Self-Conditioning via Intermediate Predictions for End-to-End Neural Speaker Diarization

Self-Conditioning via Intermediate Predictions for End-to-End Neural Speaker Diarization

This paper presents a speaker diarization model that incorporates label dependency via intermediate predictions. The proposed method is categorized as an end-to-end neural diarization (EEND), which has been a promising method for solving the speaker diarization problem with a multi-label classificat...

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Bibliographic Details
Main Authors: Yusuke Fujita, Tetsuji Ogawa, Tetsunori Kobayashi
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Encoder-decoder-based attractors
end-to-end neural diarization
intermediate objectives
non-autoregressive models
self-conditioning
speaker diarization
Online Access:https://ieeexplore.ieee.org/document/10347201/

Internet

https://ieeexplore.ieee.org/document/10347201/

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