In the recent past, deep learning methods have demonstrated remarkable success for
supervised learning tasks in multiple domains including computer vision, natural language …

A system for generating channel-compensated features of a speech signal includes a
channel noise simulator that degrades the speech signal, a feed forward convolutional …

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 …

Utterances of at least two speakers in a speech signal may be distinguished and the
associated speaker identified by use of diarization together with automatic speech …

Over the last few years, i-vectors have been the state-of-the-art technique in speaker
recognition. Recent advances in Deep Learning (DL) technology have improved the quality …

In a speaker recognition apparatus, audio features are extracted from a received recognition
speech signal, and first order Gaussian mixture model (GMM) statistics are gener ated …

The ability to recognize people uniquely and to associate personal attributes such as name
and nationality with them has been very important to the fabric of human society. Nowadays …

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 both the front-end and
backend of speaker verification systems. In this paper, we propose applying RBMs to the …

The Gaussian probabilistic linear discriminant anal-ysis (PLDA) model assumes Gaussian
distributed priors for the latent variables that represent the speaker and channel factors …