## 6 Conclusions  
In this study, we have analyzed noise robustness of Cochleogram and Mel Spectrogram features in speaker identification and verification. The deep learning model networks such as: basic 2 DCNN, ResNet-50, VGG-16, ECAPA-TDNN and TitaNet architectures are used for conducting our experiment. The input for deep learning architectures is generated from the VoxCeleb1 audio dataset and noise added VoxCeleb1 datasets. The noise added VoxCeleb1 dataset is obtained by adding three different types of noises (i.e., Babble noise, Street noise and Restaurant noise) at the SNR level from \(-5\) dB to \(20\) dB. The Cochleogram and Mel Spectrogram features which is used for training and testing the models are generated from the VoxCeleb1 and noise added VoxCeleb1 datasets. For each of the deep learning architectures, separate models are trained for speaker identification and verification evaluation. The performance is evaluated using accuracy metrics during identification and using EER during verification. The analysis results of both Cochleogram and Mel Spectrogram features in speaker identification and verification shows that Cochleogram features performs superior to Mel Spectrogram features at high noise ratio (i.e., at SNR = \(-5\) dB, \(0\) dB and \(5\) dB). The performance of Cochleogram features is also better than Mel Spectrogram at medium noise ratio (i.e., at SNR = \(10\) dB, \(15\) dB and \(20\) dB) during both speaker identification and verification. At clean dataset or dataset without additive noise both Mel Spectrogram and Cochleogram features achieved comparative performance in speaker identification and verification. The comparison of performance of Cochleogram features in speaker identification and verification with existing works also shows that Cochleogram has better performance than Mel Spectrogram features at noisy condition using similar deep learning model architectures. In conclusion, Cochleogram features has better performance than Mel Spectrogram features in deep learning-based speaker recognition at high and medium noise ratio in the dataset. In the future, this study can be extended by fusion of Cochleogram features with different types of features in order to improve the speaker recognition performance at real world conditions by incorporating the advantages of different types of features. The manuscript was written, and the experiment was conducted by [PERSON] The manuscript is supervised, reviewed and edited by [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l l l c c} \hline \hline \multicolumn{1}{c}{**Model Types**} & \multicolumn{1}{c}{**Feature Type**} & \multicolumn{1}{c}{**Dataset**} & \
\begin{tabular}{c} **Identification** \\ **Accuracy (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Verification** \\ **EER (\%)** \\ \end{tabular} \\ \hline
**ECAPA-TDNN**[9] & Mel Spectrogram & VoxCeleb1 & - & 0.87 \\ \hline
**TitaNet**[10] & Mel Spectrogram & VoxCeleb1 & - & 0.68 \\ \hline
**2 DCNN (Ours)** & Cochleogram & VoxCeleb1 & 95.62 & 5.33 \\ \hline
**ResNet-50 (Ours)** & Cochleogram & VoxCeleb1 & 97.85 & 4.06 \\ \hline
**VGG-16 (Ours)** & Cochleogram & VoxCeleb1 & 98.04 & 3.81 \\ \hline
**ECAPA-TDNN(Ours)** & Cochleogram & VoxCeleb1 & 97.89 & 0.61 \\ \hline
**TitaNet(Ours)** & Cochleogram & VoxCeleb1 & 98.02 & 0.54 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: _Cont._  
**Funding:** This research got support under the annual funding track [GRANT 2432] from Deanship of Scientific Research, King Faisal University, Saudi Arabia. This research got support under the Post Graduate Studies program, Adama Science and Technology University, Ethiopia. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.