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20192021
most citedA Comparison of Metric Learning Loss Functions for End-To-End Speaker Verification

8 citations · 8 across the 2 of their papers we have counts for

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eess.AS2021

Overlap-aware low-latency online speaker diarization based on end-to-end local segmentation

Juan M. Coria, Hervé Bredin, Sahar Ghannay +1

We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms. Every single step…

eess.AS2019

pyannote.audio: neural building blocks for speaker diarization

Hervé Bredin, Ruiqing Yin, Juan Manuel Coria +7

We introduce pyannote.audio, an open-source toolkit written in Python for speaker diarization. Based on PyTorch machine learning framework, it provides a set of trainable end-to-en…