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LANDINI, F. LOZANO DÍEZ, A. DIEZ SÁNCHEZ, M. BURGET, L.
Original Title
From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization
Type
conference paper
Language
English
Original Abstract
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.
Keywords
peaker diarization, end-to-end neural diariza- tion, simulated conversations
Authors
LANDINI, F.; LOZANO DÍEZ, A.; DIEZ SÁNCHEZ, M.; BURGET, L.
Released
18. 9. 2022
Publisher
International Speech Communication Association
Location
Incheon
ISBN
1990-9772
Periodical
Proceedings of Interspeech
Year of study
2022
Number
9
State
French Republic
Pages from
5095
Pages to
5099
Pages count
5
URL
https://www.isca-speech.org/archive/pdfs/interspeech_2022/landini22_interspeech.pdf
BibTex
@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" }