×
Well done. You've clicked the tower. This would actually achieve something if you had logged in first. Use the key for that. The name takes you home. This is where all the applicables sit. And you can't apply any changes to my site unless you are logged in.

Our policy is best summarized as "we don't care about _you_, we care about _them_", no emails, so no forgetting your password. You have no rights. It's like you don't even exist. If you publish material, I reserve the right to remove it, or use it myself.

Don't impersonate. Don't name someone involuntarily. You can lose everything if you cross the line, and no, I won't cancel your automatic payments first, so you'll have to do it the hard way. See how serious this sounds? That's how serious you're meant to take these.

×
Register


Required. 150 characters or fewer. Letters, digits and @/./+/-/_ only.
  • Your password can’t be too similar to your other personal information.
  • Your password must contain at least 8 characters.
  • Your password can’t be a commonly used password.
  • Your password can’t be entirely numeric.
Enter the same password as before, for verification.
Login

Grow A Dic
Define A Word
Make Space
Set Task
Mark Post
Apply Votestyle
Create Votes
(From: saved spaces)
Exclude Votes
Apply Dic
Exclude Dic

Self-Distillation Network with Ensemble Prototypes: Learning Robust Speaker Representations without Supervision. (arXiv:2308.02774v2 [eess.AS] UPDATED)

Click here to flash read.

Training speaker-discriminative and robust speaker verification systems
without speaker labels is still challenging and worthwhile to explore. Previous
studies have noted a substantial performance disparity between self-supervised
and fully supervised approaches. In this paper, we propose an effective
Self-Distillation network with Ensemble Prototypes (SDEP) to facilitate
self-supervised speaker representation learning. A range of experiments
conducted on the VoxCeleb datasets demonstrate the superiority of the SDEP
framework in speaker verification. SDEP achieves a new SOTA on Voxceleb1
speaker verification evaluation benchmark ( i.e., equal error rate 1.94\%,
1.99\%, and 3.77\% for trial Vox1-O, Vox1-E and Vox1-H , respectively),
discarding any speaker labels in the training phase. Code will be publicly
available at https://github.com/alibaba-damo-academy/3D-Speaker.

Click here to read this post out
ID: 338342; Unique Viewers: 0
Unique Voters: 0
Total Votes: 0
Votes:
Latest Change: Aug. 22, 2023, 7:32 a.m. Changes:
/u/anonymous
Dictionaries:
Words:
Spaces:
Views: 8
CC:
No creative common's license
Comments: