Speaker verification (SV) aims to detect an individual's identity from his/her voice. SV has
been successfully applied in various areas such as access control, remote service …

Speaker recognition has seen impressive advances with the advent of deep neural networks
(DNNs). However, state-of-the-art speaker recognition systems still rely on human …

Abstract Time Delay Neural Network (TDNN)-based speaker embeddings extraction have
become the dominant approach for text-independent speaker verification. Several single …

Domain shift is one of the most challenging problems in speaker verification. Although
numerous methods have been proposed to address domain shift, most approaches optimize …

How to effectively convert a sequence of variable-length acoustic features to a fixed-
dimension representation has always been a research focus in speaker recognition. In state …

Speaker recognition under environments with a low signal-to-noise ratio (SNR) and high
reverberation level has always been challenging. Data augmentation can be applied to …

Recent studies have demonstrated the effectiveness of speaker disentanglement in
mitigating the interference caused by speaker features in speech-based depression …

Most pooling methods in state-of-the-art speaker embedding networks are implemented in
the temporal domain. However, due to the high non-stationarity in the feature maps …

Mutual information (MI) is useful in unsupervised and selfsupervised learning. Maximizing
the MI between the low-level features and the learned embeddings can preserve meaningful …

State-of-the-art speaker verification systems take frame-level acoustics features as input and
produce fixed-dimensional embeddings as utterance-level representations. Thus, how to …