In this paper, we present Multi-scale Feature Aggregation Conformer (MFA-Conformer), an
easy-to-implement, simple but effective backbone for automatic speaker verification based …

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 …

Time delay neural network (TDNN) has been proven to be efficient for speaker verification.
One of its successful variants, ECAPA-TDNN, achieved state-of-the-art performance at the …

In self-supervised learning for speaker recognition, pseudo labels are useful as the
supervision signals. It is a known fact that a speaker recognition model doesn't always …

In active speaker detection (ASD), we would like to detect whether an on-screen person is
speaking based on audio-visual cues. Previous studies have primarily focused on modeling …

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

Humans possess the remarkable ability to selectively attend to a single speaker amidst
competing voices and background noise, known as selective auditory attention. Recent …

The emergence of large-margin softmax cross-entropy losses in training deep speaker
embedding neural networks has triggered a gradual shift from parametric back-ends to a …

The majority of recent state-of-the-art speaker verification architectures adopt multi-scale
processing and frequency-channel attention mechanisms. Convolutional layers of these …

Neural network-based speaker recognition has achieved significant improvement in recent
years. A robust speaker representation learns meaningful knowledge from both hard and …