Speaker segmentation consists in partitioning a conversation between one or more
speakers into speaker turns. Usually addressed as the late combination of three sub-tasks …

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 …

This paper investigates an end-to-end neural diarization (EEND) method for an unknown
number of speakers. In contrast to the conventional cascaded approach to speaker …

Attractor-based end-to-end diarization is achieving comparable accuracy to the carefully
tuned conventional clustering-based methods on challenging datasets. However, the main …

End-to-end neural diarization (EEND) is nowadays one of the most prominent research
topics in speaker diarization. EEND presents an attractive alternative to standard cascaded …

In this paper, we present a novel framework that jointly performs three tasks: speaker
diarization, speech separation, and speaker counting. Our proposed framework integrates …

A method to perform offline and online speaker diarization for an unlimited number of
speakers is described in this paper. End-to-end neural diarization (EEND) has achieved …

This paper proposes a novel Attention-based Encoder-Decoder network for End-to-End
Neural speaker Diarization (AED-EEND). In AED-EEND system, we incorporate the target …

Deep neural network-based systems have significantly improved the performance of
speaker diarization tasks. However, end-to-end neural diarization (EEND) systems often …

We propose a streaming diarization method based on an end-to-end neural diarization
(EEND) model, which handles flexible numbers of speakers and overlapping speech. In our …