The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

Deep speaker embedding extraction models have recently served as the cornerstone for
modular speaker diarization systems. However, in current modular systems, the extracted …

Speaker embedding extractors (EEs), which map input audio to a speaker discriminant
latent space, are of paramount importance in speaker diarisation. However, there are …

Deep learning models such as CNNs and Transformers have achieved impressive
performance for end-to-end audio tagging. Recent works have shown that despite stacking …

In recent years, there have been studies to further improve the end-to-end neural speaker
diarization (EEND) systems. This letter proposes the EEND-DEMUX model, a novel …

The task of speaker change detection (SCD), which detects points where speakers change
in an input, is essential for several applications. Several studies solved the SCD task using …

This report describes the submission system of the GIST-AiTeR team at the 2022 VoxCeleb
Speaker Recognition Challenge (VoxSRC) Track 4. Our system mainly includes speech …

We propose a modified teacher-student training for the extraction of frame-wise speaker
embeddings that allows for an effective diarization of meeting scenarios containing partially …

Speaker embedding extractors significantly influence the performance of clustering-based
speaker diarisation systems. Conventionally, only one embedding is extracted from each …

Our focus lies in developing an online speaker diarisation framework which demonstrates
robust performance across diverse domains. In online speaker diarisation, outputs …