## 2 Related Works  
There is large number of references in the speaker recognition area. In this study, we reviewed the literatures more related to our study. A number of research is conducted to improve the noise robustness of speaker recognition models by using different types of feature extraction techniques and machine learning models. In the study [7] noise robustness of MFCC and GFCC features are analyzed, then GFCC feature is recommended. Therefore, either GFCC feature or its fusion with other features has been used in conventional machine learning models to develop robust speaker recognition. In the study [11], a speaker identification system is developed using GFCC feature and GMM classifier with the additive noises (i.e., babble, factory and destroyer noises); then it achieved better performs than MFCC features. The research work on [12] used a combination of GFCC and MFCC features to develop speaker identification using Deep Neural Network (DNN) classifiers in noisy conditions, the result shows that fusion of both features perform superior to individual features. Robustness of GFCC feature for additive noise and white Gaussian noise is also evaluated in speaker recognition using i-vector in the study [13] and the result shows that GFCC shows superior performance. Fusion of classifiers like fusion of GMM and SVM is applied together with the GFCC feature to develop a speaker recognition. In GFCC, speech is represented in equivalent rectangular bandwidth (ERB) scale which has finer resolution than Mel Scale in low frequency. Both ERB scale and nonlinear rectification are the main sources for the robustness of GFCC in noisy conditions. Recently, deep learning models have attracted the attention of researchers of speaker recognition and other research areas. In the study [14] Convolutional Neural Network (CNN) and Long Short Memory Networks, respectively, showed superior performance than GMM and i-vector approaches in speaker recognition. In the paper [15], CNN model shows superior performance than Support Vector Machine (SVM) in based speaker recognition. Unlike classical machine learning models which use handcrafted features, CNN model extracts feature from input images automatically during training and classification. For CNN based speaker recognition input images are generated from speech at different steps of speech processing. In the study [16], raw waveform shows better speaker identification performance than MFCC features in noisy and reverberant environments. In the research work [17; 18], performance of spectrogram in speaker recognition surpasses both raw waveform and MFCC features both in noisy and reverberant environments. Deep learning-based speaker recognition research works in [19; 20; 21] use Mel Spectrogram as an input. Like MFCC, Mel Spectrogram represents speech in Mel Scale whose performance degrades with noises, environmental changes, physical and behavioral changes of speaker during training and test. Like GFCC, in Cochleogram [22] speech represented in Equal Rectangular Band (ERB) scale and uses nonlinear rectification. ERB scale is more robust to noise because of its finer resolution in lower frequency. In the study [23] [PERSON] et al. proposed the speaker identification model using Cochleogram and a simple CNN architecture, the model achieved better accuracy in the noise condition. Even though Cochleogram can be used as an input for deep learning models, only limited speech-based research is conducted using Cochleogram and deep learning models. None of the research works also analyzed noise robustness of Cochleogram features in speaker recognition on the datasets with different types of noises at different Signal to Noise ratio (SNR) level. In addition, only limited deep learning based speaker recognition models are developed using Cochleogram. In this study, we have conducted analysis of noise robustness of Cochleogram and Mel Spectrogram features with three different types of noises (Babble noise, street noise and restaurant noise) at SNR level from \(-5\) dB to \(20\) dB.