eprintid: 10154106
rev_number: 11
eprint_status: archive
userid: 699
dir: disk0/10/15/41/06
datestamp: 2022-08-23 10:56:53
lastmod: 2022-08-23 10:56:53
status_changed: 2022-08-23 10:56:53
type: proceedings_section
metadata_visibility: show
sword_depositor: 699
creators_name: Wang, J
creators_name: Lam, MWY
creators_name: Su, D
creators_name: Yu, D
title: Contrastive separative coding for self-supervised representation learning
ispublished: pub
divisions: C05
divisions: F48
divisions: B04
divisions: UCL
keywords: Speaker verification, speech separation, self attention, contrastive loss
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
abstract: To extract robust deep representations from long sequential modeling of speech data, we propose a self-supervised learning approach, namely Contrastive Separative Coding (CSC). Our key finding is to learn such representations by separating the target signal from contrastive interfering signals. First, a multi-task separative encoder is built to extract shared separable and discriminative embedding; secondly, we propose a powerful cross-attention mechanism performed over speaker representations across various interfering conditions, allowing the model to focus on and globally aggregate the most critical information to answer the "query"(current bottom-up embedding) while paying less attention to interfering, noisy, or irrelevant parts; lastly, we form a new probabilistic contrastive loss which estimates and maximizes the mutual information between the representations and the global speaker vector. While most prior unsupervised methods have focused on predicting the future, neighboring, or missing samples, we take a different perspective of predicting the interfered samples. Moreover, our contrastive separative loss is free from negative sampling. The experiment demonstrates that our approach can learn useful representations achieving a strong speaker verification performance in adverse conditions.
date: 2021
date_type: published
publisher: IEEE
official_url: https://doi.org/10.1109/ICASSP39728.2021.9414352
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1971130
doi: 10.1109/ICASSP39728.2021.9414352
isbn_13: 978-1-7281-7605-5
lyricists_name: Wang, Jun
lyricists_id: JWANG00
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
pres_type: paper
publication: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
pagerange: 3865-3869
event_title: ICASSP - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
event_location: Toronto, ON, Canada
event_dates: 6th-11th June 2021
book_title: Proceedings of the  ICASSP - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
citation:        Wang, J;    Lam, MWY;    Su, D;    Yu, D;      (2021)    Contrastive separative coding for self-supervised representation learning.                     In:  Proceedings of the ICASSP - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).  (pp. pp. 3865-3869).  IEEE       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10154106/1/2103.00816.pdf