Publication Details
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization
Lozano Díez Alicia, Ph.D.
Diez Sánchez Mireia, M.Sc., Ph.D. (DCGM)
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
peaker diarization, end-to-end neural diariza- tion, simulated conversations
End-to-end neural diarization (EEND) is nowadays one of the most prominent
research topics in speaker diarization. EEND presents an attractive alternative
to standard cascaded diarization systems since a single system is trained at once
to deal with the whole diarization problem. Several EEND variants and approaches
are being proposed, however, all these models require large amounts of annotated
data for training but available annotated data are scarce. Thus, EEND works have
used mostly simulated mixtures for training. However, simulated mixtures do not
resemble real conversations in many aspects. In this work we present an
alternative method for creating synthetic conversations that resemble real ones
by using statistics about distributions of pauses and overlaps estimated on
genuine conversations. Furthermore, we analyze the effect of the source of the
statistics, different augmentations and amounts of data. We demonstrate that our
approach performs substantially better than the original one, while reducing the
dependence on the fine-tuning stage. Experiments are carried out on 2-speaker
telephone conversations of Callhome and DIHARD 3. Together with this publication,
we release our implementations of EEND and the method for creating simulated
conversations.
@inproceedings{BUT179780,
author="Federico Nicolás {Landini} and Alicia {Lozano Díez} and Mireia {Diez Sánchez} and Lukáš {Burget}",
title="From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization",
booktitle="Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",
year="2022",
journal="Proceedings of Interspeech",
volume="2022",
number="9",
pages="5095--5099",
publisher="International Speech Communication Association",
address="Incheon",
doi="10.21437/Interspeech.2022-10451",
issn="1990-9772",
url="https://www.isca-speech.org/archive/pdfs/interspeech_2022/landini22_interspeech.pdf"
}