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 …

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 for real-life scenarios is an extremely challenging problem. Widely used
clustering-based diarization approaches perform rather poorly in such conditions, mainly …

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 study, we propose a new spectral clustering framework that can auto-tune the
parameters of the clustering algorithm in the context of speaker diarization. The proposed …

We performed an experimental review of current diarization systems for the conversational
telephone speech (CTS) domain. In detail, we considered a total of eight different algorithms …

In speaker diarization, standard approaches typically perform speaker clustering on some
initial segmentation before refining the segment boundaries in a re-segmentation step to …

In recent years, the spectral clustering method has gained attentions because of its superior
performance. To the best of our knowledge, the existing spectral clustering algorithms …

In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data
clustering with a maximum number of clusters as a supervised sequence-to-sequence …

Target-speaker voice activity detection (TS-VAD) has recently shown promising results for
speaker diarization on highly overlapped speech. However, the original model requires a …