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Deep neural network (DNN)-based speaker embeddings have resulted in new, state-of-the-art text-independent speaker recognition technology. However, very limited effort has been made to understand DNN speaker embeddings. In this study, our aim is analyzing the behavior of the speaker recognition systems based on speaker embeddings toward different front-end features, including the standard Mel frequency cepstral coefficients (MFCC), as well as power normalized cepstral coefficients (PNCC), anddoi:10.21437/interspeech.2018-1102 dblp:conf/interspeech/NandwanaMCHL18 fatcat:s4xhvarvkfcjnkwsu6gvkwuzjy