This paper summarizes the applied deep learning practices in the field of speaker
recognition, both verification and identification. Speaker recognition has been a widely used …

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 …

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 …

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 …

In this paper, we introduce a novel language identification system based on conformer
layers. We propose an attentive temporal pooling mechanism to allow the model to carry …

Learning fixed-dimensional speaker representation using deep neural networks is a key
step in speaker verification. In this work, we propose an auxiliary memory storable network …

Speaker verification (SV) has suffered from session variability in complex acoustic
scenarios, and learning session independent speaker representations remains a …

Speaker recognition is the task of identifying speakers based on their speech signal.
Typically, this involves comparing speech from a known speaker, with recordings from …

Neural network based attention modeling has found utility in areas such as visual analysis,
speech recognition and more recently speaker recognition. Attention represents a gating (or …