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 …

In this paper, we present a novel speaker diarization system for streaming on-device
applications. In this system, we use a transformer transducer to detect the speaker turns …

The most common approach to speaker diarization is clustering of speaker embeddings.
However, the clustering-based approach has a number of problems; ie,(i) it is not optimized …

Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which
models speakers using multiple instances of a parameter-sharing recurrent neural network …

The performance of most speaker diarization systems with x-vector embeddings is both
vulnerable to noisy environments and lacks domain robustness. Earlier work on speaker …

In this study, we address the problems encountered by incremental face clustering. Without
the benefit of having observed the entire data distribution, incremental face clustering is …

We study the problem of k-way clustering in signed graphs. Considerable attention in recent
years has been devoted to analyzing and modeling signed graphs, where the affinity …