Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

This paper describes the Speech Technology Center (STC) antispoofing systems submitted
to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended version of the previous …

Most state-of-the-art Deep Learning (DL) approaches for speaker recognition work on a
short utterance level. Given the speech signal, these algorithms extract a sequence of …

This paper aims to improve the widely used deep speaker embedding x-vector model. We
propose the following improvements:(1) a hybrid neural network structure using both time …

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 …

This paper is a description of the Speech Technology Center (STC) systems for the CHiME-6
challenge aimed at multimicrophone multi-speaker speech recognition and diarization in a …

Speaker recognition systems based on deep speaker embeddings have achieved
significant performance in controlled conditions according to the results obtained for early …

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 …

With the development of smart devices, such as the Amazon Echo and Apple's HomePod,
speech data have become a new dimension of big data. However, privacy and security …

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