Deep learning (DL) is becoming an increasingly interesting and powerful machine learning
method with successful applications in many domains, such as natural language …

The lack of labeled background data makes a big performance gap between cosine and
Probabilistic Linear Discriminant Analysis (PLDA) scoring baseline techniques for i-vectors …

In the domain of speech recognition many methods have been proposed over time like
Gaussian mixture models (GMM), GMM with universal background model (GMM-UBM …

The use of Restricted Boltzmann Machines (RBM) is proposed in this paper as a non-linear
transformation of GMM supervectors for speaker recognition. It will be shown that the RBM …

In this paper, we propose to discriminatively model target and impostor spectral features
using Deep Belief Networks (DBNs) for speaker recognition. In the feature level, the number …

Restricted Boltzmann Machines (RBMs) have shown success in different stages of speaker
recognition systems. In this paper, we propose a novel framework to produce a vector-based …

The process of manually labeling data is not affordable. Moreover, the lack of labeled data
has led to a big performance gap between scoring baseline techniques in speaker …

It is supposed in Speaker Recognition (SR) that everyone has a unique voice which could
be used as an identity rather than or in addition to other identities such as fingerprint, face, or …

This thesis is about a research conducted in the area of Speaker Recognition. The
application is concerned to the automatic detection and tracking of target speakers in …

Restricted Boltzmann Machines (RBMs) have shown success in speaker recognition. In this
paper, RBMs are investigated in a framework comprising a universal model training and …