The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Research on speaker recognition is extending to address the vulnerability in the wild
conditions, among which genre mismatch is perhaps the most challenging, for instance …

While using two-dimensional convolutional neural networks (2D-CNNs) in image
processing, it is possible to manipulate domain information using channel statistics, and …

The goal of this work is to investigate the performance of popular speaker recognition
models on speech segments from movies, where often actors intentionally disguise their …

The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and
further reduce the operation and maintenance cost at wind farms. However, in reality, wind …

Automatic speaker verification (ASV) exhibits unsatisfactory performance under domain
mismatch conditions owing to intrinsic and extrinsic factors, such as variations in speaking …

Deep learning models for verification systems often fail to generalize to new users and new
environments, even though they learn highly discriminative features. To address this …

By constraining the lexical content of input speech, text-dependent speaker verification (TD-
SV) offers more reliable performance than text-independent speaker verification (TI-SV) …

Automatic speaker verification (ASV) faces domain shift caused by the mismatch of intrinsic
and extrinsic factors such as recording device and speaking style in real-world applications …

The ability of countermeasure models to generalize from seen speech synthesis methods to
unseen ones has been investigated in the ASVspoof challenge. However, a new mismatch …