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 …

For speaker recognition, it is difficult to extract an accurate speaker representation from
speech because of its mixture of speaker traits and content. This paper proposes a …

The time delay neural network (TDNN) represents one of the state-of-the-art of neural
solutions to text-independent speaker verification. However, they require a large number of …

The residual neural networks (ResNet) demonstrate the impressive performance in
automatic speaker verification (ASV). They treat the time and frequency dimensions equally …

Traditional acoustic-phonetic approach makes use of both spectral and phonetic information
when comparing the voice of speakers. While phonetic units are not equally informative, the …

This paper proposes a multi-task learning network with phoneme-aware and channel-wise
attentive learning strategies for text-dependent Speaker Verification (SV). In the proposed …

This paper explores three novel approaches to improve the performance of speaker
verification (SV) systems based on deep neural networks (DNN) using Multi-head Self …

In this paper, we study a novel technique that exploits the interaction between speaker traits
and linguistic content to improve both speaker verification and utterance verification …

In this paper, adaptive mechanisms are applied in deep neural network (DNN) training for x-
vector-based text-independent speaker verification. First, adaptive convolutional neural …

Metric learning approaches have widely expanded to the training of Speaker Verification
(SV) systems based on Deep Neural Networks (DNNs), by using a loss function more …