Automatic Speaker Recognition using Transfer Learning Approach of Deep Learning Models

Speaker Recognition has been one of the most interesting yet challenging problems in the field of machine learning and artificial intelligence. It is used in the areas of human voice authentication for security purpose and identifying a person from a group of speakers. It has been a arduous task to teach a machine the differences in human voices, especially when people belong to different background like gender, language, accent, etc. This paper has used the deep learning approach to build and train two models, Artificial Neural Network (“ANN”) and Convolutional Neural Network (“CNN”), and compared their results. In former, the neural networks are fed on diverse extracted features from audio collection, whereas the latter is trained on spectrograms. Finally, transfer learning approach is deployed on both to get a viable output using limited data.