Result Details
Challenging margin-based speaker embedding extractors by using the variational information bottleneck
Silnova Anna, M.Sc., Ph.D., DCGM (FIT)
Rohdin Johan Andréas, M.Sc., Ph.D., FIT (FIT), DCGM (FIT)
Plchot Oldřich, Ing., Ph.D., DCGM (FIT)
Burget Lukáš, doc. Ing., Ph.D., DCGM (FIT)
Speaker embedding extractors are typically trained using a
classification loss over the training speakers. During the last
few years, the standard softmax/cross-entropy loss has been
replaced by the margin-based losses, yielding significant im-
provements in speaker recognition accuracy. Motivated by
the fact that the margin merely reduces the logit of the target
speaker during training, we consider a probabilistic framework
that has a similar effect. The variational information bottle-
neck provides a principled mechanism for making deterministic
nodes stochastic, resulting in an implicit reduction of the pos-
terior of the target speaker. We experiment with a wide range
of speaker recognition benchmarks and scoring methods and re-
port competitive results to those obtained with the state-of-the-
art Additive Angular Margin loss.
speaker recognition, variational information bottleneck
@inproceedings{BUT193738,
author="Themos {Stafylakis} and Anna {Silnova} and Johan Andréas {Rohdin} and Oldřich {Plchot} and Lukáš {Burget}",
title="Challenging margin-based speaker embedding extractors by using the variational information bottleneck",
booktitle="Proceedings of Interspeech 2024",
year="2024",
journal="Proceedings of Interspeech",
volume="2024",
number="9",
pages="3220--3224",
publisher="International Speech Communication Association",
address="Kos",
doi="10.21437/Interspeech.2024-2058",
issn="1990-9772",
url="https://www.isca-archive.org/interspeech_2024/stafylakis24_interspeech.pdf"
}Tools To Combat Voice DeepFakes, MV, Programu bezpečnostního výzkumu ČR 2021-2026: vývoj, testování a evaluace nových bezpečnostních technologií (SECTECH) - II. veřejná soutěž, VB02000060, start: 2024-01-01, end: 2026-12-31, running