Speaker Diarization based on Deep Learning Techniques: A Review-Reference-Cited by-同舟云学术

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Speaker Diarization based on Deep Learning Techniques: A Review

Published:2022-10-20 Issue: Volume: Page:
ISSN:
Container-title:2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
language:
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Author:

Al-Hadithy Thaer M.¹,Frikha Mondher²,Maseer Zaidoon Kamil³

Affiliation:

1. ATISP research unit École Nationale d ‘Électronique et de Télecommunications de Sfax (ENET'Com) University,Sfax,Tunisia

2. Directeur de l'unité de recherche: Advanced Technologies for Image and Signal Processing (ATISP) University,Sfax,Tunisia

3. Technical University of Malaysia,Malacca faculty of Information and Communications Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9932444/9932651/09932710.pdf?arnumber=9932710

Reference138 articles.

1. Speaker Diarization with LSTM

2. End-to-end text-dependent speaker verification

3. On the use of spectral and iterative methods for speaker diarization

4. Exploiting intra-conversation variability for speaker diarization

5. Stream-based speaker segmentation using speaker factors and eigenvoices

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. SiamTDNN: Enhancing Discriminative Embeddings for Speaker Diarization;Journal of Circuits, Systems and Computers;2023-08-19

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