Speaker recognition is a task of identifying persons from their voices. Recently, deep
learning has dramatically revolutionized speaker recognition. However, there is lack of …

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 …

The goal of this work is to train robust speaker recognition models without speaker labels.
Recent works on unsupervised speaker representations are based on contrastive learning …

Todays interactive devices such as smart-phone assistants and smart speakers often deal
with short-duration speech segments. As a result, speaker recognition systems integrated …

Trained on 680,000 h of massive speech data, Whisper is a multitasking, multilingual
speech foundation model demonstrating superior performance in automatic speech …

Large performance degradation is often observed for speaker verification systems when
applied to a new domain dataset. Given an unlabeled target-domain dataset, unsupervised …

The pooling function plays a vital role in the segment-level deep speaker embedding
learning framework. One common method is to calculate the statistics of the temporal …

The goal of this work is to train robust speaker recognition models using self-supervised
representation learning. Recent works on self-supervised speaker representations are …

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 …

Research in speaker recognition has recently seen significant progress due to the
application of neural network models and the availability of new large-scale datasets. There …