With the advent of automated speaker verification (ASV) systems comes an equal and
opposite development: malicious actors may seek to use voice spoofing attacks to fool those …

Audio deepfake detection is an emerging active topic. A growing number of literatures have
aimed to study deepfake detection algorithms and achieved effective performance, the …

Advancements in AI-synthesized human voices have created a growing threat of
impersonation and disinformation, making it crucial to develop methods to detect synthetic …

Abstract Auditory Attention Detection (AAD) aims to detect the target speaker from brain
signals in a multi-speaker environment. Although EEG-based AAD methods have shown …

The rhythm of bonafide speech is often difficult to replicate, which causes that the
fundamental frequency (F0) of synthetic speech is significantly different from that of real …

The rise of singing voice synthesis presents critical challenges to artists and industry
stakeholders over unauthorized voice usage. Unlike synthesized speech, synthesized …

In recent years, knowledge graph completion (KGC) models based on pre-trained language
model (PLM) have shown promising results. However, the large number of parameters and …

Existing deep learning models for spoofing speech detection often struggle to effectively
generalize to unseen spoofing attacks that were not present during the training stage …

The detection of spoofing speech generated by unseen algorithms remains an unresolved
challenge. One reason for the lack of generalization ability is that traditional detecting …

Most research in synthetic speech detection (SSD) focuses on improving performance on
standard noise-free datasets. However, in actual situations, noise interference is usually …