The field of speech processing has undergone a transformative shift with the advent of deep
learning. The use of multiple processing layers has enabled the creation of models capable …

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 …

pyannote. audio is an open-source toolkit written in Python for speaker diarization. Version
2.1 introduces a major overhaul of pyannote. audio default speaker diarization pipeline …

Self-supervised learning (SSL) achieves great success in speech recognition, while limited
exploration has been attempted for other speech processing tasks. As speech signal …

We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite. It
offers 3,670 hours of daily-life activity video spanning hundreds of scenarios (household …

SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …

Since its introduction in 2019, the whole end-to-end neural diarization (EEND) line of work
has been addressing speaker diarization as a frame-wise multi-label classification problem …

As power systems evolve by integrating renewable energy sources, distributed generation,
and electric vehicles, the complexity of managing these systems increases. With the …

In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker
representations. We employ 1D depth-wise separable convolutions with Squeeze-and …

Learning robust speaker embeddings is a crucial step in speaker diarization. Deep neural
networks can accurately capture speaker discriminative characteristics and popular deep …