## 5 Experiment and Result
### Results
Cochleogram features also shows better performance than Mel Spectrogram feature in speaker verification at the medium noise ratio and without additive noise in the dataset. For example, VGG-16 architecture in the Table 6 show that Cochleogram achieved an EER of 7.95%, 6.61% and 4.55% at SNR level of 10 dB, 15 dB and 20 dB which is minimum error rate compared to the EER of Mel Spectrogram at similar SNR level which is 9.74%, 8.37% and 5.86% at 10 dB, 15 dB and 20 dB. Generally, the results in the Tables 3 and 4 show that Cochleogram features achieved superior performance in both speaker identification and verification on the noisy data compared to the Mel Spectrogram features. In addition, in the improved deep learning architectures the Cochleogram features performance also shows better performance. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**Feature Type**} & \multicolumn{6}{c}{**Accuracy (\%) with Additive Noises**} & \multicolumn{2}{c}{**Without**} \\ \cline{3-8}  & & **SNR = \(-\)5** & **SNR = 0** & **SNR = 5** & **SNR = 10** & **SNR = 15** & **SNR = 20** & **Additive Noise** \\ \hline \multirow{3}{*}{**Basic 2 DCNN**} & Mel & \multirow{2}{*}{46.78} & \multirow{2}{*}{67.78} & \multirow{2}{*}{81.07} & \multirow{2}{*}{87.74} & \multirow{2}{*}{89.44} & \multirow{2}{*}{92.16} & \multirow{2}{*}{93.61} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 73.56 & 87.16 & 91.98 & 93.66 & 94.65 & 95.47 & 95.62 \\ \hline \multirow{3}{*}{**ResNet-50**} & Mel & \multirow{2}{*}{48.89} & \multirow{2}{*}{69.91} & \multirow{2}{*}{83.22} & \multirow{2}{*}{89.91} & \multirow{2}{*}{91.63} & \multirow{2}{*}{94.37} & \multirow{2}{*}{96.97} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 74.14 & 87.88 & 92.87 & 94.63 & 95.63 & 96.22 & 97.85 \\ \hline \multirow{3}{*}{**VGG-16**} & Mel & \multirow{2}{*}{51.96} & \multirow{2}{*}{70.82} & \multirow{2}{*}{85.3} & \multirow{2}{*}{91.64} & \multirow{2}{*}{92.81} & \multirow{2}{*}{95.77} & \multirow{2}{*}{96.93} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 75.77 & 89.38 & 93.94 & 95.96 & 96.79 & 97.32 & 98.04 \\ \hline \multirow{3}{*}{**ECAPAT-TDNN**} & Mel & \multirow{2}{*}{53.98} & \multirow{2}{*}{72.25} & \multirow{2}{*}{86.94} & \multirow{2}{*}{92.54} & \multirow{2}{*}{93.67} & \multirow{2}{*}{96.59} & \multirow{2}{*}{96.72} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 76.42 & 89.75 & 94.25 & 96.39 & 97.15 & 97.61 & 97.89 \\ \hline \multirow{3}{*}{**TitaNet**} & Mel & \multirow{2}{*}{55.25} & \multirow{2}{*}{73.03} & \multirow{2}{*}{87.61} & \multirow{2}{*}{93.15} & \multirow{2}{*}{94.19} & \multirow{2}{*}{97.17} & \multirow{2}{*}{97.55} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 78.37 & 89.97 & 94.51 & 96.55 & 97.34 & 97.81 & 98.02 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Speaker Identification Performance of the Cochleogram vs. Mel Spectrogram with and without additive noises on VoxCeleb1. The comparison of the speaker identification and verification performance of the Cochleogram features with the existing works is presented in the Table 7. The baselines such as: CNN-256-Pair Selection [27], CNN [24], Adaptive VGG-M [28], CNN-LDE [29], ECAPA-TDNN [9], and TitaNet [10] are selected for the comparison with the experiment results of our work. The results in the Table 7, show that Cochleogram features have better performance in speaker identification and verification in the noisy condition. For instance, the identification accuracy of Cochleogram features using architectures ResNet-50, VGG-16, ECAPA-TDNN and TitaNet is 97.85%, 98.04%, 97.89% and 98.02%, respectively, which is better than the performance of the baselines CNN [24], Adaptive VGG-M [28] and CNN-LDE [29] with the accuracies 92.10%, 95.31% and 95.70%, respectively. Similarly, Cochleogram features also achieved better performance in speaker verification compared to Mel Spectrogram features.