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 …

This paper investigates replacing i-vectors for text-independent speaker verification with
embeddings extracted from a feedforward deep neural network. Long-term speaker …

In this study, we investigate an end-to-end text-independent speaker verification system. The
architecture consists of a deep neural network that takes a variable length speech segment …

Recently, deep neural networks (DNN) have been incorporated into i-vector-based speaker
recognition systems, where they have significantly improved state-of-the-art performance. In …

From the available biometric technologies, automatic speaker recognition is one of the most
convenient and accessible ones due to abundance of mobile devices equipped with a …

Speaker recognition has emerged as an important field over the past several decades and
has evolved over time. This work is attempted to investigate speaker verification (SV) …

Conventional speaker recognition systems perform poorly under noisy conditions. Inspired
by auditory perception, computational auditory scene analysis (CASA) typically segregates …

Robustness of speaker recognition systems is crucial for real-world applications, which
typically contain both additive noise and room reverberation. However, the combined effects …

In this paper, we present a comprehensive study on supervised domain adaptation of PLDA
based i-vector speaker recognition systems. After describing the system parameters subject …

In this paper, we present a framework for unsupervised domain adaptation of PLDA based i-
vector speaker recognition systems. Given an existing out-of-domain PLDA system, we use it …