Publication Details
Joint Training of Speaker Embedding Extractor, Speech and Overlap Detection for Diarization
Landini Federico Nicolás, Ph.D. (RG SPEECH)
Klement Dominik, Ing. (DCGM)
Diez Sánchez Mireia, M.Sc., Ph.D. (DCGM)
Silnova Anna, M.Sc., Ph.D. (DCGM)
Delcroix Marc
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
speaker diarization, speaker embedding, voice activity detection, overlapped speech detection
In spite of the popularity of end-to-end diarization
systems nowadays, modular systems comprised of voice activity
detection (VAD), speaker embedding extraction plus clustering,
and overlapped speech detection (OSD) plus handling still attain
competitive performance in many conditions. However, one of
the main drawbacks of modular systems is the need to run
(and train) different modules independently. In this work, we
propose an approach to jointly train a model to produce speaker
embeddings, VAD and OSD simultaneously and reach competitive
performance at a fraction of the inference time of a modular
approach. Furthermore, the joint inference leads to a simplified
overall pipeline which brings us one step closer to a unified
clustering-based method that can be trained end-to-end towards
a diarization-specific objective.
@inproceedings{BUT198669,
author="Petr {Pálka} and Federico Nicolás {Landini} and Dominik {Klement} and Mireia {Diez Sánchez} and Anna {Silnova} and Marc {Delcroix} and Lukáš {Burget}",
title="Joint Training of Speaker Embedding Extractor, Speech and Overlap Detection for Diarization",
booktitle="Proceedings of Eusipco 2025",
year="2025",
pages="1--5",
publisher="IEEE Signal Processing Society",
address="Palermo",
url="https://www.fit.vut.cz/research/publication/13567/"
}