EER of 15.42%, 12.86% and 9.10% at the SNR level \(-\)5 dB, 0 dB and 5 dB, respectively, which is minimum error rate compared to the EER of Mel Spectrogram at similar SNR level which is 18.83%, 15.71% and 11.92%.