Over the last years, i-vectors have been the state-of-the-art approach in speaker recognition. Recent improvements in deep learning have increased the discriminative quality of i-vectors. However, deep learning architectures require a large amount of labeled background data which is difficult in practice. The aim of this paper is to propose an alternative scheme in order to reduce the need of labeled data. We propose the use of autoencoder pre-training in a speaker verification task. First, we train an autoencoder in an unsupervised way, using a large amount of unlabeled background data. Then, we train a Deep Neural Network (DNN) initialized with the parameters of the pre-trained autoencoder. The DNN training is carried out in a supervised way using relatively small labeled background data. In the testing phase, we extract speaker embeddings as the output of an intermediate layer of the DNN. The training and evaluation were performed on VoxCeleb-2 and VoxCeleb1 databases, respectively. The experimental results have shown that by initializing DNN with the parameters of the pre-trained autoencoder, we have achieved a relative improvement of 21%, in terms of Equal Error Rate (EER), over the baseline i-vector/PLDA system.

Peer Reviewed