Recently, deep neural networks that map utterances to fixed-dimensional embeddings have
emerged as the state-of-the-art in speaker recognition. Our prior work introduced x-vectors …

The" VOiCES from a Distance Challenge 2019" is designed to foster research in the area of
speaker recognition and automatic speech recognition (ASR) with the special focus on …

We present a magnitude estimation network that is combined with a modified ResNet x-
vector system to generate embeddings whose inner product is able to produce calibrated …

Most current works on speaker identification are based on i-vector methods; however, there
is a marked shift from the traditional i-vector to deep learning methods, especially in the form …

State-of-the-art text-independent speaker recognition systems for long recordings (a few
minutes) are based on deep neural network (DNN) speaker embeddings. Current …

This paper investigates a novel data augmentation approach to train deep neural networks
(DNNs) used for speaker embedding, ie to extract representation that allows easy …

The VoxSRC speaker recognition challenge comprises data obtained from YouTube videos
of celebrity interviews in a wide range of recording environments. The challenge provides …

Our goal is to separate out speaking style from speaker identity in utterance-level
representations of speech such as i-vectors and x-vectors. We first show that both i-vectors …

Recently, speaker embeddings extracted with deep neural networks became the state-of-the-
art method for speaker verification. In this paper we aim to facilitate its implementation on a …

In this paper, we address the problem of speaker verification in conditions unseen or
unknown during development. A standard method for speaker verification consists of …