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
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | Contrastive separative coding for self-supervised representation learning |
| Event: | ICASSP - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Location: | Toronto, ON, Canada |
| Dates: | 6th-11th June 2021 |
| ISBN-13: | 978-1-7281-7605-5 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICASSP39728.2021.9414352 |
| Publisher version: | https://doi.org/10.1109/ICASSP39728.2021.9414352 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Speaker verification, speech separation, self attention, contrastive loss |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10154106 |
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