## 1 Introduction  
Speaker recognition is classification/identification of an individual person from others based on characteristics of voice. It is a biometrics technology which identification, verification, and classification of individual speakers based on the voice characteristics [1]. It has the capability of tracking, detecting, and segmenting specific speaker from the group of speakers. It is very important in the various application areas including forensics, financial transaction, business management, access control, surveillance and law enforcement [2, 3]. Speaker recognition can be classified into two categories: Speaker identification and Speaker verification [2].  
Speaker identification is the process of determining who is speaking from the set of known speakers by the model [4]. It performs 1: N classification, where a test sample compared with all the speaker classes in the trained model. Speaker identification also known as closed set identification because it is assumed the test speaker voice come from a known set of speakers. Speaker verification is the process of determining whether the speaker identity is who the person claims to be. In speaker verification the speaker is either accepted or rejected. It is also referred as open set classification. Speaker verification performs one to one classification, in which the test voice compared with claimed speaker class in the model.  
Speaker recognition systems has two basic operations: feature extraction and speaker modeling/training [2]. Feature extraction converts raw waveform of the speech signal into low dimensional feature vectors which is important to train the model. Feature extraction plays important role in the performance of the speaker recognition. Handcrafted features such as Mel Frequency Cepstral Coefficient (MFCC), Gammatone Frequency Cepstral Coefficient (GFCC) and Linear Predictive Cepstral Coefficient (LPCC) has been used in conventional machine learning models [5]. Deep learning models automatically extracts feature from the input data during training [6]. The performance of speaker recognition systems is very good for the training and test speech without mismatch/noise [7]. In reality, mismatch or noise may occur between training and test speech which degrades performance of speaker recognition systems. Practical application of speaker recognition systems needs robustness of the system for each of the real-world conditions.  
Convolutional Neural Network (CNN) [8], which is well known deep learning model achieved very good performance in variety of problems including image classification, speech recognition, natural language processing and other computer vision research. Because of superior performance of deep learning models related with conventional machine learning, recent Speaker recognition research are conducted using deep learning models, especially to CNN architecture. Most of the CNN architectures-based speaker recognition models are developed using Mel Spectrogram of the speech signal. However, the speaker recognition performance of the Mel Spectrogram gets degraded with the noise and other mismatch. In this study, we have analyzed noise robustness of Cobleogram and Mel Spectrogram in Speaker recognition using the CNN architectures: basic 2 DCNN, ResNet-50, VGG-16, ECAPA-TDNN [9] and TitaNet [10] architectures. The deep learning-based speaker recognition models with Cochleogram feature is recommended for the noisy environment.  
The rest of the paper is organized as follows. Related works conducted in the area of our study is discussed in Section 2. Section 3 explains about Cochleogram and Mel Spectrogram generation from speech data. Section 4 explains the detail of CNN architectures used in our study. In Section 5 we explained in detail how the experiment is conducted, and we represented the results in different types of ways. We concluded this study in Section 6.