Speaker individuality information is among the most critical elements within speech signals.
By thoroughly and accurately modeling this information, it can be utilized in various …

Despite the significant advantages of communication systems between electronic control
units, the controller area network (CAN) protocol is vulnerable to attacks owing to its weak …

The automatic speaker verification task has achieved great success using deep learning
approaches with a large-scale, manually annotated dataset. However, collecting a …

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

Aspect-based sentiment analysis (ABSA) aims to predict sentiment orientations towards
textual aspects by extracting insights from user comments. While pretrained large language …

Embeddings extracted by deep neural networks have become the state-of-the-art utterance
representation in speaker verification (SV). Despite the various network architectures that …

Deep speaker embedding learning based on neural networks has become the predominant
approach in speaker verification (SV) currently. In prior studies, researchers have …

Transformer has achieved extraordinary performance in Natural Language Processing and
Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant …

Recently, there has been a significant surge of interest in Self-Attention Networks (SANs)
based on the Transformer architecture. This can be attributed to their notable ability for …

This study introduces the first single branch network designed to tackle a spectrum of
biometric matching scenarios, including unimodal, multimodal, cross-modal, and missing …