## 5 Experiment and Result
### Results
For example, ECAPA-TDNN and TitaNet architectures using Cochleogram features achieved an EER of 0.61% and 0.54% which is error rate of the Mel Spectrogram features which is 0.87% and 0.68%. \begin{table}
\begin{tabular}{c l l l l l l l l} \hline \hline \multirow{2}{*}{**Model Type**} & \multirow{2}{*}{**Feature Type**} & \multicolumn{5}{c}{**EER (\%)**} & \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}{*}{22.97} & \multirow{2}{*}{18.18} & \multirow{2}{*}{13.37} & \multirow{2}{*}{10.45} & \multirow{2}{*}{9.12} & \multirow{2}{*}{8.46} & \multirow{2}{*}{8.11} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 17.83 & 14.59 & 10.82 & 8.72 & 7.19 & 6.54 & 6.47 \\ \hline \multirow{3}{*}{**ResNet-50**} & Mel & \multirow{2}{*}{19.12} & \multirow{2}{*}{16.05} & \multirow{2}{*}{12.23} & \multirow{2}{*}{10.04} & \multirow{2}{*}{8.70} & \multirow{2}{*}{6.15} & \multirow{2}{*}{5.64} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 16.06 & 13.23 & 9.41 & 8.21 & 7.92 & 5.41 & 4.28 \\ \hline \multirow{3}{*}{**VGG-16**} & Mel & \multirow{2}{*}{18.83} & \multirow{2}{*}{15.71} & \multirow{2}{*}{11.92} & \multirow{2}{*}{9.74} & \multirow{2}{*}{8.37} & \multirow{2}{*}{5.86} & \multirow{2}{*}{5.28} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 15.42 & 12.86 & 9.10 & 7.95 & 6.61 & 4.55 & 4.16 \\ \hline \multirow{3}{*}{**ECAPA-TDNN**} & Mel & \multirow{2}{*}{13.83} & \multirow{2}{*}{10.72} & \multirow{2}{*}{6.93} & \multirow{2}{*}{3.75} & \multirow{2}{*}{2.06} & \multirow{2}{*}{1.16} & \multirow{2}{*}{0.91} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 11.15 & 9.68 & 5.84 & 2.61 & 1.30 & 0.64 & 0.61 \\ \hline \multirow{3}{*}{**TitaNet**} & Mel & \multirow{2}{*}{11.36} & \multirow{2}{*}{8.72} & \multirow{2}{*}{4.92} & \multirow{2}{*}{2.70} & \multirow{2}{*}{1.34} & \multirow{2}{*}{0.83} & \multirow{2}{*}{0.75} \\  & Spectrogram & & & & & & & \\ \cline{2-8}  & Cochleogram & 10.82 & 7.64 & 3.83 & 2.53 & 1.24 & 0.62 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Speaker Identification Performance of the Cochleogram vs. Mel Spectrogram with and without additive noises on VoxCeleb1. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multicolumn{1}{c}{**Model Types**} & \multicolumn{1}{c}{**Feature Type**} & \multicolumn{1}{c}{**Dataset**} & \multicolumn{1}{c}{**Identification**} & \multicolumn{1}{c}{**Verification**} \\  & \multicolumn{1}{c}{**Accuracy (\%)**} & \multicolumn{1}{c}{**EER (\%)**} \\ \hline
**CNN-256 + Pair**[27] & Mel Spectrogram & VoxCeleb1 & - & 10.5 & \\ \hline
**CNN**[24] & Mel Spectrogram & VoxCeleb1 & 92.10 & 7.80 & \\ \hline
**Adaptive VGG-M**[28] & Mel Spectrogram & VoxCeleb1 & 95.31 & 5.68 & \\ \hline
**CNN-LDE**[29] & Mel Spectrogram & VoxCeleb1 & 95.70 & 4.56 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of Speaker Identification and Verification Performance of Cochleogram with existing works. Generally, the experiment results of this study and the comparison of results of this study with the existing works show that Cochleogram features have superior performance than Mel Spectrogram feature in deep learning-based speaker identification and verification on the high noise ratio in the dataset. It also has better and comparative performance with the Mel Spectrogram features on the medium noise ratio and low noise ratio in the dataset, respectively.