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 …

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 …

The recently proposed VBx diarization method uses a Bayesian hidden Markov model to
find speaker clusters in a sequence of x-vectors. In this work we perform an extensive …

For many years, i-vector based audio embedding techniques were the dominant approach
for speaker verification and speaker diarization applications. However, mirroring the rise of …

Speaker diarization has been mainly developed based on the clustering of speaker
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …

In this paper, we propose a novel end-to-end neural-network-based speaker diarization
method. Unlike most existing methods, our proposed method does not have separate …

In this paper, we propose a fully supervised speaker diarization approach, named
unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker …

End-to-end speaker diarization for an unknown number of speakers is addressed in this
paper. Recently proposed end-to-end speaker diarization outperformed conventional …

Speaker diarization is an important front-end for many speech technologies in the presence
of multiple speakers, but current methods that employ i-vector clustering for short segments …

In this work, we explore the dependencies between speaker recognition and emotion
recognition. We first show that knowledge learned for speaker recognition can be reused for …