DiaPer: End-to-End Neural Diarization With Perceiver-Based Attractors-Reference-Cited by-同舟云学术

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DiaPer: End-to-End Neural Diarization With Perceiver-Based Attractors

Published:2024 Issue: Volume:32 Page:3450-3465
ISSN:2329-9290
Container-title:IEEE/ACM Transactions on Audio, Speech, and Language Processing
language:
Short-container-title:IEEE/ACM Trans. Audio Speech Lang. Process.

Author:

Landini Federico¹^ORCID,Diez Mireia¹^ORCID,Stafylakis Themos²^ORCID,Burget Lukáš¹^ORCID

Affiliation:

1. Brno University of Technology, Brno, Czech Republic

2. Omilia, Marousi, Greece

Funder

Czech Ministry of Interior

Czech National Science Foundation

Horizon 2020 Marie Sklodowska-Curie

Ministerstvo Školství, Mládeže a Tělovýchovy

e-INFRA CZ

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6570655/10304349/10584294.pdf?arnumber=10584294

Reference90 articles.

1. Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge

2. But System for the Second Dihard Speech Diarization Challenge

3. Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized Maximum Eigengap

4. A review of speaker diarization: Recent advances with deep learning

5. End-to-End Neural Speaker Diarization with Permutation-Free Objectives

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