Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …

Current speaker verification techniques rely on a neural network to extract speaker
representations. The successful x-vector architecture is a Time Delay Neural Network …

The construction of speaker-specific acoustic models for automatic speaker recognition is
almost exclusively based on deep neural network-based speaker embeddings. This work …

Speaker modeling is essential for many related tasks, such as speaker recognition and
speaker diarization. The dominant modeling approach is fixed-dimensional vector …

This report describes our submission to the VoxCeleb Speaker Recognition Challenge
(VoxSRC) at Interspeech 2020. We perform a careful analysis of speaker recognition models …

We held the second installment of the VoxCeleb Speaker Recognition Challenge in
conjunction with Interspeech 2020. The goal of this challenge was to assess how well …

This paper describes the IDLab submission for the text-independent task of the Short-
duration Speaker Verification Challenge 2021 (SdSVC-21). This speaker verification …

The third instalment of the VoxCeleb Speaker Recognition Challenge was held in
conjunction with Interspeech 2021. The aim of this challenge was to assess how well current …

This paper summarises the findings from the VoxCeleb Speaker Recognition Challenge
2022 (VoxSRC-22), which was held in conjunction with INTERSPEECH 2022. The goal of …

Speaker modeling plays a crucial role in various tasks, and fixed-dimensional vector
representations, known as speaker embeddings, are the predominant modeling approach …