Speaker identification and speaker verification are the main tasks in the field of speaker recognition. The former involves inferring the speaker of an utterance from a set of possible identities, whereas the latter aims at assessing whether a claimed identity corresponds to the speaker of a given speech segment. Thanks to the advances in the field of Deep Learning, Deep Neural Networks (DNN) have recently become the state-of-the-art technique for utterance representation in the speaker recognition field. The DNN approach consists in training a neural network to extract speaker embeddings, i.e. fixed dimensional utterance representations that contain speaker-discriminant information. DNN embeddings significantly outperform previous state-of-the-art methods such as i-vectors in terms of verification accuracy.