Speech is a natural way of transforming information between the speaker and the listener. Speaker identification is the process of identifying who is speaking based on his /her unique voiceprint features. Studies on speaker identification systems were done for different languages using the traditional Mel Frequency Cepstrum Coefficient, Gaussian Mixture Models, i-vector methods, and fusion techniques. However, such models are limited by their dependency on hand-crafted feature engineering, processing time, susceptibility to noise, and performance deficiency for short utterances. Related to this, due to all-natural languages having their particular characteristics, it is impossible to use an identical speaker identification model for different languages. In this thesis, an end-to-end Convolutional Neural Network and a combined convolutional neural network with a support vector machine approach had been used for a text-independent Awngi language speaker identification. For our identification model, speech signals were collected from thirty individual speakers including both genders. For our dataset, a total of 3750 speakers speech samples were collected, and each speech has 4 seconds duration. To build our model, we have used 3000 speech samples for training, 750 speech samples for testing, and twenty percent of the training set samples for the validation set. After being collected and pre-processed, each speech is transformed into a spectrogram image by using digital signal processing techniques. Then, the resized spectrogram images are used as input to the proposed model to learn and extract speaker-specific spectral features. Our model achieved 94.4 % and 98.8 % accuracy for an end-to-end Convolutional Neural Network and a convolutional neural network with a support vector machine approach respectively. To see the effect of gender on the speaker identification for the Awngi language, our model was tested with distinct 15 male and 15 female speakers. Then, we have found an accuracy of 96.53 % and 98.4 % for males and females respectively. Finally, to evaluate the performance of our proposed model, we compared our model with a pre-trained AlexNet model using our datasets. Then, we have found 80 % accuracy for a pre-trained end-to-end AlexNet model. So, using Convolutional Neural Network as a feature extraction and support vector machine as a classification approach enhances both the accuracy and training time of an end-to-end Convolutional Neural Network and a pre-trained AlexNet model.