Humans can identify a speaker by listening to their voice, over the telephone, or on any
digital devices. Acquiring this congenital human competency, authentication technologies …

DIHARD III was the third in a series of speaker diarization challenges intended to improve
the robustness of diarization systems to variability in recording equipment, noise conditions …

The goal of this paper is speaker diarisation of videos collected'in the wild'. We make three
key contributions. First, we propose an automatic audio-visual diarisation method for …

This paper introduces the second DIHARD challenge, the second in a series of speaker
diarization challenges intended to improve the robustness of diarization systems to variation …

Overlapping speech has been identified as one of the main sources of errors in diarization of
meeting room conversations. Therefore, overlap detection has become an important step …

Overlapped speech is referred to a monophonic audio signal in which at least two speakers
are present at the same time. In this study, the focus is on distinguishing overlapped from …

Detecting segments of overlapping speech (when two or more speakers are active at the
same time) is a challenging problem. Previously, mostly HMM-based systems have been …

This paper presents a new method for Long Short-Term Memory Recurrent Neural Network
(LSTM) based speech overlap detection. To this end, speech overlap data is created …

Speaker diarization is usually referred to as the task that determines``who spoke when''in a
recording. Until a few years ago, all competitive approaches were modular. Systems based …

This paper demonstrates how the harmonic structure of voiced speech can be exploited to
segment multiple overlapping speakers in a speaker diarization task. We explore how a …