In this paper, we use data augmentation to improve performance of deep neural network
(DNN) embeddings for speaker recognition. The DNN, which is trained to discriminate …

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 …

Internet-connected devices, such as smartphones, smartwatches, and laptops, have become
ubiquitous in modern life, reaching ever deeper into our private spheres. Among the sensors …

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 …

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 …

We investigate deep neural network performance in the textindependent speaker
recognition task. We demonstrate that using angular softmax activation at the last …

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 …

Deep neural network based speaker embeddings become increasingly popular in the text-
independent speaker recognition task. In contrast to a generatively trained i-vector extractor …

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of leveraging
unlabelled utterances, due to the classification loss over training speakers. In this paper, we …